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'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : List[str] , lowercase_ : Optional[int] , lowercase_ : Dict=7 , lowercase_ : Tuple=3 , lowercase_ : Tuple=30 , lowercase_ : Union[str, Any]=400 , lowercase_ : Optional[int]=True , lowercase_ : List[str]=None , lowercase_ : Optional[int]=True , lowercase_ : Tuple=[0.5, 0.5, 0.5] , lowercase_ : Any=[0.5, 0.5, 0.5] , lowercase_ : Any=True , lowercase_ : Union[str, Any]=1 / 255 , lowercase_ : List[Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase_ : List[str] = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
lowercase_ : Dict = parent
lowercase_ : Optional[Any] = batch_size
lowercase_ : Optional[int] = num_channels
lowercase_ : Dict = min_resolution
lowercase_ : int = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[int] = size
lowercase_ : Optional[int] = do_normalize
lowercase_ : Union[str, Any] = image_mean
lowercase_ : List[Any] = image_std
lowercase_ : str = do_rescale
lowercase_ : List[Any] = rescale_factor
lowercase_ : Optional[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self : str ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : int , lowercase_ : Dict=False ):
if not batched:
lowercase_ : Optional[int] = image_inputs[0]
if isinstance(lowercase_ , Image.Image ):
lowercase_ , lowercase_ : Optional[int] = image.size
else:
lowercase_ , lowercase_ : List[Any] = image.shape[1], image.shape[2]
if w < h:
lowercase_ : Union[str, Any] = int(self.size["""shortest_edge"""] * h / w )
lowercase_ : Any = self.size["""shortest_edge"""]
elif w > h:
lowercase_ : Optional[Any] = self.size["""shortest_edge"""]
lowercase_ : Tuple = int(self.size["""shortest_edge"""] * w / h )
else:
lowercase_ : Union[str, Any] = self.size["""shortest_edge"""]
lowercase_ : str = self.size["""shortest_edge"""]
else:
lowercase_ : int = []
for image in image_inputs:
lowercase_ , lowercase_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase_ : Any = max(lowercase_ , key=lambda lowercase_ : item[0] )[0]
lowercase_ : int = max(lowercase_ , key=lambda lowercase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = DeformableDetrImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = DeformableDetrImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """image_mean""" ) )
self.assertTrue(hasattr(lowercase_ , """image_std""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """do_rescale""" ) )
self.assertTrue(hasattr(lowercase_ , """do_pad""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333} )
self.assertEqual(image_processor.do_pad , lowercase_ )
lowercase_ : Dict = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowercase_ )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
# Initialize image_processing
lowercase_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , Image.Image )
# Test not batched input
lowercase_ : str = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : List[str] = self.image_processor_tester.get_expected_values(lowercase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ , lowercase_ : str = self.image_processor_tester.get_expected_values(lowercase_ , batched=lowercase_ )
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
# Initialize image_processing
lowercase_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , numpify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , np.ndarray )
# Test not batched input
lowercase_ : Tuple = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : Any = self.image_processor_tester.get_expected_values(lowercase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Dict = image_processing(lowercase_ , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : Optional[int] = self.image_processor_tester.get_expected_values(lowercase_ , batched=lowercase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , torchify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , torch.Tensor )
# Test not batched input
lowercase_ : List[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : Dict = self.image_processor_tester.get_expected_values(lowercase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Optional[int] = image_processing(lowercase_ , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : int = self.image_processor_tester.get_expected_values(lowercase_ , batched=lowercase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
# prepare image and target
lowercase_ : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
lowercase_ : Tuple = json.loads(f.read() )
lowercase_ : Union[str, Any] = {"""image_id""": 39769, """annotations""": target}
# encode them
lowercase_ : Tuple = DeformableDetrImageProcessor()
lowercase_ : Optional[Any] = image_processing(images=lowercase_ , annotations=lowercase_ , return_tensors="""pt""" )
# verify pixel values
lowercase_ : Dict = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , lowercase_ )
lowercase_ : str = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , lowercase_ , atol=1E-4 ) )
# verify area
lowercase_ : Optional[Any] = torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , lowercase_ ) )
# verify boxes
lowercase_ : str = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , lowercase_ )
lowercase_ : List[Any] = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , lowercase_ , atol=1E-3 ) )
# verify image_id
lowercase_ : Optional[int] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , lowercase_ ) )
# verify is_crowd
lowercase_ : Optional[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , lowercase_ ) )
# verify class_labels
lowercase_ : List[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , lowercase_ ) )
# verify orig_size
lowercase_ : List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , lowercase_ ) )
# verify size
lowercase_ : Optional[Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , lowercase_ ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
# prepare image, target and masks_path
lowercase_ : str = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
lowercase_ : Union[str, Any] = json.loads(f.read() )
lowercase_ : str = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}
lowercase_ : Union[str, Any] = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
lowercase_ : Optional[Any] = DeformableDetrImageProcessor(format="""coco_panoptic""" )
lowercase_ : int = image_processing(images=lowercase_ , annotations=lowercase_ , masks_path=lowercase_ , return_tensors="""pt""" )
# verify pixel values
lowercase_ : Optional[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , lowercase_ )
lowercase_ : List[str] = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , lowercase_ , atol=1E-4 ) )
# verify area
lowercase_ : List[Any] = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , lowercase_ ) )
# verify boxes
lowercase_ : Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , lowercase_ )
lowercase_ : Union[str, Any] = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , lowercase_ , atol=1E-3 ) )
# verify image_id
lowercase_ : Optional[Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , lowercase_ ) )
# verify is_crowd
lowercase_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , lowercase_ ) )
# verify class_labels
lowercase_ : Optional[int] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , lowercase_ ) )
# verify masks
lowercase_ : List[str] = 822873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , lowercase_ )
# verify orig_size
lowercase_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , lowercase_ ) )
# verify size
lowercase_ : List[str] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , lowercase_ ) )
| 21 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
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
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
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
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = 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))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# 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.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > 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}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# 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
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 1 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase ( UpperCAmelCase__ : Any ) -> str:
lowercase_ : List[Any] = filter(lambda UpperCAmelCase__ : p.requires_grad , model.parameters() )
lowercase_ : Optional[Any] = sum([np.prod(p.size() ) for p in model_parameters] )
return params
_lowercase : Union[str, Any] = logging.getLogger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ) -> Tuple:
if metric == "rouge2":
lowercase_ : List[str] = """{val_avg_rouge2:.4f}-{step_count}"""
elif metric == "bleu":
lowercase_ : List[str] = """{val_avg_bleu:.4f}-{step_count}"""
elif metric == "em":
lowercase_ : int = """{val_avg_em:.4f}-{step_count}"""
else:
raise NotImplementedError(
F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
""" function.""" )
lowercase_ : Dict = ModelCheckpoint(
dirpath=UpperCAmelCase__ , filename=UpperCAmelCase__ , monitor=F'''val_{metric}''' , mode="""max""" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def lowerCamelCase ( UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Union[str, Any] ) -> Optional[Any]:
return EarlyStopping(
monitor=F'''val_{metric}''' , mode="""min""" if """loss""" in metric else """max""" , patience=UpperCAmelCase__ , verbose=UpperCAmelCase__ , )
class __magic_name__ ( pl.Callback):
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Any , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = {f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(lowercase_ )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : pl.Trainer , lowercase_ : pl.LightningModule , lowercase_ : str , lowercase_ : Any=True ):
logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
lowercase_ : Union[str, Any] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} )
# Log results
lowercase_ : Optional[int] = Path(pl_module.hparams.output_dir )
if type_path == "test":
lowercase_ : Optional[Any] = od / """test_results.txt"""
lowercase_ : str = od / """test_generations.txt"""
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
lowercase_ : Any = od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
lowercase_ : Tuple = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=lowercase_ )
generations_file.parent.mkdir(exist_ok=lowercase_ )
with open(lowercase_ , """a+""" ) as writer:
for key in sorted(lowercase_ ):
if key in ["log", "progress_bar", "preds"]:
continue
lowercase_ : List[str] = metrics[key]
if isinstance(lowercase_ , torch.Tensor ):
lowercase_ : List[str] = val.item()
lowercase_ : int = f'''{key}: {val:.6f}\n'''
writer.write(lowercase_ )
if not save_generations:
return
if "preds" in metrics:
lowercase_ : int = """\n""".join(metrics["""preds"""] )
generations_file.open("""w+""" ).write(lowercase_ )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Any , lowercase_ : Optional[Any] ):
try:
lowercase_ : List[Any] = pl_module.model.model.num_parameters()
except AttributeError:
lowercase_ : Union[str, Any] = pl_module.model.num_parameters()
lowercase_ : Union[str, Any] = count_trainable_parameters(lowercase_ )
# mp stands for million parameters
trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6} )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : pl.Trainer , lowercase_ : pl.LightningModule ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(lowercase_ , lowercase_ , """test""" )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : pl.Trainer , lowercase_ : Optional[int] ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 21 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[str] = """laion/clap-htsat-unfused"""
lowercase_ : Dict = tempfile.mkdtemp()
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , **lowercase_ : Any ):
return RobertaTokenizer.from_pretrained(self.checkpoint , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , **lowercase_ : str ):
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Union[str, Any] = self.get_tokenizer()
lowercase_ : Optional[Any] = self.get_feature_extractor()
lowercase_ : str = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
processor.save_pretrained(self.tmpdirname )
lowercase_ : Tuple = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
lowercase_ : Tuple = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase_ : Optional[int] = self.get_feature_extractor(do_normalize=lowercase_ , padding_value=1.0 )
lowercase_ : Optional[int] = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowercase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Dict = self.get_feature_extractor()
lowercase_ : List[str] = self.get_tokenizer()
lowercase_ : List[Any] = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
lowercase_ : Optional[int] = floats_list((3, 1000) )
lowercase_ : int = feature_extractor(lowercase_ , return_tensors="""np""" )
lowercase_ : List[Any] = processor(audios=lowercase_ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[str] = self.get_feature_extractor()
lowercase_ : Optional[int] = self.get_tokenizer()
lowercase_ : List[Any] = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
lowercase_ : Optional[Any] = """This is a test string"""
lowercase_ : str = processor(text=lowercase_ )
lowercase_ : int = tokenizer(lowercase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.get_feature_extractor()
lowercase_ : Dict = self.get_tokenizer()
lowercase_ : Optional[Any] = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
lowercase_ : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase_ : List[str] = processor.batch_decode(lowercase_ )
lowercase_ : Tuple = tokenizer.batch_decode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = self.get_feature_extractor()
lowercase_ : int = self.get_tokenizer()
lowercase_ : Optional[Any] = ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 21 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Dict = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[int] = [
"BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BioGptForCausalLM",
"BioGptForTokenClassification",
"BioGptForSequenceClassification",
"BioGptModel",
"BioGptPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
_lowercase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance < 0:
raise ValueError("""Resistance cannot be negative""" )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | '''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : list ) -> float:
lowercase_ : Tuple = 0
while len(UpperCAmelCase__ ) > 1:
lowercase_ : Optional[Any] = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
lowercase_ : Optional[Any] = files.index(min(UpperCAmelCase__ ) )
temp += files[min_index]
files.pop(UpperCAmelCase__ )
files.append(UpperCAmelCase__ )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 1 |
'''simple docstring'''
import math
def lowerCamelCase ( UpperCAmelCase__ : int ) -> bool:
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(UpperCAmelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase ( UpperCAmelCase__ : int = 10001 ) -> int:
try:
lowercase_ : Any = int(UpperCAmelCase__ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
lowercase_ : list[int] = []
lowercase_ : List[str] = 2
while len(UpperCAmelCase__ ) < nth:
if is_prime(UpperCAmelCase__ ):
primes.append(UpperCAmelCase__ )
num += 1
else:
num += 1
return primes[len(UpperCAmelCase__ ) - 1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | 1 |
'''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 __magic_name__ ( unittest.TestCase):
def __init__( self : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : Tuple=7 , lowercase_ : List[Any]=3 , lowercase_ : str=18 , lowercase_ : Optional[Any]=30 , lowercase_ : Dict=400 , lowercase_ : int=True , lowercase_ : List[Any]=32 , lowercase_ : List[str]=True , ):
lowercase_ : List[str] = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Tuple = num_channels
lowercase_ : Union[str, Any] = image_size
lowercase_ : Any = min_resolution
lowercase_ : Union[str, Any] = max_resolution
lowercase_ : List[str] = do_resize
lowercase_ : List[str] = size_divisor
lowercase_ : List[Any] = do_rescale
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = GLPNImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Optional[Any] = GLPNImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size_divisor""" ) )
self.assertTrue(hasattr(lowercase_ , """resample""" ) )
self.assertTrue(hasattr(lowercase_ , """do_rescale""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
# Initialize image_processing
lowercase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowercase_ : str = 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 SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
# Initialize image_processing
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , numpify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowercase_ : str = 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 SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , torchify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowercase_ : Optional[int] = 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 )
| 21 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 1 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowercase : Optional[Any] = 16
_lowercase : Any = 32
def lowerCamelCase ( UpperCAmelCase__ : Accelerator , UpperCAmelCase__ : int = 16 ) -> Optional[Any]:
lowercase_ : Optional[int] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase_ : Any = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCAmelCase__ : str ):
# max_length=None => use the model max length (it's actually the default)
lowercase_ : List[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCAmelCase__ , max_length=UpperCAmelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase_ : List[str] = datasets.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase_ : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCAmelCase__ : List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase_ : List[str] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase_ : List[Any] = 16
elif accelerator.mixed_precision != "no":
lowercase_ : List[str] = 8
else:
lowercase_ : Optional[Any] = None
return tokenizer.pad(
UpperCAmelCase__ , padding="""longest""" , max_length=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase_ : Tuple = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , drop_last=UpperCAmelCase__ )
lowercase_ : str = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , drop_last=(accelerator.mixed_precision == """fp8""") , )
return train_dataloader, eval_dataloader
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict ) -> str:
# Initialize accelerator
lowercase_ : Optional[int] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase_ : Tuple = config["""lr"""]
lowercase_ : Optional[Any] = int(config["""num_epochs"""] )
lowercase_ : List[Any] = int(config["""seed"""] )
lowercase_ : int = int(config["""batch_size"""] )
lowercase_ : int = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
lowercase_ : List[str] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowercase_ : int = batch_size // MAX_GPU_BATCH_SIZE
lowercase_ : Optional[int] = MAX_GPU_BATCH_SIZE
set_seed(UpperCAmelCase__ )
lowercase_ , lowercase_ : str = get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCAmelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
lowercase_ : Union[str, Any] = AdamW(params=model.parameters() , lr=UpperCAmelCase__ )
# Instantiate scheduler
lowercase_ : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCAmelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = accelerator.prepare(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Now we train the model
for epoch in range(UpperCAmelCase__ ):
model.train()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase_ : Union[str, Any] = model(**UpperCAmelCase__ )
lowercase_ : int = outputs.loss
lowercase_ : Any = loss / gradient_accumulation_steps
accelerator.backward(UpperCAmelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase_ : str = model(**UpperCAmelCase__ )
lowercase_ : Union[str, Any] = outputs.logits.argmax(dim=-1 )
lowercase_ , lowercase_ : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=UpperCAmelCase__ , references=UpperCAmelCase__ , )
lowercase_ : Dict = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , UpperCAmelCase__ )
def lowerCamelCase ( ) -> Tuple:
lowercase_ : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowercase_ : int = parser.parse_args()
lowercase_ : Optional[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
import torch
def lowerCamelCase ( ) -> List[str]:
if torch.cuda.is_available():
lowercase_ : Any = torch.cuda.device_count()
else:
lowercase_ : Any = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase : List[str] = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"configuration_data2vec_text": [
"DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Data2VecTextConfig",
"Data2VecTextOnnxConfig",
],
"configuration_data2vec_vision": [
"DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Data2VecVisionConfig",
"Data2VecVisionOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Any = [
"DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
"Data2VecAudioForAudioFrameClassification",
"Data2VecAudioForCTC",
"Data2VecAudioForSequenceClassification",
"Data2VecAudioForXVector",
"Data2VecAudioModel",
"Data2VecAudioPreTrainedModel",
]
_lowercase : Any = [
"DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Data2VecTextForCausalLM",
"Data2VecTextForMaskedLM",
"Data2VecTextForMultipleChoice",
"Data2VecTextForQuestionAnswering",
"Data2VecTextForSequenceClassification",
"Data2VecTextForTokenClassification",
"Data2VecTextModel",
"Data2VecTextPreTrainedModel",
]
_lowercase : Any = [
"DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
"Data2VecVisionForImageClassification",
"Data2VecVisionForMaskedImageModeling",
"Data2VecVisionForSemanticSegmentation",
"Data2VecVisionModel",
"Data2VecVisionPreTrainedModel",
]
if is_tf_available():
_lowercase : Dict = [
"TFData2VecVisionForImageClassification",
"TFData2VecVisionForSemanticSegmentation",
"TFData2VecVisionModel",
"TFData2VecVisionPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
_lowercase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | '''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | 1 |
'''simple docstring'''
import functools
def lowerCamelCase ( UpperCAmelCase__ : list[int] , UpperCAmelCase__ : list[int] ) -> int:
# Validation
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or not all(isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(UpperCAmelCase__ ) != 3 or not all(isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for cost in costs ):
raise ValueError("""The parameter costs should be a list of three integers""" )
if len(UpperCAmelCase__ ) == 0:
return 0
if min(UpperCAmelCase__ ) <= 0:
raise ValueError("""All days elements should be greater than 0""" )
if max(UpperCAmelCase__ ) >= 366:
raise ValueError("""All days elements should be less than 366""" )
lowercase_ : Optional[int] = set(UpperCAmelCase__ )
@functools.cache
def dynamic_programming(UpperCAmelCase__ : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | '''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : List[Any] ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None ):
lowercase_ : Optional[Any] = {}
lowercase_ : Tuple = {}
if prompt is not None:
lowercase_ : Tuple = prompt
if generate_kwargs is not None:
lowercase_ : List[str] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase_ : List[Any] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase_ : str = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : Optional[int] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : Tuple=None ):
lowercase_ : List[Any] = load_image(lowercase_ )
if prompt is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowercase_ )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase_ : List[Any] = self.model.config.model_type
if model_type == "git":
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : Union[str, Any] = self.tokenizer(text=lowercase_ , add_special_tokens=lowercase_ ).input_ids
lowercase_ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase_ : List[Any] = torch.tensor(lowercase_ ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase_ : Union[str, Any] = self.image_processor(images=lowercase_ , header_text=lowercase_ , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : List[str] = self.tokenizer(lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase_ : str = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict , lowercase_ : Optional[Any]=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , lowercase_ )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase_ : Any = None
if generate_kwargs is None:
lowercase_ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase_ : Dict = model_inputs.pop(self.model.main_input_name )
lowercase_ : Any = self.model.generate(lowercase_ , **lowercase_ , **lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] ):
lowercase_ : List[str] = []
for output_ids in model_outputs:
lowercase_ : Union[str, Any] = {
"""generated_text""": self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , )
}
records.append(lowercase_ )
return records
| 21 | 1 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , *lowercase_ : Optional[Any] , lowercase_ : Optional[Any]=None , lowercase_ : Any=None , **lowercase_ : int ):
super().__init__(*lowercase_ , **lowercase_ )
lowercase_ : Dict = eval_examples
lowercase_ : List[Any] = post_process_function
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any]=None , lowercase_ : int=None , lowercase_ : int=None , lowercase_ : str = "eval" ):
lowercase_ : Optional[Any] = self.eval_dataset if eval_dataset is None else eval_dataset
lowercase_ : str = self.get_eval_dataloader(lowercase_ )
lowercase_ : Tuple = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowercase_ : Optional[Any] = self.compute_metrics
lowercase_ : List[Any] = None
lowercase_ : Optional[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
lowercase_ : Dict = time.time()
try:
lowercase_ : Any = eval_loop(
lowercase_ , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowercase_ , metric_key_prefix=lowercase_ , )
finally:
lowercase_ : int = compute_metrics
lowercase_ : List[Any] = self.args.eval_batch_size * self.args.world_size
if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
lowercase_ , lowercase_ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
lowercase_ : Optional[int] = self.post_process_function(lowercase_ , lowercase_ , output.predictions )
lowercase_ : Union[str, Any] = self.compute_metrics(lowercase_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'''{metric_key_prefix}_''' ):
lowercase_ : str = metrics.pop(lowercase_ )
metrics.update(output.metrics )
else:
lowercase_ : List[str] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(lowercase_ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
lowercase_ : Optional[int] = self.callback_handler.on_evaluate(self.args , self.state , self.control , lowercase_ )
return metrics
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Dict=None , lowercase_ : str = "test" ):
lowercase_ : Tuple = self.get_test_dataloader(lowercase_ )
# Temporarily disable metric computation, we will do it in the loop here.
lowercase_ : str = self.compute_metrics
lowercase_ : Union[str, Any] = None
lowercase_ : Union[str, Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
lowercase_ : Any = time.time()
try:
lowercase_ : Optional[int] = eval_loop(
lowercase_ , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowercase_ , metric_key_prefix=lowercase_ , )
finally:
lowercase_ : Optional[int] = compute_metrics
lowercase_ : int = self.args.eval_batch_size * self.args.world_size
if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
lowercase_ , lowercase_ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
lowercase_ : Any = self.post_process_function(lowercase_ , lowercase_ , output.predictions , """predict""" )
lowercase_ : Dict = self.compute_metrics(lowercase_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'''{metric_key_prefix}_''' ):
lowercase_ : int = metrics.pop(lowercase_ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowercase_ )
| 21 | '''simple docstring'''
class __magic_name__ :
def __init__( self : int , lowercase_ : list ):
lowercase_ : Dict = set_counts
lowercase_ : List[Any] = max(lowercase_ )
lowercase_ : str = len(lowercase_ )
lowercase_ : str = [1] * num_sets
lowercase_ : Dict = list(range(lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : int ):
lowercase_ : List[Any] = self.get_parent(lowercase_ )
lowercase_ : Union[str, Any] = self.get_parent(lowercase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase_ : List[str] = 0
lowercase_ : Optional[int] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase_ : int = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase_ : int = 0
lowercase_ : List[Any] = src_parent
lowercase_ : List[Any] = self.set_counts[src_parent]
lowercase_ : Tuple = max(self.max_set , lowercase_ )
return True
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
lowercase_ : int = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 21 | 1 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys())})
UpperCamelCase__ = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''})
UpperCamelCase__ = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Dict = self.task_name.lower()
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''train'''
UpperCamelCase__ = '''dev'''
UpperCamelCase__ = '''test'''
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
def __init__( self : str , lowercase_ : GlueDataTrainingArguments , lowercase_ : PreTrainedTokenizerBase , lowercase_ : Optional[int] = None , lowercase_ : Union[str, Split] = Split.train , lowercase_ : Optional[str] = None , ):
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , lowercase_ , )
lowercase_ : Union[str, Any] = args
lowercase_ : List[Any] = glue_processors[args.task_name]()
lowercase_ : Optional[Any] = glue_output_modes[args.task_name]
if isinstance(lowercase_ , lowercase_ ):
try:
lowercase_ : List[Any] = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
lowercase_ : str = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}''' , )
lowercase_ : Tuple = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowercase_ , lowercase_ : List[str] = label_list[2], label_list[1]
lowercase_ : Union[str, Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase_ : List[Any] = cached_features_file + """.lock"""
with FileLock(lowercase_ ):
if os.path.exists(lowercase_ ) and not args.overwrite_cache:
lowercase_ : Optional[Any] = time.time()
lowercase_ : Optional[int] = torch.load(lowercase_ )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
else:
logger.info(f'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
lowercase_ : Union[str, Any] = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
lowercase_ : Tuple = self.processor.get_test_examples(args.data_dir )
else:
lowercase_ : Optional[Any] = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
lowercase_ : Dict = examples[:limit_length]
lowercase_ : List[Any] = glue_convert_examples_to_features(
lowercase_ , lowercase_ , max_length=args.max_seq_length , label_list=lowercase_ , output_mode=self.output_mode , )
lowercase_ : int = time.time()
torch.save(self.features , lowercase_ )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self : List[Any] ):
return len(self.features )
def __getitem__( self : int , lowercase_ : Optional[Any] ):
return self.features[i]
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.label_list
| 21 | '''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : List[str] = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''xlm-roberta-xl'''
def __init__( self : Optional[int] , lowercase_ : str=250880 , lowercase_ : Optional[int]=2560 , lowercase_ : Optional[Any]=36 , lowercase_ : Dict=32 , lowercase_ : int=10240 , lowercase_ : Optional[int]="gelu" , lowercase_ : Dict=0.1 , lowercase_ : Any=0.1 , lowercase_ : List[Any]=514 , lowercase_ : Any=1 , lowercase_ : Any=0.02 , lowercase_ : Tuple=1E-05 , lowercase_ : str=1 , lowercase_ : List[str]=0 , lowercase_ : Optional[int]=2 , lowercase_ : Optional[Any]="absolute" , lowercase_ : int=True , lowercase_ : List[str]=None , **lowercase_ : Any , ):
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
lowercase_ : Optional[Any] = vocab_size
lowercase_ : int = hidden_size
lowercase_ : str = num_hidden_layers
lowercase_ : int = num_attention_heads
lowercase_ : int = hidden_act
lowercase_ : List[str] = intermediate_size
lowercase_ : int = hidden_dropout_prob
lowercase_ : Tuple = attention_probs_dropout_prob
lowercase_ : List[Any] = max_position_embeddings
lowercase_ : str = type_vocab_size
lowercase_ : int = initializer_range
lowercase_ : Any = layer_norm_eps
lowercase_ : Optional[Any] = position_embedding_type
lowercase_ : Optional[Any] = use_cache
lowercase_ : int = classifier_dropout
class __magic_name__ ( _UpperCAmelCase):
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
if self.task == "multiple-choice":
lowercase_ : int = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ : List[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 21 | '''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 lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , 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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = 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 __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = 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 __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# 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]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | 1 |
'''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=1337 , num_examples=42 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1337 , num_examples=42 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def lowerCamelCase ( UpperCAmelCase__ : SplitDict ) -> Any:
lowercase_ : str = split_dict._to_yaml_list()
assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = SplitDict._from_yaml_list(UpperCAmelCase__ )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
lowercase_ : List[Any] = None
# the split name of split_dict takes over the name of the split info object
lowercase_ : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=UpperCAmelCase__ ), SplitInfo(dataset_name="""my_dataset""" )] )
def lowerCamelCase ( UpperCAmelCase__ : str ) -> List[Any]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
lowercase_ : Optional[int] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 21 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int=7 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=18 , lowercase_ : List[Any]=30 , lowercase_ : int=400 , lowercase_ : Dict=True , lowercase_ : List[Any]=None , lowercase_ : Dict=True , ):
lowercase_ : Tuple = size if size is not None else {"""height""": 18, """width""": 18}
lowercase_ : List[str] = parent
lowercase_ : Any = batch_size
lowercase_ : Optional[Any] = num_channels
lowercase_ : Tuple = image_size
lowercase_ : Optional[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[Any] = size
lowercase_ : Union[str, Any] = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """clusters""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Union[str, Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , obj[key] ) )
else:
self.assertEqual(obj[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , """image_processor.json""" )
image_processor_first.to_json_file(lowercase_ )
lowercase_ : Optional[Any] = self.image_processing_class.from_json_file(lowercase_ ).to_dict()
lowercase_ : Any = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowercase_ )
lowercase_ : Any = self.image_processing_class.from_pretrained(lowercase_ ).to_dict()
lowercase_ : List[str] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
pass
def lowerCamelCase ( ) -> Any:
lowercase_ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
lowercase_ : Any = Image.open(dataset[4]["""file"""] )
lowercase_ : Dict = Image.open(dataset[5]["""file"""] )
lowercase_ : int = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
lowercase_ : Optional[int] = prepare_images()
# test non-batched
lowercase_ : str = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
lowercase_ : Tuple = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowercase_ )
# test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
lowercase_ : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowercase_ )
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int ) -> bool:
lowercase_ : str = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowerCamelCase ( UpperCAmelCase__ : int = 5000 ) -> int:
lowercase_ : Optional[Any] = [(i * (3 * i - 1)) // 2 for i in range(1 , UpperCAmelCase__ )]
for i, pentagonal_i in enumerate(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ , len(UpperCAmelCase__ ) ):
lowercase_ : List[str] = pentagonal_nums[j]
lowercase_ : Union[str, Any] = pentagonal_i + pentagonal_j
lowercase_ : Any = pentagonal_j - pentagonal_i
if is_pentagonal(UpperCAmelCase__ ) and is_pentagonal(UpperCAmelCase__ ):
return b
return -1
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : int = {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
"allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
),
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''longformer'''
def __init__( self : Dict , lowercase_ : Union[List[int], int] = 512 , lowercase_ : int = 2 , lowercase_ : int = 1 , lowercase_ : int = 0 , lowercase_ : int = 2 , lowercase_ : int = 30522 , lowercase_ : int = 768 , lowercase_ : int = 12 , lowercase_ : int = 12 , lowercase_ : int = 3072 , lowercase_ : str = "gelu" , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : int = 512 , lowercase_ : int = 2 , lowercase_ : float = 0.02 , lowercase_ : float = 1E-12 , lowercase_ : bool = False , **lowercase_ : List[str] , ):
super().__init__(pad_token_id=lowercase_ , **lowercase_ )
lowercase_ : List[Any] = attention_window
lowercase_ : Dict = sep_token_id
lowercase_ : str = bos_token_id
lowercase_ : str = eos_token_id
lowercase_ : Dict = vocab_size
lowercase_ : Optional[int] = hidden_size
lowercase_ : List[Any] = num_hidden_layers
lowercase_ : Dict = num_attention_heads
lowercase_ : Any = hidden_act
lowercase_ : int = intermediate_size
lowercase_ : Optional[int] = hidden_dropout_prob
lowercase_ : str = attention_probs_dropout_prob
lowercase_ : Optional[int] = max_position_embeddings
lowercase_ : Dict = type_vocab_size
lowercase_ : Tuple = initializer_range
lowercase_ : Tuple = layer_norm_eps
lowercase_ : Tuple = onnx_export
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : "PretrainedConfig" , lowercase_ : str = "default" , lowercase_ : "List[PatchingSpec]" = None ):
super().__init__(lowercase_ , lowercase_ , lowercase_ )
lowercase_ : Dict = True
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
if self.task == "multiple-choice":
lowercase_ : int = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ : List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""global_attention_mask""", dynamic_axis),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : int = super().outputs
if self.task == "default":
lowercase_ : Optional[int] = {0: """batch"""}
return outputs
@property
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return 1E-4
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : "PreTrainedTokenizerBase" , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
lowercase_ : str = super().generate_dummy_inputs(
preprocessor=lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
lowercase_ : int = torch.zeros_like(inputs["""input_ids"""] )
# make every second token global
lowercase_ : List[Any] = 1
return inputs
| 21 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 21 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase ( UpperCAmelCase__ : int ) -> bool:
lowercase_ : int = int(number**0.5 )
return number == sq * sq
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> tuple[int, int]:
lowercase_ : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
lowercase_ : int = x_den * y_den * z_den
lowercase_ : int = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
top //= hcf
bottom //= hcf
return top, bottom
def lowerCamelCase ( UpperCAmelCase__ : int = 35 ) -> int:
lowercase_ : set = set()
lowercase_ : int
lowercase_ : Fraction = Fraction(0 )
lowercase_ : tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
lowercase_ : Union[str, Any] = x_num * y_den + x_den * y_num
lowercase_ : int = x_den * y_den
lowercase_ : List[Any] = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase_ : Optional[Any] = add_three(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
unique_s.add(UpperCAmelCase__ )
# n=2
lowercase_ : Any = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
lowercase_ : Union[str, Any] = x_den * x_den * y_den * y_den
if is_sq(UpperCAmelCase__ ) and is_sq(UpperCAmelCase__ ):
lowercase_ : Optional[Any] = int(sqrt(UpperCAmelCase__ ) )
lowercase_ : Dict = int(sqrt(UpperCAmelCase__ ) )
lowercase_ : Any = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase_ : Any = add_three(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
unique_s.add(UpperCAmelCase__ )
# n=-1
lowercase_ : List[str] = x_num * y_num
lowercase_ : Optional[Any] = x_den * y_num + x_num * y_den
lowercase_ : Dict = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase_ : List[str] = add_three(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
unique_s.add(UpperCAmelCase__ )
# n=2
lowercase_ : Optional[Any] = x_num * x_num * y_num * y_num
lowercase_ : Optional[Any] = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(UpperCAmelCase__ ) and is_sq(UpperCAmelCase__ ):
lowercase_ : List[str] = int(sqrt(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = int(sqrt(UpperCAmelCase__ ) )
lowercase_ : Dict = gcd(UpperCAmelCase__ , UpperCAmelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase_ : Union[str, Any] = add_three(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
unique_s.add(UpperCAmelCase__ )
for num, den in unique_s:
total += Fraction(UpperCAmelCase__ , UpperCAmelCase__ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | '''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
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(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 21 | 1 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Optional[Any] , lowercase_ : Dict , lowercase_ : Union[str, Any]=13 , lowercase_ : List[str]=7 , lowercase_ : Any=True , lowercase_ : Union[str, Any]=True , lowercase_ : Optional[int]=False , lowercase_ : List[str]=True , lowercase_ : int=99 , lowercase_ : Any=32 , lowercase_ : List[str]=5 , lowercase_ : List[str]=4 , lowercase_ : Optional[int]=64 , lowercase_ : Tuple="gelu" , lowercase_ : Dict=0.1 , lowercase_ : int=0.1 , lowercase_ : Union[str, Any]=512 , lowercase_ : List[Any]=16 , lowercase_ : List[Any]=2 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : Any=3 , lowercase_ : List[Any]=4 , lowercase_ : List[str]=None , lowercase_ : str=2 , lowercase_ : Tuple=2 , lowercase_ : Optional[int]=2 , lowercase_ : Tuple=2 , lowercase_ : Optional[Any]=4 , lowercase_ : str=1 , ):
lowercase_ : Union[str, Any] = parent
lowercase_ : Optional[Any] = batch_size
lowercase_ : List[str] = seq_length
lowercase_ : Dict = is_training
lowercase_ : Union[str, Any] = use_input_mask
lowercase_ : Tuple = use_token_type_ids
lowercase_ : Optional[int] = use_labels
lowercase_ : List[Any] = vocab_size
lowercase_ : Dict = hidden_size
lowercase_ : Optional[int] = num_hidden_layers
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : List[Any] = hidden_act
lowercase_ : int = hidden_dropout_prob
lowercase_ : Any = attention_probs_dropout_prob
lowercase_ : Optional[int] = max_position_embeddings
lowercase_ : str = type_vocab_size
lowercase_ : Optional[Any] = type_sequence_label_size
lowercase_ : Optional[int] = initializer_range
lowercase_ : Optional[int] = num_labels
lowercase_ : int = num_choices
lowercase_ : List[str] = scope
lowercase_ : Optional[Any] = q_groups
lowercase_ : Union[str, Any] = k_groups
lowercase_ : Any = v_groups
lowercase_ : Optional[int] = post_attention_groups
lowercase_ : Union[str, Any] = intermediate_groups
lowercase_ : Optional[Any] = output_groups
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : str = None
if self.use_input_mask:
lowercase_ : int = random_attention_mask([self.batch_size, self.seq_length] )
lowercase_ : Any = None
lowercase_ : Optional[int] = None
lowercase_ : Tuple = None
if self.use_labels:
lowercase_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : Dict = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : Union[str, Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , 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 , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : int = SqueezeBertModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = model(lowercase_ , lowercase_ )
lowercase_ : List[Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Optional[int] ):
lowercase_ : Optional[Any] = SqueezeBertForMaskedLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Tuple = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Dict , lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Dict = SqueezeBertForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Union[str, Any] = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : int ):
lowercase_ : Optional[Any] = self.num_labels
lowercase_ : Optional[Any] = SqueezeBertForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : List[str] = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : Union[str, Any] ):
lowercase_ : List[Any] = self.num_labels
lowercase_ : int = SqueezeBertForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Dict = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : int ):
lowercase_ : Any = self.num_choices
lowercase_ : Any = SqueezeBertForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase_ : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase_ : Optional[Any] = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[str] = self.prepare_config_and_inputs()
((lowercase_) , (lowercase_) , (lowercase_) , (lowercase_) , (lowercase_) , (lowercase_)) : Union[str, Any] = config_and_inputs
lowercase_ : Any = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
UpperCamelCase__ = (
{
'''feature-extraction''': SqueezeBertModel,
'''fill-mask''': SqueezeBertForMaskedLM,
'''question-answering''': SqueezeBertForQuestionAnswering,
'''text-classification''': SqueezeBertForSequenceClassification,
'''token-classification''': SqueezeBertForTokenClassification,
'''zero-shot''': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = SqueezeBertModelTester(self )
lowercase_ : Union[str, Any] = ConfigTester(self , config_class=lowercase_ , dim=37 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : int = SqueezeBertModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : List[str] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
lowercase_ : str = torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
lowercase_ : List[Any] = model(lowercase_ )[0]
lowercase_ : Tuple = torch.Size((1, 3) )
self.assertEqual(output.shape , lowercase_ )
lowercase_ : Dict = torch.tensor([[0.64_01, -0.03_49, -0.60_41]] )
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1E-4 ) )
| 21 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_lowercase : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
_lowercase : Tuple = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __magic_name__ :
UpperCamelCase__ = 42
UpperCamelCase__ = 42
class __magic_name__ :
def __init__( self : Union[str, Any] , lowercase_ : Iterable[int] ):
lowercase_ : Node | None = None
for i in sorted(lowercase_ , reverse=lowercase_ ):
lowercase_ : Optional[int] = Node(lowercase_ , self.head )
def __iter__( self : Optional[int] ):
lowercase_ : Tuple = self.head
while node:
yield node.data
lowercase_ : str = node.next_node
def __len__( self : str ):
return sum(1 for _ in self )
def __str__( self : List[str] ):
return " -> ".join([str(lowercase_ ) for node in self] )
def lowerCamelCase ( UpperCAmelCase__ : SortedLinkedList , UpperCAmelCase__ : SortedLinkedList ) -> SortedLinkedList:
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : List[str] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 21 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | 1 |
'''simple docstring'''
from manim import *
class __magic_name__ ( _UpperCAmelCase):
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : List[Any] = Rectangle(height=0.5 , width=0.5 )
lowercase_ : Optional[Any] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowercase_ : Dict = [mem.copy() for i in range(6 )]
lowercase_ : Dict = [mem.copy() for i in range(6 )]
lowercase_ : Union[str, Any] = VGroup(*lowercase_ ).arrange(lowercase_ , buff=0 )
lowercase_ : int = VGroup(*lowercase_ ).arrange(lowercase_ , buff=0 )
lowercase_ : Optional[int] = VGroup(lowercase_ , lowercase_ ).arrange(lowercase_ , buff=0 )
lowercase_ : str = Text("""CPU""" , font_size=24 )
lowercase_ : int = Group(lowercase_ , lowercase_ ).arrange(lowercase_ , buff=0.5 , aligned_edge=lowercase_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowercase_ )
lowercase_ : Union[str, Any] = [mem.copy() for i in range(4 )]
lowercase_ : Tuple = VGroup(*lowercase_ ).arrange(lowercase_ , buff=0 )
lowercase_ : List[Any] = Text("""GPU""" , font_size=24 )
lowercase_ : int = Group(lowercase_ , lowercase_ ).arrange(lowercase_ , buff=0.5 , aligned_edge=lowercase_ )
gpu.move_to([-1, -1, 0] )
self.add(lowercase_ )
lowercase_ : Tuple = [mem.copy() for i in range(6 )]
lowercase_ : List[Any] = VGroup(*lowercase_ ).arrange(lowercase_ , buff=0 )
lowercase_ : Any = Text("""Model""" , font_size=24 )
lowercase_ : int = Group(lowercase_ , lowercase_ ).arrange(lowercase_ , buff=0.5 , aligned_edge=lowercase_ )
model.move_to([3, -1.0, 0] )
self.add(lowercase_ )
lowercase_ : Tuple = []
for i, rect in enumerate(lowercase_ ):
rect.set_stroke(lowercase_ )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
lowercase_ : Optional[Any] = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(lowercase_ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=lowercase_ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=lowercase_ , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=lowercase_ , buff=0.0 )
self.add(lowercase_ )
cpu_targs.append(lowercase_ )
lowercase_ : Tuple = [mem.copy() for i in range(6 )]
lowercase_ : Optional[Any] = VGroup(*lowercase_ ).arrange(lowercase_ , buff=0 )
lowercase_ : int = Text("""Loaded Checkpoint""" , font_size=24 )
lowercase_ : str = Group(lowercase_ , lowercase_ ).arrange(lowercase_ , aligned_edge=lowercase_ , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
lowercase_ : str = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowercase_ : Optional[int] = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowercase_ , lowercase_ )
lowercase_ : List[str] = MarkupText(
f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(lowercase_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
lowercase_ : str = MarkupText(
f'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowercase_ ) , Write(lowercase_ ) )
self.play(Write(lowercase_ , run_time=1 ) , Create(lowercase_ , run_time=1 ) )
lowercase_ : Optional[Any] = []
lowercase_ : Any = []
for i, rect in enumerate(lowercase_ ):
lowercase_ : Tuple = fill.copy().set_fill(lowercase_ , opacity=0.7 )
target.move_to(lowercase_ )
first_animations.append(GrowFromCenter(lowercase_ , run_time=1 ) )
lowercase_ : Optional[int] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(lowercase_ , run_time=1.5 ) )
self.play(*lowercase_ )
self.play(*lowercase_ )
self.wait()
| 21 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : Dict = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
),
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''xlm-prophetnet'''
UpperCamelCase__ = ['''past_key_values''']
UpperCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self : List[Any] , lowercase_ : Optional[float] = 0.1 , lowercase_ : Optional[Union[str, Callable]] = "gelu" , lowercase_ : Optional[int] = 30522 , lowercase_ : Optional[int] = 1024 , lowercase_ : Optional[int] = 4096 , lowercase_ : Optional[int] = 12 , lowercase_ : Optional[int] = 16 , lowercase_ : Optional[int] = 4096 , lowercase_ : Optional[int] = 12 , lowercase_ : Optional[int] = 16 , lowercase_ : Optional[float] = 0.1 , lowercase_ : Optional[float] = 0.1 , lowercase_ : Optional[int] = 512 , lowercase_ : Optional[float] = 0.02 , lowercase_ : Optional[bool] = True , lowercase_ : Optional[bool] = True , lowercase_ : Optional[int] = 0 , lowercase_ : Optional[int] = 2 , lowercase_ : Optional[int] = 32 , lowercase_ : Optional[int] = 128 , lowercase_ : Optional[bool] = False , lowercase_ : Optional[float] = 0.0 , lowercase_ : Optional[bool] = True , lowercase_ : Optional[int] = 0 , lowercase_ : Optional[int] = 1 , lowercase_ : Optional[int] = 2 , **lowercase_ : List[Any] , ):
lowercase_ : Dict = vocab_size
lowercase_ : List[Any] = hidden_size
lowercase_ : Optional[int] = encoder_ffn_dim
lowercase_ : Any = num_encoder_layers
lowercase_ : int = num_encoder_attention_heads
lowercase_ : Tuple = decoder_ffn_dim
lowercase_ : List[str] = num_decoder_layers
lowercase_ : List[str] = num_decoder_attention_heads
lowercase_ : List[Any] = max_position_embeddings
lowercase_ : int = init_std # Normal(0, this parameter)
lowercase_ : Tuple = activation_function
# parameters for xlmprophetnet
lowercase_ : Optional[Any] = ngram
lowercase_ : str = num_buckets
lowercase_ : str = relative_max_distance
lowercase_ : Union[str, Any] = disable_ngram_loss
lowercase_ : List[Any] = eps
# 3 Types of Dropout
lowercase_ : Optional[Any] = attention_dropout
lowercase_ : str = activation_dropout
lowercase_ : Any = dropout
lowercase_ : Tuple = use_cache
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , add_cross_attention=lowercase_ , decoder_start_token_id=lowercase_ , **lowercase_ , )
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Optional[Any] ):
raise NotImplementedError(
"""This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
""" `num_decoder_layers`.""" )
| 21 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
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
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
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
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = 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))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# 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.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > 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}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# 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
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Tuple ):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
lowercase_ : str = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : List[Any] = """sshleifer/tiny-gpt2"""
lowercase_ : Dict = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : List[str] = PyTorchBenchmark(lowercase_ )
lowercase_ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Dict = """sgugger/tiny-distilbert-classification"""
lowercase_ : Optional[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , only_pretrain_model=lowercase_ , )
lowercase_ : Optional[Any] = PyTorchBenchmark(lowercase_ )
lowercase_ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[Any] = """sshleifer/tiny-gpt2"""
lowercase_ : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , torchscript=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : List[str] = PyTorchBenchmark(lowercase_ )
lowercase_ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Any = """sshleifer/tiny-gpt2"""
lowercase_ : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , fpaa=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : Dict = PyTorchBenchmark(lowercase_ )
lowercase_ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Optional[Any] = """sshleifer/tiny-gpt2"""
lowercase_ : Tuple = AutoConfig.from_pretrained(lowercase_ )
# set architectures equal to `None`
lowercase_ : Union[str, Any] = None
lowercase_ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : Any = PyTorchBenchmark(lowercase_ , configs=[config] )
lowercase_ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : int = """sshleifer/tiny-gpt2"""
lowercase_ : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : Tuple = PyTorchBenchmark(lowercase_ )
lowercase_ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == """cpu""" , """Can't do half precision""" )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : List[Any] = """sshleifer/tiny-gpt2"""
lowercase_ : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , fpaa=lowercase_ , multi_process=lowercase_ , )
lowercase_ : Optional[Any] = PyTorchBenchmark(lowercase_ )
lowercase_ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = """sshleifer/tiny-gpt2"""
lowercase_ : List[Any] = AutoConfig.from_pretrained(lowercase_ )
lowercase_ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : Union[str, Any] = PyTorchBenchmark(lowercase_ , configs=[config] )
lowercase_ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Union[str, Any] = """sshleifer/tinier_bart"""
lowercase_ : Optional[Any] = AutoConfig.from_pretrained(lowercase_ )
lowercase_ : List[str] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : Union[str, Any] = PyTorchBenchmark(lowercase_ , configs=[config] )
lowercase_ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : str = """sshleifer/tiny-gpt2"""
lowercase_ : Tuple = AutoConfig.from_pretrained(lowercase_ )
lowercase_ : Dict = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : Tuple = PyTorchBenchmark(lowercase_ , configs=[config] )
lowercase_ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = """sshleifer/tinier_bart"""
lowercase_ : Any = AutoConfig.from_pretrained(lowercase_ )
lowercase_ : List[str] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase_ , )
lowercase_ : str = PyTorchBenchmark(lowercase_ , configs=[config] )
lowercase_ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , save_to_csv=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(lowercase_ , """inf_time.csv""" ) , train_memory_csv_file=os.path.join(lowercase_ , """train_mem.csv""" ) , inference_memory_csv_file=os.path.join(lowercase_ , """inf_mem.csv""" ) , train_time_csv_file=os.path.join(lowercase_ , """train_time.csv""" ) , env_info_csv_file=os.path.join(lowercase_ , """env.csv""" ) , multi_process=lowercase_ , )
lowercase_ : Optional[Any] = PyTorchBenchmark(lowercase_ )
benchmark.run()
self.assertTrue(Path(os.path.join(lowercase_ , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase_ , """train_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase_ , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase_ , """train_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(lowercase_ , """env.csv""" ) ).exists() )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : str = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(lowercase_ : Union[str, Any] ):
self.assertTrue(hasattr(lowercase_ , """sequential""" ) )
self.assertTrue(hasattr(lowercase_ , """cumulative""" ) )
self.assertTrue(hasattr(lowercase_ , """current""" ) )
self.assertTrue(hasattr(lowercase_ , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=lowercase_ , inference=lowercase_ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(lowercase_ , """log.txt""" ) , log_print=lowercase_ , trace_memory_line_by_line=lowercase_ , multi_process=lowercase_ , )
lowercase_ : Optional[int] = PyTorchBenchmark(lowercase_ )
lowercase_ : Tuple = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(lowercase_ , """log.txt""" ) ).exists() )
| 21 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
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 logging
if is_vision_available():
import PIL
_lowercase : Any = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Dict ) -> 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 __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = ['''pixel_values''']
def __init__( self : Optional[Any] , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = PILImageResampling.BILINEAR , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : bool = True , lowercase_ : Union[int, float] = 1 / 255 , lowercase_ : bool = True , lowercase_ : bool = True , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , **lowercase_ : List[str] , ):
super().__init__(**lowercase_ )
lowercase_ : Optional[int] = size if size is not None else {"""shortest_edge""": 256}
lowercase_ : Optional[int] = get_size_dict(lowercase_ , default_to_square=lowercase_ )
lowercase_ : List[Any] = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowercase_ : List[str] = get_size_dict(lowercase_ , param_name="""crop_size""" )
lowercase_ : str = do_resize
lowercase_ : Dict = size
lowercase_ : Union[str, Any] = do_center_crop
lowercase_ : int = crop_size
lowercase_ : int = resample
lowercase_ : int = do_rescale
lowercase_ : str = rescale_factor
lowercase_ : Tuple = offset
lowercase_ : Any = do_normalize
lowercase_ : List[str] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase_ : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : PILImageResampling = PILImageResampling.BILINEAR , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : List[str] , ):
lowercase_ : Union[str, Any] = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "shortest_edge" in size:
lowercase_ : str = get_resize_output_image_size(lowercase_ , size["""shortest_edge"""] , default_to_square=lowercase_ )
elif "height" in size and "width" in size:
lowercase_ : List[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(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Optional[Any] , ):
lowercase_ : Optional[Any] = get_size_dict(lowercase_ )
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(lowercase_ , size=(size["""height"""], size["""width"""]) , data_format=lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : np.ndarray , lowercase_ : Union[int, float] , lowercase_ : bool = True , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Any , ):
lowercase_ : List[str] = image.astype(np.floataa )
if offset:
lowercase_ : Optional[int] = image - (scale / 2)
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : np.ndarray , lowercase_ : Union[float, List[float]] , lowercase_ : Union[float, List[float]] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : List[str] , ):
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : bool = None , lowercase_ : float = None , lowercase_ : bool = None , lowercase_ : bool = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[ChannelDimension] = ChannelDimension.FIRST , ):
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
lowercase_ : Dict = to_numpy_array(lowercase_ )
if do_resize:
lowercase_ : Union[str, Any] = self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ )
if do_center_crop:
lowercase_ : Union[str, Any] = self.center_crop(lowercase_ , size=lowercase_ )
if do_rescale:
lowercase_ : Union[str, Any] = self.rescale(image=lowercase_ , scale=lowercase_ , offset=lowercase_ )
if do_normalize:
lowercase_ : Tuple = self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ )
lowercase_ : str = to_channel_dimension_format(lowercase_ , lowercase_ )
return image
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : bool = None , lowercase_ : float = None , lowercase_ : bool = None , lowercase_ : bool = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : ChannelDimension = ChannelDimension.FIRST , **lowercase_ : Tuple , ):
lowercase_ : int = do_resize if do_resize is not None else self.do_resize
lowercase_ : Tuple = resample if resample is not None else self.resample
lowercase_ : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase_ : Dict = do_rescale if do_rescale is not None else self.do_rescale
lowercase_ : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase_ : Tuple = offset if offset is not None else self.offset
lowercase_ : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
lowercase_ : Optional[int] = image_mean if image_mean is not None else self.image_mean
lowercase_ : List[str] = image_std if image_std is not None else self.image_std
lowercase_ : Dict = size if size is not None else self.size
lowercase_ : Any = get_size_dict(lowercase_ , default_to_square=lowercase_ )
lowercase_ : List[Any] = crop_size if crop_size is not None else self.crop_size
lowercase_ : Optional[Any] = get_size_dict(lowercase_ , param_name="""crop_size""" )
if not valid_images(lowercase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowercase_ : Union[str, Any] = make_batched(lowercase_ )
lowercase_ : Any = [
[
self._preprocess_image(
image=lowercase_ , do_resize=lowercase_ , size=lowercase_ , resample=lowercase_ , do_center_crop=lowercase_ , crop_size=lowercase_ , do_rescale=lowercase_ , rescale_factor=lowercase_ , offset=lowercase_ , do_normalize=lowercase_ , image_mean=lowercase_ , image_std=lowercase_ , data_format=lowercase_ , )
for img in video
]
for video in videos
]
lowercase_ : List[str] = {"""pixel_values""": videos}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 21 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("""only integers accepted as input""" )
else:
lowercase_ : Dict = str(abs(UpperCAmelCase__ ) )
lowercase_ : Any = [list(UpperCAmelCase__ ) for char in range(len(UpperCAmelCase__ ) )]
for index in range(len(UpperCAmelCase__ ) ):
num_transpositions[index].pop(UpperCAmelCase__ )
return max(
int("""""".join(list(UpperCAmelCase__ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("doctest").testmod()
| 21 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 1 |
'''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 lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , 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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = 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 __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = 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 __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# 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]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | '''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
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
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
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
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = 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))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# 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.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > 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}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# 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
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 1 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCamelCase ( ) -> Dict:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(UpperCAmelCase__ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def lowerCamelCase ( ) -> int:
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def lowerCamelCase ( ) -> Union[str, Any]:
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(UpperCAmelCase__ ):
http_head("""https://huggingface.co""" )
| 21 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | 1 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowercase : List[Any] = "\nimport os\n"
_lowercase : List[str] = "\ndef foo():\n import os\n return False\n"
_lowercase : List[str] = "\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n"
_lowercase : Any = "\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n"
_lowercase : str = "\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n"
_lowercase : List[Any] = "\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n"
_lowercase : Optional[int] = "\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n"
_lowercase : Tuple = "\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n"
_lowercase : List[Any] = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n"
_lowercase : Optional[int] = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n"
_lowercase : Any = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("""case""" , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Tuple ) -> Union[str, Any]:
lowercase_ : Tuple = os.path.join(UpperCAmelCase__ , """test_file.py""" )
with open(UpperCAmelCase__ , """w""" ) as _tmp_file:
_tmp_file.write(UpperCAmelCase__ )
lowercase_ : Optional[Any] = get_imports(UpperCAmelCase__ )
assert parsed_imports == ["os"]
| 21 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : list ) -> List[str]:
_enforce_args(UpperCAmelCase__ , UpperCAmelCase__ )
if n == 0:
return 0
lowercase_ : Dict = float("""-inf""" )
for i in range(1 , n + 1 ):
lowercase_ : Optional[int] = max(
UpperCAmelCase__ , prices[i - 1] + naive_cut_rod_recursive(n - i , UpperCAmelCase__ ) )
return max_revue
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : list ) -> Dict:
_enforce_args(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Any = [float("""-inf""" ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : list , UpperCAmelCase__ : list ) -> Optional[Any]:
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
lowercase_ : Any = float("""-inf""" )
for i in range(1 , n + 1 ):
lowercase_ : List[str] = max(
UpperCAmelCase__ , prices[i - 1] + _top_down_cut_rod_recursive(n - i , UpperCAmelCase__ , UpperCAmelCase__ ) , )
lowercase_ : Optional[Any] = max_revenue
return max_rev[n]
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : list ) -> Tuple:
_enforce_args(UpperCAmelCase__ , UpperCAmelCase__ )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
lowercase_ : int = [float("""-inf""" ) for _ in range(n + 1 )]
lowercase_ : Optional[Any] = 0
for i in range(1 , n + 1 ):
lowercase_ : Optional[int] = max_rev[i]
for j in range(1 , i + 1 ):
lowercase_ : int = max(UpperCAmelCase__ , prices[j - 1] + max_rev[i - j] )
lowercase_ : Union[str, Any] = max_revenue_i
return max_rev[n]
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : list ) -> str:
if n < 0:
lowercase_ : str = F'''n must be greater than or equal to 0. Got n = {n}'''
raise ValueError(UpperCAmelCase__ )
if n > len(UpperCAmelCase__ ):
lowercase_ : int = (
"""Each integral piece of rod must have a corresponding price. """
F'''Got n = {n} but length of prices = {len(UpperCAmelCase__ )}'''
)
raise ValueError(UpperCAmelCase__ )
def lowerCamelCase ( ) -> Dict:
lowercase_ : Tuple = [6, 10, 12, 15, 20, 23]
lowercase_ : Optional[Any] = len(UpperCAmelCase__ )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
lowercase_ : Optional[Any] = 36
lowercase_ : Dict = top_down_cut_rod(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : int = bottom_up_cut_rod(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : int = naive_cut_rod_recursive(UpperCAmelCase__ , UpperCAmelCase__ )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
_lowercase : int = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n"
_lowercase : Optional[int] = "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"
_lowercase : Union[str, Any] = "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n predictions: list of predictions to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n \"accuracy\": Accuracy\n \"f1\": F1 score\n \"pearson\": Pearson Correlation\n \"spearmanr\": Spearman Correlation\n \"matthews_correlation\": Matthew Correlation\nExamples:\n\n >>> glue_metric = datasets.load_metric('glue', 'sst2') # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'mrpc') # 'mrpc' or 'qqp'\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'stsb')\n >>> references = [0., 1., 2., 3., 4., 5.]\n >>> predictions = [0., 1., 2., 3., 4., 5.]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n {'pearson': 1.0, 'spearmanr': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'cola')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str ) -> Tuple:
return float((preds == labels).mean() )
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[Any] ) -> Dict:
lowercase_ : List[str] = simple_accuracy(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = float(fa_score(y_true=UpperCAmelCase__ , y_pred=UpperCAmelCase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowerCamelCase ( UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[Any] ) -> List[Any]:
lowercase_ : List[Any] = float(pearsonr(UpperCAmelCase__ , UpperCAmelCase__ )[0] )
lowercase_ : List[Any] = float(spearmanr(UpperCAmelCase__ , UpperCAmelCase__ )[0] )
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class __magic_name__ ( datasets.Metric):
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
if self.config_name not in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"cola",
"stsb",
"mrpc",
"qqp",
"qnli",
"rte",
"wnli",
"hans",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
"""\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
"""references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
} ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" , )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : str , lowercase_ : List[Any] ):
if self.config_name == "cola":
return {"matthews_correlation": matthews_corrcoef(lowercase_ , lowercase_ )}
elif self.config_name == "stsb":
return pearson_and_spearman(lowercase_ , lowercase_ )
elif self.config_name in ["mrpc", "qqp"]:
return acc_and_fa(lowercase_ , lowercase_ )
elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
return {"accuracy": simple_accuracy(lowercase_ , lowercase_ )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
"""\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
_lowercase : int = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 21 | '''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | 1 |
'''simple docstring'''
# 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
_lowercase : List[str] = get_logger()
_lowercase : Optional[dict] = None
class __magic_name__ ( TensorFormatter[Mapping, '''jax.Array''', Mapping]):
def __init__( self : Union[str, Any] , lowercase_ : Optional[Any]=None , lowercase_ : Any=None , **lowercase_ : int ):
super().__init__(features=lowercase_ )
import jax
from jaxlib.xla_client import Device
if isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Expected {device} to be a `str` not {type(lowercase_ )}, 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`.""" )
lowercase_ : List[str] = device if isinstance(lowercase_ , lowercase_ ) 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:
lowercase_ : int = 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] )}.''' )
lowercase_ : Any = str(jax.devices()[0] )
lowercase_ : Optional[Any] = jnp_array_kwargs
@staticmethod
def SCREAMING_SNAKE_CASE_ ( ):
import jax
return {str(lowercase_ ): device for device in jax.devices()}
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Dict ):
import jax
import jax.numpy as jnp
if isinstance(lowercase_ , lowercase_ ) and column:
if all(
isinstance(lowercase_ , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(lowercase_ , axis=0 )
return column
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Union[str, Any] ):
import jax
import jax.numpy as jnp
if isinstance(lowercase_ , (str, bytes, type(lowercase_ )) ):
return value
elif isinstance(lowercase_ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowercase_ : List[str] = {}
if isinstance(lowercase_ , (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:
lowercase_ : List[str] = {"""dtype""": jnp.intaa}
else:
lowercase_ : str = {"""dtype""": jnp.intaa}
elif isinstance(lowercase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowercase_ : List[Any] = {"""dtype""": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(lowercase_ , PIL.Image.Image ):
lowercase_ : str = np.asarray(lowercase_ )
# 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:
lowercase_ : int = 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(lowercase_ , **{**default_dtype, **self.jnp_array_kwargs} )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Tuple ):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(lowercase_ , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(lowercase_ , """__array__""" ) and not isinstance(lowercase_ , jax.Array ):
lowercase_ : int = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(lowercase_ , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(lowercase_ ) for substruct in data_struct] )
elif isinstance(lowercase_ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(lowercase_ ) for substruct in data_struct] )
return self._tensorize(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : dict ):
return map_nested(self._recursive_tensorize , lowercase_ , map_list=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : pa.Table ):
lowercase_ : Union[str, Any] = self.numpy_arrow_extractor().extract_row(lowercase_ )
lowercase_ : Dict = self.python_features_decoder.decode_row(lowercase_ )
return self.recursive_tensorize(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : pa.Table ):
lowercase_ : Optional[Any] = self.numpy_arrow_extractor().extract_column(lowercase_ )
lowercase_ : str = self.python_features_decoder.decode_column(lowercase_ , pa_table.column_names[0] )
lowercase_ : Any = self.recursive_tensorize(lowercase_ )
lowercase_ : int = self._consolidate(lowercase_ )
return column
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : pa.Table ):
lowercase_ : List[Any] = self.numpy_arrow_extractor().extract_batch(lowercase_ )
lowercase_ : Tuple = self.python_features_decoder.decode_batch(lowercase_ )
lowercase_ : Optional[int] = self.recursive_tensorize(lowercase_ )
for column_name in batch:
lowercase_ : Union[str, Any] = self._consolidate(batch[column_name] )
return batch
| 21 | '''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : List[Any] ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None ):
lowercase_ : Optional[Any] = {}
lowercase_ : Tuple = {}
if prompt is not None:
lowercase_ : Tuple = prompt
if generate_kwargs is not None:
lowercase_ : List[str] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase_ : List[Any] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase_ : str = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : Optional[int] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : Tuple=None ):
lowercase_ : List[Any] = load_image(lowercase_ )
if prompt is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowercase_ )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase_ : List[Any] = self.model.config.model_type
if model_type == "git":
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : Union[str, Any] = self.tokenizer(text=lowercase_ , add_special_tokens=lowercase_ ).input_ids
lowercase_ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase_ : List[Any] = torch.tensor(lowercase_ ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase_ : Union[str, Any] = self.image_processor(images=lowercase_ , header_text=lowercase_ , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : List[str] = self.tokenizer(lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase_ : str = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict , lowercase_ : Optional[Any]=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , lowercase_ )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase_ : Any = None
if generate_kwargs is None:
lowercase_ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase_ : Dict = model_inputs.pop(self.model.main_input_name )
lowercase_ : Any = self.model.generate(lowercase_ , **lowercase_ , **lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] ):
lowercase_ : List[str] = []
for output_ids in model_outputs:
lowercase_ : Union[str, Any] = {
"""generated_text""": self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , )
}
records.append(lowercase_ )
return records
| 21 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Optional[int] , lowercase_ : List[str] , lowercase_ : Any=7 , lowercase_ : Tuple=3 , lowercase_ : str=30 , lowercase_ : Optional[Any]=400 , lowercase_ : Optional[Any]=True , lowercase_ : Optional[Any]=None , lowercase_ : Optional[int]=True , lowercase_ : int=[0.5, 0.5, 0.5] , lowercase_ : Tuple=[0.5, 0.5, 0.5] , lowercase_ : int=True , lowercase_ : List[str]=1 / 255 , lowercase_ : List[Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase_ : List[str] = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
lowercase_ : Any = parent
lowercase_ : Optional[Any] = batch_size
lowercase_ : int = num_channels
lowercase_ : Any = min_resolution
lowercase_ : Any = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : int = size
lowercase_ : List[Any] = do_normalize
lowercase_ : int = image_mean
lowercase_ : Union[str, Any] = image_std
lowercase_ : str = do_rescale
lowercase_ : List[Any] = rescale_factor
lowercase_ : Union[str, Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self : str ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Optional[int] , lowercase_ : Union[str, Any]=False ):
if not batched:
lowercase_ : List[Any] = image_inputs[0]
if isinstance(lowercase_ , Image.Image ):
lowercase_ , lowercase_ : Dict = image.size
else:
lowercase_ , lowercase_ : List[Any] = image.shape[1], image.shape[2]
if w < h:
lowercase_ : str = int(self.size["""shortest_edge"""] * h / w )
lowercase_ : List[Any] = self.size["""shortest_edge"""]
elif w > h:
lowercase_ : List[str] = self.size["""shortest_edge"""]
lowercase_ : int = int(self.size["""shortest_edge"""] * w / h )
else:
lowercase_ : int = self.size["""shortest_edge"""]
lowercase_ : str = self.size["""shortest_edge"""]
else:
lowercase_ : Dict = []
for image in image_inputs:
lowercase_ , lowercase_ : Optional[int] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase_ : Union[str, Any] = max(lowercase_ , key=lambda lowercase_ : item[0] )[0]
lowercase_ : Tuple = max(lowercase_ , key=lambda lowercase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = DetaImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = DetaImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """image_mean""" ) )
self.assertTrue(hasattr(lowercase_ , """image_std""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """do_rescale""" ) )
self.assertTrue(hasattr(lowercase_ , """do_pad""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333} )
self.assertEqual(image_processor.do_pad , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
# Initialize image_processing
lowercase_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , Image.Image )
# Test not batched input
lowercase_ : List[str] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : Dict = self.image_processor_tester.get_expected_values(lowercase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ , lowercase_ : Optional[int] = self.image_processor_tester.get_expected_values(lowercase_ , batched=lowercase_ )
lowercase_ : List[Any] = image_processing(lowercase_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self : int ):
# Initialize image_processing
lowercase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , numpify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , np.ndarray )
# Test not batched input
lowercase_ : str = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : str = self.image_processor_tester.get_expected_values(lowercase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : int = self.image_processor_tester.get_expected_values(lowercase_ , batched=lowercase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self : str ):
# Initialize image_processing
lowercase_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase_ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , torchify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , torch.Tensor )
# Test not batched input
lowercase_ : Any = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : Union[str, Any] = self.image_processor_tester.get_expected_values(lowercase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase_ : Optional[Any] = image_processing(lowercase_ , return_tensors="""pt""" ).pixel_values
lowercase_ , lowercase_ : Tuple = self.image_processor_tester.get_expected_values(lowercase_ , batched=lowercase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
# prepare image and target
lowercase_ : Dict = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
lowercase_ : int = json.loads(f.read() )
lowercase_ : List[Any] = {"""image_id""": 39769, """annotations""": target}
# encode them
lowercase_ : Optional[Any] = DetaImageProcessor()
lowercase_ : Tuple = image_processing(images=lowercase_ , annotations=lowercase_ , return_tensors="""pt""" )
# verify pixel values
lowercase_ : Tuple = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , lowercase_ )
lowercase_ : Tuple = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , lowercase_ , atol=1E-4 ) )
# verify area
lowercase_ : str = torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , lowercase_ ) )
# verify boxes
lowercase_ : Union[str, Any] = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , lowercase_ )
lowercase_ : Any = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , lowercase_ , atol=1E-3 ) )
# verify image_id
lowercase_ : Union[str, Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , lowercase_ ) )
# verify is_crowd
lowercase_ : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , lowercase_ ) )
# verify class_labels
lowercase_ : Optional[int] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , lowercase_ ) )
# verify orig_size
lowercase_ : List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , lowercase_ ) )
# verify size
lowercase_ : List[str] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , lowercase_ ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
# prepare image, target and masks_path
lowercase_ : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
lowercase_ : List[Any] = json.loads(f.read() )
lowercase_ : List[str] = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}
lowercase_ : List[str] = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
lowercase_ : Tuple = DetaImageProcessor(format="""coco_panoptic""" )
lowercase_ : List[Any] = image_processing(images=lowercase_ , annotations=lowercase_ , masks_path=lowercase_ , return_tensors="""pt""" )
# verify pixel values
lowercase_ : str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , lowercase_ )
lowercase_ : Optional[Any] = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , lowercase_ , atol=1E-4 ) )
# verify area
lowercase_ : Optional[Any] = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , lowercase_ ) )
# verify boxes
lowercase_ : Dict = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , lowercase_ )
lowercase_ : List[Any] = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , lowercase_ , atol=1E-3 ) )
# verify image_id
lowercase_ : Dict = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , lowercase_ ) )
# verify is_crowd
lowercase_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , lowercase_ ) )
# verify class_labels
lowercase_ : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , lowercase_ ) )
# verify masks
lowercase_ : Optional[Any] = 822873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , lowercase_ )
# verify orig_size
lowercase_ : List[str] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , lowercase_ ) )
# verify size
lowercase_ : Dict = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , lowercase_ ) )
| 21 | '''simple docstring'''
class __magic_name__ :
def __init__( self : int , lowercase_ : list ):
lowercase_ : Dict = set_counts
lowercase_ : List[Any] = max(lowercase_ )
lowercase_ : str = len(lowercase_ )
lowercase_ : str = [1] * num_sets
lowercase_ : Dict = list(range(lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : int ):
lowercase_ : List[Any] = self.get_parent(lowercase_ )
lowercase_ : Union[str, Any] = self.get_parent(lowercase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase_ : List[str] = 0
lowercase_ : Optional[int] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase_ : int = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase_ : int = 0
lowercase_ : List[Any] = src_parent
lowercase_ : List[Any] = self.set_counts[src_parent]
lowercase_ : Tuple = max(self.max_set , lowercase_ )
return True
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
lowercase_ : int = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 21 | 1 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_lowercase : Tuple = True
except ImportError:
_lowercase : int = False
_lowercase : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCamelCase ( UpperCAmelCase__ : Namespace ) -> List[str]:
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class __magic_name__ ( _UpperCAmelCase):
@staticmethod
def SCREAMING_SNAKE_CASE_ ( lowercase_ : ArgumentParser ):
lowercase_ : Union[str, Any] = parser.add_parser("""add-new-model""" )
add_new_model_parser.add_argument("""--testing""" , action="""store_true""" , help="""If in testing mode.""" )
add_new_model_parser.add_argument("""--testing_file""" , type=lowercase_ , help="""Configuration file on which to run.""" )
add_new_model_parser.add_argument(
"""--path""" , type=lowercase_ , help="""Path to cookiecutter. Should only be used for testing purposes.""" )
add_new_model_parser.set_defaults(func=lowercase_ )
def __init__( self : List[Any] , lowercase_ : bool , lowercase_ : str , lowercase_ : int=None , *lowercase_ : str ):
lowercase_ : Any = testing
lowercase_ : Dict = testing_file
lowercase_ : Dict = path
def SCREAMING_SNAKE_CASE_ ( self : Any ):
warnings.warn(
"""The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
"""It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
"""checks, you should use `transformers-cli add-new-model-like` instead.""" )
if not _has_cookiecutter:
raise ImportError(
"""Model creation dependencies are required to use the `add_new_model` command. Install them by running """
"""the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n""" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
lowercase_ : Optional[Any] = [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
if len(lowercase_ ) > 0:
raise ValueError(
"""Several directories starting with `cookiecutter-template-` in current working directory. """
"""Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
"""change your working directory.""" )
lowercase_ : Optional[int] = (
Path(lowercase_ ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
lowercase_ : Optional[Any] = path_to_transformer_root / """templates""" / """adding_a_new_model"""
# Execute cookiecutter
if not self._testing:
cookiecutter(str(lowercase_ ) )
else:
with open(self._testing_file , """r""" ) as configuration_file:
lowercase_ : List[Any] = json.load(lowercase_ )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=lowercase_ , extra_context=lowercase_ , )
lowercase_ : List[str] = [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]
# Retrieve configuration
with open(directory + """/configuration.json""" , """r""" ) as configuration_file:
lowercase_ : int = json.load(lowercase_ )
lowercase_ : Optional[int] = configuration["""lowercase_modelname"""]
lowercase_ : List[Any] = configuration["""generate_tensorflow_pytorch_and_flax"""]
os.remove(f'''{directory}/configuration.json''' )
lowercase_ : Union[str, Any] = """PyTorch""" in generate_tensorflow_pytorch_and_flax
lowercase_ : Optional[Any] = """TensorFlow""" in generate_tensorflow_pytorch_and_flax
lowercase_ : Union[str, Any] = """Flax""" in generate_tensorflow_pytorch_and_flax
lowercase_ : Tuple = f'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
os.makedirs(lowercase_ , exist_ok=lowercase_ )
os.makedirs(f'''{path_to_transformer_root}/tests/models/{lowercase_model_name}''' , exist_ok=lowercase_ )
# Tests require submodules as they have parent imports
with open(f'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py''' , """w""" ):
pass
shutil.move(
f'''{directory}/__init__.py''' , f'''{model_dir}/__init__.py''' , )
shutil.move(
f'''{directory}/configuration_{lowercase_model_name}.py''' , f'''{model_dir}/configuration_{lowercase_model_name}.py''' , )
def remove_copy_lines(lowercase_ : Any ):
with open(lowercase_ , """r""" ) as f:
lowercase_ : str = f.readlines()
with open(lowercase_ , """w""" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(lowercase_ )
if output_pytorch:
if not self._testing:
remove_copy_lines(f'''{directory}/modeling_{lowercase_model_name}.py''' )
shutil.move(
f'''{directory}/modeling_{lowercase_model_name}.py''' , f'''{model_dir}/modeling_{lowercase_model_name}.py''' , )
shutil.move(
f'''{directory}/test_modeling_{lowercase_model_name}.py''' , f'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py''' , )
else:
os.remove(f'''{directory}/modeling_{lowercase_model_name}.py''' )
os.remove(f'''{directory}/test_modeling_{lowercase_model_name}.py''' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f'''{directory}/modeling_tf_{lowercase_model_name}.py''' )
shutil.move(
f'''{directory}/modeling_tf_{lowercase_model_name}.py''' , f'''{model_dir}/modeling_tf_{lowercase_model_name}.py''' , )
shutil.move(
f'''{directory}/test_modeling_tf_{lowercase_model_name}.py''' , f'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py''' , )
else:
os.remove(f'''{directory}/modeling_tf_{lowercase_model_name}.py''' )
os.remove(f'''{directory}/test_modeling_tf_{lowercase_model_name}.py''' )
if output_flax:
if not self._testing:
remove_copy_lines(f'''{directory}/modeling_flax_{lowercase_model_name}.py''' )
shutil.move(
f'''{directory}/modeling_flax_{lowercase_model_name}.py''' , f'''{model_dir}/modeling_flax_{lowercase_model_name}.py''' , )
shutil.move(
f'''{directory}/test_modeling_flax_{lowercase_model_name}.py''' , f'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py''' , )
else:
os.remove(f'''{directory}/modeling_flax_{lowercase_model_name}.py''' )
os.remove(f'''{directory}/test_modeling_flax_{lowercase_model_name}.py''' )
shutil.move(
f'''{directory}/{lowercase_model_name}.md''' , f'''{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md''' , )
shutil.move(
f'''{directory}/tokenization_{lowercase_model_name}.py''' , f'''{model_dir}/tokenization_{lowercase_model_name}.py''' , )
shutil.move(
f'''{directory}/tokenization_fast_{lowercase_model_name}.py''' , f'''{model_dir}/tokenization_{lowercase_model_name}_fast.py''' , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(lowercase_ : str , lowercase_ : str , lowercase_ : List[str] ):
# Create temp file
lowercase_ , lowercase_ : str = mkstemp()
lowercase_ : Any = False
with fdopen(lowercase_ , """w""" ) as new_file:
with open(lowercase_ ) as old_file:
for line in old_file:
new_file.write(lowercase_ )
if line_to_copy_below in line:
lowercase_ : List[str] = True
for line_to_copy in lines_to_copy:
new_file.write(lowercase_ )
if not line_found:
raise ValueError(f'''Line {line_to_copy_below} was not found in file.''' )
# Copy the file permissions from the old file to the new file
copymode(lowercase_ , lowercase_ )
# Remove original file
remove(lowercase_ )
# Move new file
move(lowercase_ , lowercase_ )
def skip_units(lowercase_ : List[Any] ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(lowercase_ : Any ):
with open(lowercase_ ) as datafile:
lowercase_ : int = []
lowercase_ : Union[str, Any] = False
lowercase_ : Any = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
lowercase_ : List[str] = line.split("""\"""" )[1]
lowercase_ : str = skip_units(lowercase_ )
elif "# Below: " in line and "##" not in line:
lowercase_ : List[str] = line.split("""\"""" )[1]
lowercase_ : Optional[Any] = skip_units(lowercase_ )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(lowercase_ , lowercase_ , lowercase_ )
lowercase_ : List[Any] = []
elif "# Replace with" in line and "##" not in line:
lowercase_ : str = []
elif "##" not in line:
lines_to_copy.append(lowercase_ )
remove(lowercase_ )
replace_in_files(f'''{directory}/to_replace_{lowercase_model_name}.py''' )
os.rmdir(lowercase_ )
| 21 | '''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 1 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
_lowercase : Any = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
_lowercase : Tuple = "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
_lowercase : Union[str, Any] = "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class __magic_name__ ( datasets.Metric):
def SCREAMING_SNAKE_CASE_ ( self : int ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""] , )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : str , lowercase_ : Any , lowercase_ : Tuple=None , lowercase_ : Optional[Any]=1 , lowercase_ : Any="binary" , lowercase_ : Optional[Any]=None , lowercase_ : Any="warn" , ):
lowercase_ : Union[str, Any] = recall_score(
lowercase_ , lowercase_ , labels=lowercase_ , pos_label=lowercase_ , average=lowercase_ , sample_weight=lowercase_ , zero_division=lowercase_ , )
return {"recall": float(lowercase_ ) if score.size == 1 else score}
| 21 | '''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 lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , 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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = 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 __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = 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 __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# 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]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase : Dict = logging.get_logger(__name__)
_lowercase : Dict = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''trajectory_transformer'''
UpperCamelCase__ = ['''past_key_values''']
UpperCamelCase__ = {
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : int , lowercase_ : Tuple=100 , lowercase_ : Optional[int]=5 , lowercase_ : Optional[int]=1 , lowercase_ : int=1 , lowercase_ : Any=249 , lowercase_ : List[str]=6 , lowercase_ : List[Any]=17 , lowercase_ : Tuple=25 , lowercase_ : Any=4 , lowercase_ : int=4 , lowercase_ : Optional[Any]=128 , lowercase_ : Tuple=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : List[str]=0.1 , lowercase_ : Union[str, Any]=0.00_06 , lowercase_ : List[Any]=512 , lowercase_ : Tuple=0.02 , lowercase_ : List[str]=1E-12 , lowercase_ : Union[str, Any]=1 , lowercase_ : Optional[Any]=True , lowercase_ : Tuple=1 , lowercase_ : Optional[int]=50256 , lowercase_ : Union[str, Any]=50256 , **lowercase_ : int , ):
lowercase_ : str = vocab_size
lowercase_ : Union[str, Any] = action_weight
lowercase_ : int = reward_weight
lowercase_ : List[Any] = value_weight
lowercase_ : Dict = max_position_embeddings
lowercase_ : List[str] = block_size
lowercase_ : Optional[Any] = action_dim
lowercase_ : Any = observation_dim
lowercase_ : Union[str, Any] = transition_dim
lowercase_ : int = learning_rate
lowercase_ : Tuple = n_layer
lowercase_ : Optional[int] = n_head
lowercase_ : Any = n_embd
lowercase_ : str = embd_pdrop
lowercase_ : int = attn_pdrop
lowercase_ : int = resid_pdrop
lowercase_ : Any = initializer_range
lowercase_ : List[Any] = layer_norm_eps
lowercase_ : int = kaiming_initializer_range
lowercase_ : List[str] = use_cache
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
| 21 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int=7 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=18 , lowercase_ : List[Any]=30 , lowercase_ : int=400 , lowercase_ : Dict=True , lowercase_ : List[Any]=None , lowercase_ : Dict=True , ):
lowercase_ : Tuple = size if size is not None else {"""height""": 18, """width""": 18}
lowercase_ : List[str] = parent
lowercase_ : Any = batch_size
lowercase_ : Optional[Any] = num_channels
lowercase_ : Tuple = image_size
lowercase_ : Optional[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[Any] = size
lowercase_ : Union[str, Any] = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """clusters""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Union[str, Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , obj[key] ) )
else:
self.assertEqual(obj[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , """image_processor.json""" )
image_processor_first.to_json_file(lowercase_ )
lowercase_ : Optional[Any] = self.image_processing_class.from_json_file(lowercase_ ).to_dict()
lowercase_ : Any = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowercase_ )
lowercase_ : Any = self.image_processing_class.from_pretrained(lowercase_ ).to_dict()
lowercase_ : List[str] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
pass
def lowerCamelCase ( ) -> Any:
lowercase_ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
lowercase_ : Any = Image.open(dataset[4]["""file"""] )
lowercase_ : Dict = Image.open(dataset[5]["""file"""] )
lowercase_ : int = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
lowercase_ : Optional[int] = prepare_images()
# test non-batched
lowercase_ : str = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
lowercase_ : Tuple = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowercase_ )
# test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
lowercase_ : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowercase_ )
| 21 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_lowercase : Tuple = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Dict , *lowercase_ : List[Any] , **lowercase_ : int ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Optional[Any]=None ):
lowercase_ : List[Any] = {}
if top_k is not None:
lowercase_ : str = top_k
return {}, {}, postprocess_params
def __call__( self : Union[str, Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : List[Any] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : Tuple ):
lowercase_ : Optional[int] = load_image(lowercase_ )
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, Any] ):
lowercase_ : Any = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Optional[int]=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[str] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : Any = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Dict = probs.topk(lowercase_ )
elif self.framework == "tf":
lowercase_ : Optional[Any] = stable_softmax(model_outputs.logits , axis=-1 )[0]
lowercase_ : List[str] = tf.math.top_k(lowercase_ , k=lowercase_ )
lowercase_ , lowercase_ : Optional[Any] = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : List[Any] = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowerCamelCase ( UpperCAmelCase__ : Tuple ) -> Optional[int]:
return x + 2
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[str] = """x = 3"""
lowercase_ : str = {}
lowercase_ : List[str] = evaluate(lowercase_ , {} , state=lowercase_ )
assert result == 3
self.assertDictEqual(lowercase_ , {"""x""": 3} )
lowercase_ : Tuple = """x = y"""
lowercase_ : str = {"""y""": 5}
lowercase_ : Dict = evaluate(lowercase_ , {} , state=lowercase_ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowercase_ , {"""x""": 5, """y""": 5} )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[Any] = """y = add_two(x)"""
lowercase_ : int = {"""x""": 3}
lowercase_ : Optional[Any] = evaluate(lowercase_ , {"""add_two""": add_two} , state=lowercase_ )
assert result == 5
self.assertDictEqual(lowercase_ , {"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowercase_ : Optional[Any] = evaluate(lowercase_ , {} , state=lowercase_ )
assert result is None
assert "tried to execute add_two" in out.out
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Any = """x = 3"""
lowercase_ : List[Any] = {}
lowercase_ : Any = evaluate(lowercase_ , {} , state=lowercase_ )
assert result == 3
self.assertDictEqual(lowercase_ , {"""x""": 3} )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : str = """test_dict = {'x': x, 'y': add_two(x)}"""
lowercase_ : Optional[int] = {"""x""": 3}
lowercase_ : str = evaluate(lowercase_ , {"""add_two""": add_two} , state=lowercase_ )
self.assertDictEqual(lowercase_ , {"""x""": 3, """y""": 5} )
self.assertDictEqual(lowercase_ , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[int] = """x = 3\ny = 5"""
lowercase_ : int = {}
lowercase_ : List[str] = evaluate(lowercase_ , {} , state=lowercase_ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowercase_ , {"""x""": 3, """y""": 5} )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[int] = """text = f'This is x: {x}.'"""
lowercase_ : Optional[Any] = {"""x""": 3}
lowercase_ : Optional[Any] = evaluate(lowercase_ , {} , state=lowercase_ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(lowercase_ , {"""x""": 3, """text""": """This is x: 3."""} )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : str = """if x <= 3:\n y = 2\nelse:\n y = 5"""
lowercase_ : List[Any] = {"""x""": 3}
lowercase_ : Tuple = evaluate(lowercase_ , {} , state=lowercase_ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(lowercase_ , {"""x""": 3, """y""": 2} )
lowercase_ : Tuple = {"""x""": 8}
lowercase_ : Tuple = evaluate(lowercase_ , {} , state=lowercase_ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowercase_ , {"""x""": 8, """y""": 5} )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Optional[int] = """test_list = [x, add_two(x)]"""
lowercase_ : Dict = {"""x""": 3}
lowercase_ : Any = evaluate(lowercase_ , {"""add_two""": add_two} , state=lowercase_ )
self.assertListEqual(lowercase_ , [3, 5] )
self.assertDictEqual(lowercase_ , {"""x""": 3, """test_list""": [3, 5]} )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : str = """y = x"""
lowercase_ : str = {"""x""": 3}
lowercase_ : Dict = evaluate(lowercase_ , {} , state=lowercase_ )
assert result == 3
self.assertDictEqual(lowercase_ , {"""x""": 3, """y""": 3} )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = """test_list = [x, add_two(x)]\ntest_list[1]"""
lowercase_ : Union[str, Any] = {"""x""": 3}
lowercase_ : Any = evaluate(lowercase_ , {"""add_two""": add_two} , state=lowercase_ )
assert result == 5
self.assertDictEqual(lowercase_ , {"""x""": 3, """test_list""": [3, 5]} )
lowercase_ : Dict = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
lowercase_ : Optional[int] = {"""x""": 3}
lowercase_ : Any = evaluate(lowercase_ , {"""add_two""": add_two} , state=lowercase_ )
assert result == 5
self.assertDictEqual(lowercase_ , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[Any] = """x = 0\nfor i in range(3):\n x = i"""
lowercase_ : Optional[int] = {}
lowercase_ : List[Any] = evaluate(lowercase_ , {"""range""": range} , state=lowercase_ )
assert result == 2
self.assertDictEqual(lowercase_ , {"""x""": 2, """i""": 2} )
| 21 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 21 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
_lowercase : Union[str, Any] = [8, 5, 9, 7]
_lowercase : Optional[int] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowercase : Union[str, Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self : int , lowercase_ : list[int] , lowercase_ : list[list[int]] , lowercase_ : list[list[int]] , ):
lowercase_ : List[Any] = claim_vector
lowercase_ : List[str] = allocated_resources_table
lowercase_ : Optional[int] = maximum_claim_table
def SCREAMING_SNAKE_CASE_ ( self : Any ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def SCREAMING_SNAKE_CASE_ ( self : str ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(lowercase_ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {self.__need().index(lowercase_ ): i for i in self.__need()}
def SCREAMING_SNAKE_CASE_ ( self : Tuple , **lowercase_ : Dict ):
lowercase_ : List[str] = self.__need()
lowercase_ : Optional[int] = self.__allocated_resources_table
lowercase_ : Dict = self.__available_resources()
lowercase_ : str = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
lowercase_ : Any = False
for each_need in need_list:
lowercase_ : Union[str, Any] = True
for index, need in enumerate(lowercase_ ):
if need > available_resources[index]:
lowercase_ : Optional[int] = False
break
if execution:
lowercase_ : List[Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowercase_ : Any = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(lowercase_ )
# update available/freed resources stack
lowercase_ : int = np.array(lowercase_ ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(lowercase_ ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def SCREAMING_SNAKE_CASE_ ( self : int ):
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(lowercase_ ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(lowercase_ ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(lowercase_ ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(lowercase_ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | '''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
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(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int ) -> list[int]:
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
lowercase_ : int = [True] * (num + 1)
lowercase_ : int = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , UpperCAmelCase__ ):
lowercase_ : List[Any] = False
p += 1
return [prime for prime in range(2 , num + 1 ) if primes[prime]]
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : Any = int(input("Enter a positive integer: ").strip())
print(prime_sieve_eratosthenes(user_num))
| 21 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def lowerCamelCase ( UpperCAmelCase__ : Any ) -> int:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self : int ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : Any , lowercase_ : Tuple , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : List[Any]=None , **lowercase_ : Any ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Dict = TFVisionTextDualEncoderModel(lowercase_ )
lowercase_ : int = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Union[str, Any] = TFVisionTextDualEncoderModel(vision_model=lowercase_ , text_model=lowercase_ )
lowercase_ : Union[str, Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str]=None , **lowercase_ : List[str] ):
lowercase_ , lowercase_ : str = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : List[str] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : List[str] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : str=None , **lowercase_ : Any ):
lowercase_ , lowercase_ : Tuple = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = TFVisionTextDualEncoderModel(vision_model=lowercase_ , text_model=lowercase_ )
lowercase_ : int = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : str = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : List[Any] = TFVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : Dict = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Optional[int] = after_output[0].numpy()
lowercase_ : int = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Any , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Any=None , **lowercase_ : Union[str, Any] ):
lowercase_ , lowercase_ : List[str] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Any = TFVisionTextDualEncoderModel(vision_model=lowercase_ , text_model=lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : List[Any] = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : int = to_atuple(vision_model.config.image_size )
lowercase_ : int = to_atuple(vision_model.config.patch_size )
lowercase_ : List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : int = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Optional[int] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , 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 SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : str = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : str = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Dict = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Dict = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Dict = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ , lowercase_ : Tuple = self.get_pretrained_model_and_inputs()
lowercase_ : List[str] = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : str = TFVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : int = model_a(**lowercase_ )
lowercase_ : str = after_outputs[0].numpy()
lowercase_ : Dict = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_tf
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Tuple = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
lowercase_ : Optional[int] = 13
lowercase_ : Tuple = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase_ : List[str] = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase_ : Optional[Any] = random_attention_mask([batch_size, 4] )
lowercase_ : Any = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : Any , lowercase_ : Union[str, Any] ):
lowercase_ : Any = TFViTModel(lowercase_ , name="""vision_model""" )
lowercase_ : Optional[Any] = TFBertModel(lowercase_ , name="""text_model""" )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Optional[int] = TFViTModelTester(self )
lowercase_ : Tuple = TFBertModelTester(self )
lowercase_ : Optional[int] = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Union[str, Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ : Tuple = vision_config_and_inputs
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Any = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : str ):
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
lowercase_ : str = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
lowercase_ : Optional[int] = 13
lowercase_ : List[Any] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase_ : Tuple = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase_ : Optional[Any] = random_attention_mask([batch_size, 4] )
lowercase_ : str = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : Union[str, Any] ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = TFVisionTextDualEncoderModel(vision_model=lowercase_ , text_model=lowercase_ )
lowercase_ : int = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : str = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
lowercase_ : Dict = to_atuple(vision_model.config.image_size )
lowercase_ : Tuple = to_atuple(vision_model.config.patch_size )
lowercase_ : Optional[int] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : List[str] = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Dict = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , 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 SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[str] , lowercase_ : Dict ):
lowercase_ : Dict = TFDeiTModel(lowercase_ , name="""vision_model""" )
lowercase_ : Tuple = TFRobertaModel(lowercase_ , name="""text_model""" )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Optional[Any] = TFDeiTModelTester(self )
lowercase_ : int = TFRobertaModelTester(self )
lowercase_ : Optional[int] = vit_model_tester.prepare_config_and_inputs()
lowercase_ : int = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ : int = vision_config_and_inputs
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Tuple = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : List[str] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
lowercase_ : List[Any] = 13
lowercase_ : Optional[int] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowercase_ : Any = random_attention_mask([batch_size, 4] )
lowercase_ : Optional[int] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : str , lowercase_ : List[Any] ):
lowercase_ : Dict = TFCLIPVisionModel(lowercase_ , name="""vision_model""" )
lowercase_ : Dict = TFBertModel(lowercase_ , name="""text_model""" )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Optional[int] = TFCLIPVisionModelTester(self )
lowercase_ : Union[str, Any] = TFBertModelTester(self )
lowercase_ : int = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Dict = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : int = vision_config_and_inputs
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Tuple = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Tuple = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=lowercase_ )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Any = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : Dict = model(**lowercase_ )
# 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]) , )
lowercase_ : List[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , lowercase_ , atol=1E-3 ) )
| 21 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
while second != 0:
lowercase_ : str = first & second
first ^= second
lowercase_ : str = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : Tuple = int(input("Enter the first number: ").strip())
_lowercase : Any = int(input("Enter the second number: ").strip())
print(f"""{add(first, second) = }""")
| 21 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
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
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
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
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = 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))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# 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.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > 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}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# 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
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def lowerCamelCase ( UpperCAmelCase__ : str ) -> str:
return "".join(sorted(UpperCAmelCase__ ) )
def lowerCamelCase ( UpperCAmelCase__ : str ) -> list[str]:
return word_by_signature[signature(UpperCAmelCase__ )]
_lowercase : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
_lowercase : List[Any] = sorted({word.strip().lower() for word in data.splitlines()})
_lowercase : List[Any] = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
_lowercase : List[str] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 21 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = CycleDiffusionPipeline
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'''negative_prompt''',
'''height''',
'''width''',
'''negative_prompt_embeds''',
}
UpperCamelCase__ = PipelineTesterMixin.required_optional_params - {'''latents'''}
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''})
UpperCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE_ ( self : int ):
torch.manual_seed(0 )
lowercase_ : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
lowercase_ : Any = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="""scaled_linear""" , num_train_timesteps=1000 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
torch.manual_seed(0 )
lowercase_ : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
lowercase_ : List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
lowercase_ : int = CLIPTextModel(lowercase_ )
lowercase_ : Dict = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase_ : Union[str, Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str , lowercase_ : int=0 ):
lowercase_ : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
lowercase_ : int = image / 2 + 0.5
if str(lowercase_ ).startswith("""mps""" ):
lowercase_ : Dict = torch.manual_seed(lowercase_ )
else:
lowercase_ : List[str] = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowercase_ : Optional[int] = {
"""prompt""": """An astronaut riding an elephant""",
"""source_prompt""": """An astronaut riding a horse""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""eta""": 0.1,
"""strength""": 0.8,
"""guidance_scale""": 3,
"""source_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Tuple = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase_ : Any = self.get_dummy_components()
lowercase_ : List[Any] = CycleDiffusionPipeline(**lowercase_ )
lowercase_ : Any = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Dict = self.get_dummy_inputs(lowercase_ )
lowercase_ : Any = pipe(**lowercase_ )
lowercase_ : Dict = output.images
lowercase_ : Dict = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
lowercase_ : List[Any] = np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : int = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowercase_ , """half""" ):
lowercase_ : List[str] = module.half()
lowercase_ : str = CycleDiffusionPipeline(**lowercase_ )
lowercase_ : Optional[Any] = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : str = self.get_dummy_inputs(lowercase_ )
lowercase_ : List[Any] = pipe(**lowercase_ )
lowercase_ : int = output.images
lowercase_ : Union[str, Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
lowercase_ : List[Any] = np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
return super().test_save_load_local()
@unittest.skip("""non-deterministic pipeline""" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
return super().test_inference_batch_single_identical()
@skip_mps
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def SCREAMING_SNAKE_CASE_ ( self : str ):
return super().test_save_load_optional_components()
@skip_mps
def SCREAMING_SNAKE_CASE_ ( self : str ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
lowercase_ : List[str] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy""" )
lowercase_ : int = init_image.resize((512, 512) )
lowercase_ : Optional[int] = """CompVis/stable-diffusion-v1-4"""
lowercase_ : Any = DDIMScheduler.from_pretrained(lowercase_ , subfolder="""scheduler""" )
lowercase_ : List[Any] = CycleDiffusionPipeline.from_pretrained(
lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , torch_dtype=torch.floataa , revision="""fp16""" )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
lowercase_ : Tuple = """A black colored car"""
lowercase_ : Optional[Any] = """A blue colored car"""
lowercase_ : List[Any] = torch.manual_seed(0 )
lowercase_ : Union[str, Any] = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type="""np""" , )
lowercase_ : List[str] = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5E-1
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
lowercase_ : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy""" )
lowercase_ : str = init_image.resize((512, 512) )
lowercase_ : Any = """CompVis/stable-diffusion-v1-4"""
lowercase_ : int = DDIMScheduler.from_pretrained(lowercase_ , subfolder="""scheduler""" )
lowercase_ : Any = CycleDiffusionPipeline.from_pretrained(lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
lowercase_ : List[Any] = """A black colored car"""
lowercase_ : List[Any] = """A blue colored car"""
lowercase_ : Any = torch.manual_seed(0 )
lowercase_ : Optional[Any] = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type="""np""" , )
lowercase_ : List[str] = output.images
assert np.abs(image - expected_image ).max() < 2E-2
| 21 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
_lowercase : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCamelCase ( UpperCAmelCase__ : Union[List, PIL.Image.Image, torch.Tensor] ) -> List[Any]:
warnings.warn(
"""The preprocess method is deprecated and will be removed in a future version. Please"""
""" use VaeImageProcessor.preprocess instead""" , UpperCAmelCase__ , )
if isinstance(UpperCAmelCase__ , torch.Tensor ):
return image
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = [image]
if isinstance(image[0] , PIL.Image.Image ):
lowercase_ , lowercase_ : List[Any] = image[0].size
lowercase_ , lowercase_ : Dict = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
lowercase_ : List[Any] = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
lowercase_ : List[str] = np.concatenate(UpperCAmelCase__ , axis=0 )
lowercase_ : Tuple = np.array(UpperCAmelCase__ ).astype(np.floataa ) / 255.0
lowercase_ : str = image.transpose(0 , 3 , 1 , 2 )
lowercase_ : Any = 2.0 * image - 1.0
lowercase_ : str = torch.from_numpy(UpperCAmelCase__ )
elif isinstance(image[0] , torch.Tensor ):
lowercase_ : Tuple = torch.cat(UpperCAmelCase__ , dim=0 )
return image
def lowerCamelCase ( UpperCAmelCase__ : Union[List, PIL.Image.Image, torch.Tensor] ) -> List[Any]:
if isinstance(UpperCAmelCase__ , torch.Tensor ):
return mask
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : Any = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
lowercase_ , lowercase_ : List[Any] = mask[0].size
lowercase_ , lowercase_ : List[str] = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
lowercase_ : Any = [np.array(m.convert("""L""" ).resize((w, h) , resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
lowercase_ : List[Any] = np.concatenate(UpperCAmelCase__ , axis=0 )
lowercase_ : Union[str, Any] = mask.astype(np.floataa ) / 255.0
lowercase_ : Tuple = 0
lowercase_ : Dict = 1
lowercase_ : str = torch.from_numpy(UpperCAmelCase__ )
elif isinstance(mask[0] , torch.Tensor ):
lowercase_ : Optional[int] = torch.cat(UpperCAmelCase__ , dim=0 )
return mask
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = 42
UpperCamelCase__ = 42
def __init__( self : Union[str, Any] , lowercase_ : Any , lowercase_ : int ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : Optional[int] , lowercase_ : Union[torch.Tensor, PIL.Image.Image] , lowercase_ : Union[torch.Tensor, PIL.Image.Image] , lowercase_ : int = 250 , lowercase_ : float = 0.0 , lowercase_ : int = 10 , lowercase_ : int = 10 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , ):
lowercase_ : List[str] = image
lowercase_ : Optional[Any] = _preprocess_image(lowercase_ )
lowercase_ : Dict = original_image.to(device=self.device , dtype=self.unet.dtype )
lowercase_ : Optional[Any] = _preprocess_mask(lowercase_ )
lowercase_ : Dict = mask_image.to(device=self.device , dtype=self.unet.dtype )
lowercase_ : Optional[int] = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Optional[int] = original_image.shape
lowercase_ : Dict = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(lowercase_ , lowercase_ , lowercase_ , self.device )
lowercase_ : Optional[int] = eta
lowercase_ : Optional[Any] = self.scheduler.timesteps[0] + 1
lowercase_ : int = generator[0] if isinstance(lowercase_ , lowercase_ ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
lowercase_ : Any = self.unet(lowercase_ , lowercase_ ).sample
# compute previous image: x_t -> x_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
lowercase_ : str = self.scheduler.undo_step(lowercase_ , lowercase_ , lowercase_ )
lowercase_ : Dict = t
lowercase_ : Any = (image / 2 + 0.5).clamp(0 , 1 )
lowercase_ : Any = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowercase_ : str = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase_ )
| 21 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 1 |
'''simple docstring'''
import argparse
import os
# New Code #
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 import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowercase : List[Any] = 16
_lowercase : List[str] = 32
def lowerCamelCase ( UpperCAmelCase__ : Accelerator , UpperCAmelCase__ : int = 16 ) -> Optional[Any]:
lowercase_ : Any = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase_ : int = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCAmelCase__ : int ):
# max_length=None => use the model max length (it's actually the default)
lowercase_ : List[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCAmelCase__ , max_length=UpperCAmelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase_ : Optional[int] = datasets.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase_ : Any = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCAmelCase__ : int ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase_ : Optional[int] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase_ : List[str] = 16
elif accelerator.mixed_precision != "no":
lowercase_ : Union[str, Any] = 8
else:
lowercase_ : List[str] = None
return tokenizer.pad(
UpperCAmelCase__ , padding="""longest""" , max_length=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase_ : List[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
lowercase_ : Tuple = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowercase : Optional[int] = mocked_dataloaders # noqa: F811
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] ) -> List[str]:
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCAmelCase__ ) == "1":
lowercase_ : List[str] = 2
# Initialize accelerator
lowercase_ : Tuple = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase_ : List[Any] = config["""lr"""]
lowercase_ : Optional[Any] = int(config["""num_epochs"""] )
lowercase_ : Optional[int] = int(config["""seed"""] )
lowercase_ : Any = int(config["""batch_size"""] )
lowercase_ : Union[str, Any] = evaluate.load("""glue""" , """mrpc""" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=UpperCAmelCase__ )
def inner_training_loop(UpperCAmelCase__ : Union[str, Any] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(UpperCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase_ : Any = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCAmelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
lowercase_ : Union[str, Any] = AdamW(params=model.parameters() , lr=UpperCAmelCase__ )
lowercase_ , lowercase_ : int = get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__ )
# Instantiate scheduler
lowercase_ : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCAmelCase__ ) * num_epochs) , )
# 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.
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ : Optional[int] = accelerator.prepare(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Now we train the model
for epoch in range(UpperCAmelCase__ ):
model.train()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase_ : str = model(**UpperCAmelCase__ )
lowercase_ : Tuple = outputs.loss
accelerator.backward(UpperCAmelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase_ : Tuple = model(**UpperCAmelCase__ )
lowercase_ : int = outputs.logits.argmax(dim=-1 )
lowercase_ , lowercase_ : List[str] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=UpperCAmelCase__ , references=UpperCAmelCase__ , )
lowercase_ : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , UpperCAmelCase__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def lowerCamelCase ( ) -> int:
lowercase_ : List[Any] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowercase_ : int = parser.parse_args()
lowercase_ : Dict = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 21 | '''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> Any:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase__ , int(b / 2 ) ) * actual_power(UpperCAmelCase__ , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase__ , int(b / 2 ) ) * actual_power(UpperCAmelCase__ , int(b / 2 ) )
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> float:
if b < 0:
return 1 / actual_power(UpperCAmelCase__ , UpperCAmelCase__ )
return actual_power(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
print(power(-2, -3))
| 21 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase : Optional[int] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
"processing_speech_to_text": ["Speech2TextProcessor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : int = ["Speech2TextTokenizer"]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[int] = ["Speech2TextFeatureExtractor"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Tuple = [
"TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSpeech2TextForConditionalGeneration",
"TFSpeech2TextModel",
"TFSpeech2TextPreTrainedModel",
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : int = [
"SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2TextForConditionalGeneration",
"Speech2TextModel",
"Speech2TextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
_lowercase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def lowerCamelCase ( UpperCAmelCase__ : Any ) -> Tuple:
lowercase_ : Any = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = StableDiffusionLatentUpscalePipeline
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'''height''',
'''width''',
'''cross_attention_kwargs''',
'''negative_prompt_embeds''',
'''prompt_embeds''',
}
UpperCamelCase__ = PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase__ = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase__ = frozenset([])
UpperCamelCase__ = True
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : List[str] = 1
lowercase_ : Union[str, Any] = 4
lowercase_ : Optional[Any] = (16, 16)
lowercase_ : Tuple = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(lowercase_ )
return image
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
torch.manual_seed(0 )
lowercase_ : int = UNetaDConditionModel(
act_fn="""gelu""" , attention_head_dim=8 , norm_num_groups=lowercase_ , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
"""KDownBlock2D""",
"""KCrossAttnDownBlock2D""",
"""KCrossAttnDownBlock2D""",
"""KCrossAttnDownBlock2D""",
) , in_channels=8 , mid_block_type=lowercase_ , only_cross_attention=lowercase_ , out_channels=5 , resnet_time_scale_shift="""scale_shift""" , time_embedding_type="""fourier""" , timestep_post_act="""gelu""" , up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""") , )
lowercase_ : List[str] = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
lowercase_ : int = EulerDiscreteScheduler(prediction_type="""sample""" )
lowercase_ : List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""quick_gelu""" , projection_dim=512 , )
lowercase_ : int = CLIPTextModel(lowercase_ )
lowercase_ : Tuple = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase_ : List[str] = {
"""unet""": model.eval(),
"""vae""": vae.eval(),
"""scheduler""": scheduler,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : Optional[Any]=0 ):
if str(lowercase_ ).startswith("""mps""" ):
lowercase_ : Optional[int] = torch.manual_seed(lowercase_ )
else:
lowercase_ : str = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowercase_ : str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": self.dummy_image.cpu(),
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Optional[Any] = """cpu"""
lowercase_ : Any = self.get_dummy_components()
lowercase_ : str = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Optional[int] = self.get_dummy_inputs(lowercase_ )
lowercase_ : List[Any] = pipe(**lowercase_ ).images
lowercase_ : Optional[Any] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
lowercase_ : Any = np.array(
[0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55] )
lowercase_ : Optional[int] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
super().test_inference_batch_single_identical(expected_max_diff=7E-3 )
def SCREAMING_SNAKE_CASE_ ( self : str ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : str ):
super().test_save_load_local(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : int ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : int = [
"""DDIMScheduler""",
"""DDPMScheduler""",
"""PNDMScheduler""",
"""HeunDiscreteScheduler""",
"""EulerAncestralDiscreteScheduler""",
"""KDPM2DiscreteScheduler""",
"""KDPM2AncestralDiscreteScheduler""",
"""DPMSolverSDEScheduler""",
]
lowercase_ : Union[str, Any] = self.get_dummy_components()
lowercase_ : Dict = self.pipeline_class(**lowercase_ )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase_ : Dict = self.get_dummy_inputs(lowercase_ )
lowercase_ : Optional[int] = 2
lowercase_ : int = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
lowercase_ : Union[str, Any] = getattr(lowercase_ , scheduler_enum.name )
lowercase_ : List[str] = scheduler_cls.from_config(pipe.scheduler.config )
lowercase_ : Union[str, Any] = pipe(**lowercase_ )[0]
outputs.append(lowercase_ )
assert check_same_shape(lowercase_ )
@require_torch_gpu
@slow
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : int = torch.manual_seed(33 )
lowercase_ : Union[str, Any] = StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" , torch_dtype=torch.floataa )
pipe.to("""cuda""" )
lowercase_ : Any = StableDiffusionLatentUpscalePipeline.from_pretrained(
"""stabilityai/sd-x2-latent-upscaler""" , torch_dtype=torch.floataa )
upscaler.to("""cuda""" )
lowercase_ : List[Any] = """a photo of an astronaut high resolution, unreal engine, ultra realistic"""
lowercase_ : int = pipe(lowercase_ , generator=lowercase_ , output_type="""latent""" ).images
lowercase_ : Optional[int] = upscaler(
prompt=lowercase_ , image=lowercase_ , num_inference_steps=20 , guidance_scale=0 , generator=lowercase_ , output_type="""np""" , ).images[0]
lowercase_ : Any = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy""" )
assert np.abs((expected_image - image).mean() ) < 5E-2
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = torch.manual_seed(33 )
lowercase_ : str = StableDiffusionLatentUpscalePipeline.from_pretrained(
"""stabilityai/sd-x2-latent-upscaler""" , torch_dtype=torch.floataa )
upscaler.to("""cuda""" )
lowercase_ : Union[str, Any] = """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""
lowercase_ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png""" )
lowercase_ : Dict = upscaler(
prompt=lowercase_ , image=lowercase_ , num_inference_steps=20 , guidance_scale=0 , generator=lowercase_ , output_type="""np""" , ).images[0]
lowercase_ : Optional[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy""" )
assert np.abs((expected_image - image).max() ) < 5E-2
| 21 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''WhisperFeatureExtractor'''
UpperCamelCase__ = '''WhisperTokenizer'''
def __init__( self : Any , lowercase_ : Optional[Any] , lowercase_ : Dict ):
super().__init__(lowercase_ , lowercase_ )
lowercase_ : Any = self.feature_extractor
lowercase_ : Tuple = False
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : str=None , lowercase_ : Any=None , lowercase_ : Any=True ):
return self.tokenizer.get_decoder_prompt_ids(task=lowercase_ , language=lowercase_ , no_timestamps=lowercase_ )
def __call__( self : Union[str, Any] , *lowercase_ : str , **lowercase_ : Dict ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*lowercase_ , **lowercase_ )
lowercase_ : str = kwargs.pop("""audio""" , lowercase_ )
lowercase_ : Tuple = kwargs.pop("""sampling_rate""" , lowercase_ )
lowercase_ : Dict = kwargs.pop("""text""" , lowercase_ )
if len(lowercase_ ) > 0:
lowercase_ : Tuple = args[0]
lowercase_ : Any = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
lowercase_ : List[Any] = self.feature_extractor(lowercase_ , *lowercase_ , sampling_rate=lowercase_ , **lowercase_ )
if text is not None:
lowercase_ : Any = self.tokenizer(lowercase_ , **lowercase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowercase_ : List[Any] = encodings["""input_ids"""]
return inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , *lowercase_ : List[str] , **lowercase_ : List[Any] ):
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , *lowercase_ : int , **lowercase_ : List[Any] ):
return self.tokenizer.decode(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : str , lowercase_ : List[Any]="np" ):
return self.tokenizer.get_prompt_ids(lowercase_ , return_tensors=lowercase_ )
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Tuple , lowercase_ : Optional[int] , lowercase_ : Any=13 , lowercase_ : Optional[int]=7 , lowercase_ : List[Any]=True , lowercase_ : List[str]=True , lowercase_ : int=True , lowercase_ : List[Any]=True , lowercase_ : Any=99 , lowercase_ : List[str]=32 , lowercase_ : Union[str, Any]=5 , lowercase_ : Tuple=4 , lowercase_ : int=37 , lowercase_ : Any="gelu" , lowercase_ : int=0.1 , lowercase_ : Optional[int]=0.1 , lowercase_ : Tuple=512 , lowercase_ : Optional[Any]=16 , lowercase_ : Optional[int]=2 , lowercase_ : Any=0.02 , lowercase_ : str=False , lowercase_ : List[Any]=True , lowercase_ : Dict="None" , lowercase_ : Tuple=3 , lowercase_ : Any=4 , lowercase_ : Tuple=None , ):
lowercase_ : int = parent
lowercase_ : List[Any] = batch_size
lowercase_ : Optional[Any] = seq_length
lowercase_ : List[str] = is_training
lowercase_ : List[Any] = use_input_mask
lowercase_ : Optional[int] = use_token_type_ids
lowercase_ : Union[str, Any] = use_labels
lowercase_ : Dict = vocab_size
lowercase_ : List[str] = hidden_size
lowercase_ : Optional[int] = num_hidden_layers
lowercase_ : Dict = num_attention_heads
lowercase_ : Optional[Any] = intermediate_size
lowercase_ : Optional[int] = hidden_act
lowercase_ : Any = hidden_dropout_prob
lowercase_ : Any = attention_probs_dropout_prob
lowercase_ : Any = max_position_embeddings
lowercase_ : Dict = type_vocab_size
lowercase_ : List[str] = type_sequence_label_size
lowercase_ : List[Any] = initializer_range
lowercase_ : Union[str, Any] = num_labels
lowercase_ : Any = num_choices
lowercase_ : Optional[int] = relative_attention
lowercase_ : Tuple = position_biased_input
lowercase_ : List[str] = pos_att_type
lowercase_ : Any = scope
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : Optional[Any] = None
if self.use_input_mask:
lowercase_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
lowercase_ : List[str] = None
if self.use_token_type_ids:
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ : str = None
lowercase_ : List[str] = None
lowercase_ : List[Any] = None
if self.use_labels:
lowercase_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : str = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self : str ):
return DebertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[str] = self.get_config()
lowercase_ : int = 300
return config
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : Dict , lowercase_ : Tuple ):
lowercase_ : str = DebertaModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : str = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ )[0]
lowercase_ : Any = model(lowercase_ , token_type_ids=lowercase_ )[0]
lowercase_ : Any = model(lowercase_ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Dict , lowercase_ : Any , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : str ):
lowercase_ : Any = DebertaForMaskedLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Tuple = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Dict , lowercase_ : Any , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : int ):
lowercase_ : List[Any] = self.num_labels
lowercase_ : Optional[Any] = DebertaForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Optional[Any] = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : str ):
lowercase_ : List[Any] = self.num_labels
lowercase_ : List[Any] = DebertaForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Union[str, Any] = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Dict ):
lowercase_ : Any = DebertaForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : int = model(
lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
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 SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : List[Any] = config_and_inputs
lowercase_ : str = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{
'''feature-extraction''': DebertaModel,
'''fill-mask''': DebertaForMaskedLM,
'''question-answering''': DebertaForQuestionAnswering,
'''text-classification''': DebertaForSequenceClassification,
'''token-classification''': DebertaForTokenClassification,
'''zero-shot''': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Optional[int] = DebertaModelTester(self )
lowercase_ : Any = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Any = DebertaModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase):
@unittest.skip(reason="""Model not available yet""" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
pass
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Optional[int] = DebertaModel.from_pretrained("""microsoft/deberta-base""" )
lowercase_ : Union[str, Any] = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
lowercase_ : int = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase_ : int = model(lowercase_ , attention_mask=lowercase_ )[0]
# compare the actual values for a slice.
lowercase_ : str = torch.tensor(
[[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowercase_ , atol=1E-4 ) , f'''{output[:, 1:4, 1:4]}''' )
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | '''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | 1 |
'''simple docstring'''
from __future__ import annotations
import requests
def lowerCamelCase ( UpperCAmelCase__ : str ) -> dict:
lowercase_ : Any = F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(UpperCAmelCase__ ).json()
def lowerCamelCase ( UpperCAmelCase__ : int = 10 ) -> list[dict]:
lowercase_ : Optional[int] = """https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
lowercase_ : str = requests.get(UpperCAmelCase__ ).json()[:max_stories]
return [get_hackernews_story(UpperCAmelCase__ ) for story_id in story_ids]
def lowerCamelCase ( UpperCAmelCase__ : int = 10 ) -> str:
lowercase_ : List[Any] = hackernews_top_stories(UpperCAmelCase__ )
return "\n".join("""* [{title}]({url})""".format(**UpperCAmelCase__ ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 21 | '''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : List[Any] ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None ):
lowercase_ : Optional[Any] = {}
lowercase_ : Tuple = {}
if prompt is not None:
lowercase_ : Tuple = prompt
if generate_kwargs is not None:
lowercase_ : List[str] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase_ : List[Any] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase_ : str = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : Optional[int] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : Tuple=None ):
lowercase_ : List[Any] = load_image(lowercase_ )
if prompt is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowercase_ )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase_ : List[Any] = self.model.config.model_type
if model_type == "git":
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : Union[str, Any] = self.tokenizer(text=lowercase_ , add_special_tokens=lowercase_ ).input_ids
lowercase_ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase_ : List[Any] = torch.tensor(lowercase_ ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase_ : Union[str, Any] = self.image_processor(images=lowercase_ , header_text=lowercase_ , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : List[str] = self.tokenizer(lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase_ : str = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict , lowercase_ : Optional[Any]=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , lowercase_ )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase_ : Any = None
if generate_kwargs is None:
lowercase_ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase_ : Dict = model_inputs.pop(self.model.main_input_name )
lowercase_ : Any = self.model.generate(lowercase_ , **lowercase_ , **lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] ):
lowercase_ : List[str] = []
for output_ids in model_outputs:
lowercase_ : Union[str, Any] = {
"""generated_text""": self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , )
}
records.append(lowercase_ )
return records
| 21 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowercase : Any = logging.get_logger(__name__)
_lowercase : List[Any] = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
# See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''deformable_detr'''
UpperCamelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : Any , lowercase_ : Tuple=True , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=3 , lowercase_ : Any=300 , lowercase_ : Tuple=1024 , lowercase_ : Optional[int]=6 , lowercase_ : List[str]=1024 , lowercase_ : Dict=8 , lowercase_ : Dict=6 , lowercase_ : Tuple=1024 , lowercase_ : Optional[int]=8 , lowercase_ : Dict=0.0 , lowercase_ : str=True , lowercase_ : Union[str, Any]="relu" , lowercase_ : List[str]=256 , lowercase_ : List[str]=0.1 , lowercase_ : Any=0.0 , lowercase_ : Any=0.0 , lowercase_ : Optional[Any]=0.02 , lowercase_ : Tuple=1.0 , lowercase_ : Dict=True , lowercase_ : Union[str, Any]=False , lowercase_ : Tuple="sine" , lowercase_ : Dict="resnet50" , lowercase_ : Union[str, Any]=True , lowercase_ : Union[str, Any]=False , lowercase_ : List[str]=4 , lowercase_ : List[Any]=4 , lowercase_ : Union[str, Any]=4 , lowercase_ : Union[str, Any]=False , lowercase_ : List[Any]=300 , lowercase_ : Any=False , lowercase_ : Any=1 , lowercase_ : Union[str, Any]=5 , lowercase_ : List[Any]=2 , lowercase_ : List[str]=1 , lowercase_ : List[Any]=1 , lowercase_ : Optional[int]=5 , lowercase_ : List[str]=2 , lowercase_ : Tuple=0.1 , lowercase_ : Optional[Any]=0.25 , lowercase_ : Tuple=False , **lowercase_ : Optional[int] , ):
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
lowercase_ : Dict = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowercase_ , lowercase_ ):
lowercase_ : str = backbone_config.get("""model_type""" )
lowercase_ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowercase_ : Union[str, Any] = config_class.from_dict(lowercase_ )
lowercase_ : Tuple = use_timm_backbone
lowercase_ : Dict = backbone_config
lowercase_ : str = num_channels
lowercase_ : List[Any] = num_queries
lowercase_ : str = max_position_embeddings
lowercase_ : Union[str, Any] = d_model
lowercase_ : List[Any] = encoder_ffn_dim
lowercase_ : Dict = encoder_layers
lowercase_ : Tuple = encoder_attention_heads
lowercase_ : Any = decoder_ffn_dim
lowercase_ : int = decoder_layers
lowercase_ : Optional[Any] = decoder_attention_heads
lowercase_ : List[Any] = dropout
lowercase_ : Dict = attention_dropout
lowercase_ : Optional[int] = activation_dropout
lowercase_ : int = activation_function
lowercase_ : Union[str, Any] = init_std
lowercase_ : Optional[Any] = init_xavier_std
lowercase_ : Union[str, Any] = encoder_layerdrop
lowercase_ : Optional[int] = auxiliary_loss
lowercase_ : Optional[Any] = position_embedding_type
lowercase_ : str = backbone
lowercase_ : Any = use_pretrained_backbone
lowercase_ : Optional[int] = dilation
# deformable attributes
lowercase_ : List[Any] = num_feature_levels
lowercase_ : Tuple = encoder_n_points
lowercase_ : Optional[Any] = decoder_n_points
lowercase_ : Optional[int] = two_stage
lowercase_ : Union[str, Any] = two_stage_num_proposals
lowercase_ : str = with_box_refine
if two_stage is True and with_box_refine is False:
raise ValueError("""If two_stage is True, with_box_refine must be True.""" )
# Hungarian matcher
lowercase_ : str = class_cost
lowercase_ : Optional[Any] = bbox_cost
lowercase_ : str = giou_cost
# Loss coefficients
lowercase_ : str = mask_loss_coefficient
lowercase_ : Union[str, Any] = dice_loss_coefficient
lowercase_ : Optional[Any] = bbox_loss_coefficient
lowercase_ : Union[str, Any] = giou_loss_coefficient
lowercase_ : str = eos_coefficient
lowercase_ : List[Any] = focal_alpha
lowercase_ : Union[str, Any] = disable_custom_kernels
super().__init__(is_encoder_decoder=lowercase_ , **lowercase_ )
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE_ ( self : Any ):
return self.d_model
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[Any] = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowercase_ : str = self.backbone_config.to_dict()
lowercase_ : Dict = self.__class__.model_type
return output
| 21 | '''simple docstring'''
class __magic_name__ :
def __init__( self : int , lowercase_ : list ):
lowercase_ : Dict = set_counts
lowercase_ : List[Any] = max(lowercase_ )
lowercase_ : str = len(lowercase_ )
lowercase_ : str = [1] * num_sets
lowercase_ : Dict = list(range(lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : int ):
lowercase_ : List[Any] = self.get_parent(lowercase_ )
lowercase_ : Union[str, Any] = self.get_parent(lowercase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase_ : List[str] = 0
lowercase_ : Optional[int] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase_ : int = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase_ : int = 0
lowercase_ : List[Any] = src_parent
lowercase_ : List[Any] = self.set_counts[src_parent]
lowercase_ : Tuple = max(self.max_set , lowercase_ )
return True
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
lowercase_ : int = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 21 | 1 |
'''simple docstring'''
import re
def lowerCamelCase ( UpperCAmelCase__ : str ) -> bool:
lowercase_ : Any = re.compile(
R"""^(?:0|94|\+94|0{2}94)""" R"""7(0|1|2|4|5|6|7|8)""" R"""(-| |)""" R"""\d{7}$""" )
return bool(re.search(UpperCAmelCase__ , UpperCAmelCase__ ) )
if __name__ == "__main__":
_lowercase : Optional[int] = "0094702343221"
print(is_sri_lankan_phone_number(phone))
| 21 | '''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def lowerCamelCase ( UpperCAmelCase__ : int = 8 , UpperCAmelCase__ : int | None = None ) -> str:
lowercase_ : Tuple = np.random.default_rng(seed=UpperCAmelCase__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
lowercase_ : Dict = 6 * key_len
# Measurement basis for Alice's qubits.
lowercase_ : int = rng.integers(2 , size=UpperCAmelCase__ )
# The set of states Alice will prepare.
lowercase_ : Dict = rng.integers(2 , size=UpperCAmelCase__ )
# Measurement basis for Bob's qubits.
lowercase_ : Tuple = rng.integers(2 , size=UpperCAmelCase__ )
# Quantum Circuit to simulate BB84
lowercase_ : int = qiskit.QuantumCircuit(UpperCAmelCase__ , name="""BB84""" )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(UpperCAmelCase__ ):
if alice_state[index] == 1:
bbaa_circ.x(UpperCAmelCase__ )
if alice_basis[index] == 1:
bbaa_circ.h(UpperCAmelCase__ )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(UpperCAmelCase__ ):
if bob_basis[index] == 1:
bbaa_circ.h(UpperCAmelCase__ )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
lowercase_ : int = qiskit.Aer.get_backend("""aer_simulator""" )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
lowercase_ : List[str] = qiskit.execute(UpperCAmelCase__ , UpperCAmelCase__ , shots=1 , seed_simulator=UpperCAmelCase__ )
# Returns the result of measurement.
lowercase_ : Any = job.result().get_counts(UpperCAmelCase__ ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
lowercase_ : Union[str, Any] = """""".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
lowercase_ : Union[str, Any] = gen_key[:key_len] if len(UpperCAmelCase__ ) >= key_len else gen_key.ljust(UpperCAmelCase__ , """0""" )
return key
if __name__ == "__main__":
print(f"""The generated key is : {bbaa(8, seed=0)}""")
from doctest import testmod
testmod()
| 21 | '''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 lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , 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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = 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 __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = 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 __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# 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]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module):
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 0.0
UpperCamelCase__ = 1
UpperCamelCase__ = 1
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = jnp.floataa
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : int = []
lowercase_ : List[Any] = []
for i in range(self.num_layers ):
lowercase_ : str = self.in_channels if i == 0 else self.out_channels
lowercase_ : int = FlaxResnetBlockaD(
in_channels=lowercase_ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowercase_ )
lowercase_ : Optional[int] = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowercase_ )
lowercase_ : Dict = resnets
lowercase_ : List[str] = attentions
if self.add_downsample:
lowercase_ : Optional[int] = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : int , lowercase_ : str , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any]=True ):
lowercase_ : str = ()
for resnet, attn in zip(self.resnets , self.attentions ):
lowercase_ : Any = resnet(lowercase_ , lowercase_ , deterministic=lowercase_ )
lowercase_ : Optional[int] = attn(lowercase_ , lowercase_ , deterministic=lowercase_ )
output_states += (hidden_states,)
if self.add_downsample:
lowercase_ : List[str] = self.downsamplers_a(lowercase_ )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module):
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 0.0
UpperCamelCase__ = 1
UpperCamelCase__ = True
UpperCamelCase__ = jnp.floataa
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Tuple = []
for i in range(self.num_layers ):
lowercase_ : Tuple = self.in_channels if i == 0 else self.out_channels
lowercase_ : Dict = FlaxResnetBlockaD(
in_channels=lowercase_ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowercase_ )
lowercase_ : Dict = resnets
if self.add_downsample:
lowercase_ : Optional[int] = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , lowercase_ : int , lowercase_ : Dict , lowercase_ : Tuple=True ):
lowercase_ : Optional[Any] = ()
for resnet in self.resnets:
lowercase_ : Union[str, Any] = resnet(lowercase_ , lowercase_ , deterministic=lowercase_ )
output_states += (hidden_states,)
if self.add_downsample:
lowercase_ : Tuple = self.downsamplers_a(lowercase_ )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module):
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 0.0
UpperCamelCase__ = 1
UpperCamelCase__ = 1
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = jnp.floataa
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[Any] = []
lowercase_ : List[Any] = []
for i in range(self.num_layers ):
lowercase_ : Tuple = self.in_channels if (i == self.num_layers - 1) else self.out_channels
lowercase_ : List[Any] = self.prev_output_channel if i == 0 else self.out_channels
lowercase_ : str = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowercase_ )
lowercase_ : Any = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowercase_ )
lowercase_ : int = resnets
lowercase_ : Tuple = attentions
if self.add_upsample:
lowercase_ : Optional[int] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , lowercase_ : str , lowercase_ : int , lowercase_ : int , lowercase_ : Tuple , lowercase_ : int=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
lowercase_ : str = res_hidden_states_tuple[-1]
lowercase_ : Optional[int] = res_hidden_states_tuple[:-1]
lowercase_ : Optional[int] = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
lowercase_ : Optional[int] = resnet(lowercase_ , lowercase_ , deterministic=lowercase_ )
lowercase_ : List[str] = attn(lowercase_ , lowercase_ , deterministic=lowercase_ )
if self.add_upsample:
lowercase_ : int = self.upsamplers_a(lowercase_ )
return hidden_states
class __magic_name__ ( nn.Module):
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 0.0
UpperCamelCase__ = 1
UpperCamelCase__ = True
UpperCamelCase__ = jnp.floataa
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Dict = []
for i in range(self.num_layers ):
lowercase_ : Optional[int] = self.in_channels if (i == self.num_layers - 1) else self.out_channels
lowercase_ : int = self.prev_output_channel if i == 0 else self.out_channels
lowercase_ : Any = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowercase_ )
lowercase_ : int = resnets
if self.add_upsample:
lowercase_ : Union[str, Any] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]=True ):
for resnet in self.resnets:
# pop res hidden states
lowercase_ : int = res_hidden_states_tuple[-1]
lowercase_ : List[str] = res_hidden_states_tuple[:-1]
lowercase_ : int = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
lowercase_ : Union[str, Any] = resnet(lowercase_ , lowercase_ , deterministic=lowercase_ )
if self.add_upsample:
lowercase_ : Dict = self.upsamplers_a(lowercase_ )
return hidden_states
class __magic_name__ ( nn.Module):
UpperCamelCase__ = 42
UpperCamelCase__ = 0.0
UpperCamelCase__ = 1
UpperCamelCase__ = 1
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = jnp.floataa
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
# there is always at least one resnet
lowercase_ : List[str] = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
lowercase_ : List[Any] = []
for _ in range(self.num_layers ):
lowercase_ : str = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowercase_ )
lowercase_ : Union[str, Any] = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowercase_ )
lowercase_ : Optional[Any] = resnets
lowercase_ : Optional[int] = attentions
def __call__( self : Optional[Any] , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : str=True ):
lowercase_ : Optional[int] = self.resnets[0](lowercase_ , lowercase_ )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
lowercase_ : List[Any] = attn(lowercase_ , lowercase_ , deterministic=lowercase_ )
lowercase_ : str = resnet(lowercase_ , lowercase_ , deterministic=lowercase_ )
return hidden_states
| 21 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int=7 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=18 , lowercase_ : List[Any]=30 , lowercase_ : int=400 , lowercase_ : Dict=True , lowercase_ : List[Any]=None , lowercase_ : Dict=True , ):
lowercase_ : Tuple = size if size is not None else {"""height""": 18, """width""": 18}
lowercase_ : List[str] = parent
lowercase_ : Any = batch_size
lowercase_ : Optional[Any] = num_channels
lowercase_ : Tuple = image_size
lowercase_ : Optional[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[Any] = size
lowercase_ : Union[str, Any] = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """clusters""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Union[str, Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , obj[key] ) )
else:
self.assertEqual(obj[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , """image_processor.json""" )
image_processor_first.to_json_file(lowercase_ )
lowercase_ : Optional[Any] = self.image_processing_class.from_json_file(lowercase_ ).to_dict()
lowercase_ : Any = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowercase_ )
lowercase_ : Any = self.image_processing_class.from_pretrained(lowercase_ ).to_dict()
lowercase_ : List[str] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
pass
def lowerCamelCase ( ) -> Any:
lowercase_ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
lowercase_ : Any = Image.open(dataset[4]["""file"""] )
lowercase_ : Dict = Image.open(dataset[5]["""file"""] )
lowercase_ : int = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
lowercase_ : Optional[int] = prepare_images()
# test non-batched
lowercase_ : str = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
lowercase_ : Tuple = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowercase_ )
# test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
lowercase_ : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowercase_ )
| 21 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_lowercase : str = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default='''cifar10''', metadata={'''help''': '''Name of a dataset from the datasets package'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''The column name of the images in the files.'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''A folder containing the training data.'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''A folder containing the validation data.'''})
UpperCamelCase__ = field(
default=0.15, metadata={'''help''': '''Percent to split off of train for validation.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
}, )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : int = {}
if self.train_dir is not None:
lowercase_ : int = self.train_dir
if self.validation_dir is not None:
lowercase_ : Optional[int] = self.validation_dir
lowercase_ : Optional[Any] = data_files if data_files else None
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''})
UpperCamelCase__ = field(
default='''main''', metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}, )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Name or path of preprocessor config.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
}, )
UpperCamelCase__ = field(
default=0.75, metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''})
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = field(
default=1e-3, metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''})
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Optional[Any]:
lowercase_ : List[Any] = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowerCamelCase ( ) -> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase_ : Optional[int] = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase_ , lowercase_ , lowercase_ : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase_ , lowercase_ , lowercase_ : Tuple = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , UpperCAmelCase__ , UpperCAmelCase__ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase_ : str = training_args.get_process_log_level()
logger.setLevel(UpperCAmelCase__ )
transformers.utils.logging.set_verbosity(UpperCAmelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowercase_ : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase_ : int = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
lowercase_ : Union[str, Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowercase_ : List[str] = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , UpperCAmelCase__ ) and data_args.train_val_split > 0.0:
lowercase_ : int = ds["""train"""].train_test_split(data_args.train_val_split )
lowercase_ : Union[str, Any] = split["""train"""]
lowercase_ : Tuple = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase_ : Any = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
lowercase_ : Any = ViTMAEConfig.from_pretrained(model_args.config_name , **UpperCAmelCase__ )
elif model_args.model_name_or_path:
lowercase_ : List[str] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **UpperCAmelCase__ )
else:
lowercase_ : Dict = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
lowercase_ : Union[str, Any] = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **UpperCAmelCase__ )
elif model_args.model_name_or_path:
lowercase_ : str = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **UpperCAmelCase__ )
else:
lowercase_ : List[str] = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
lowercase_ : str = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
lowercase_ : Optional[int] = ViTMAEForPreTraining(UpperCAmelCase__ )
if training_args.do_train:
lowercase_ : Union[str, Any] = ds["""train"""].column_names
else:
lowercase_ : str = ds["""validation"""].column_names
if data_args.image_column_name is not None:
lowercase_ : Optional[int] = data_args.image_column_name
elif "image" in column_names:
lowercase_ : Tuple = """image"""
elif "img" in column_names:
lowercase_ : int = """img"""
else:
lowercase_ : List[Any] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
lowercase_ : Union[str, Any] = image_processor.size["""shortest_edge"""]
else:
lowercase_ : str = (image_processor.size["""height"""], image_processor.size["""width"""])
lowercase_ : List[str] = Compose(
[
Lambda(lambda UpperCAmelCase__ : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(UpperCAmelCase__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(UpperCAmelCase__ : List[str] ):
lowercase_ : int = [transforms(UpperCAmelCase__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
lowercase_ : Optional[Any] = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(UpperCAmelCase__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
lowercase_ : Union[str, Any] = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(UpperCAmelCase__ )
# Compute absolute learning rate
lowercase_ : Union[str, Any] = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
lowercase_ : Union[str, Any] = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
lowercase_ : int = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
lowercase_ : Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
lowercase_ : Optional[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase_ : List[Any] = last_checkpoint
lowercase_ : str = trainer.train(resume_from_checkpoint=UpperCAmelCase__ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase_ : int = trainer.evaluate()
trainer.log_metrics("""eval""" , UpperCAmelCase__ )
trainer.save_metrics("""eval""" , UpperCAmelCase__ )
# Write model card and (optionally) push to hub
lowercase_ : Union[str, Any] = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCAmelCase__ )
else:
trainer.create_model_card(**UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 21 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 21 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 21 | 1 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 21 | '''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
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(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 21 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : List[str] = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
"tokenization_electra": ["ElectraTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Tuple = ["ElectraTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[int] = [
"ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"ElectraForCausalLM",
"ElectraForMaskedLM",
"ElectraForMultipleChoice",
"ElectraForPreTraining",
"ElectraForQuestionAnswering",
"ElectraForSequenceClassification",
"ElectraForTokenClassification",
"ElectraModel",
"ElectraPreTrainedModel",
"load_tf_weights_in_electra",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFElectraForMaskedLM",
"TFElectraForMultipleChoice",
"TFElectraForPreTraining",
"TFElectraForQuestionAnswering",
"TFElectraForSequenceClassification",
"TFElectraForTokenClassification",
"TFElectraModel",
"TFElectraPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[int] = [
"FlaxElectraForCausalLM",
"FlaxElectraForMaskedLM",
"FlaxElectraForMultipleChoice",
"FlaxElectraForPreTraining",
"FlaxElectraForQuestionAnswering",
"FlaxElectraForSequenceClassification",
"FlaxElectraForTokenClassification",
"FlaxElectraModel",
"FlaxElectraPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
_lowercase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 1 |
'''simple docstring'''
_lowercase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
lowercase_ : int = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
lowercase_ : str = _calculate(days - 1 , UpperCAmelCase__ , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
lowercase_ : List[Any] = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
lowercase_ : int = _calculate(days - 1 , UpperCAmelCase__ , 0 )
lowercase_ : int = state_late + state_absent + state_ontime
lowercase_ : Optional[int] = prizestrings
return prizestrings
def lowerCamelCase ( UpperCAmelCase__ : int = 30 ) -> int:
return _calculate(UpperCAmelCase__ , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 21 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | 1 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
_lowercase : Any = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowercase : Tuple = get_tests_dir("fixtures/vocab.json")
_lowercase : Optional[int] = get_tests_dir("fixtures")
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Union[str, Any] = 0
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : int = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : int = WavaVecaConfig()
lowercase_ : str = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
# save in new folder
model_config.save_pretrained(lowercase_ )
processor.save_pretrained(lowercase_ )
lowercase_ : Tuple = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int ):
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(lowercase_ , os.path.join(lowercase_ , lowercase_ ) )
copyfile(lowercase_ , os.path.join(lowercase_ , """vocab.json""" ) )
lowercase_ : str = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : List[str] = WavaVecaFeatureExtractor()
lowercase_ : List[Any] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase_ : Optional[Any] = WavaVecaProcessor(lowercase_ , lowercase_ )
# save in new folder
processor.save_pretrained(lowercase_ )
# drop `processor_class` in tokenizer
with open(os.path.join(lowercase_ , lowercase_ ) , """r""" ) as f:
lowercase_ : List[Any] = json.load(lowercase_ )
config_dict.pop("""processor_class""" )
with open(os.path.join(lowercase_ , lowercase_ ) , """w""" ) as f:
f.write(json.dumps(lowercase_ ) )
lowercase_ : Optional[int] = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Tuple = WavaVecaFeatureExtractor()
lowercase_ : List[Any] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase_ : Tuple = WavaVecaProcessor(lowercase_ , lowercase_ )
# save in new folder
processor.save_pretrained(lowercase_ )
# drop `processor_class` in feature extractor
with open(os.path.join(lowercase_ , lowercase_ ) , """r""" ) as f:
lowercase_ : Optional[Any] = json.load(lowercase_ )
config_dict.pop("""processor_class""" )
with open(os.path.join(lowercase_ , lowercase_ ) , """w""" ) as f:
f.write(json.dumps(lowercase_ ) )
lowercase_ : Any = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Any = WavaVecaConfig(processor_class="""Wav2Vec2Processor""" )
model_config.save_pretrained(lowercase_ )
# copy relevant files
copyfile(lowercase_ , os.path.join(lowercase_ , """vocab.json""" ) )
# create emtpy sample processor
with open(os.path.join(lowercase_ , lowercase_ ) , """w""" ) as f:
f.write("""{}""" )
lowercase_ : Any = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowercase_ ):
lowercase_ : List[Any] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowercase_ ):
lowercase_ : Optional[Any] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ )
lowercase_ : Optional[int] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
lowercase_ : Optional[Any] = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
lowercase_ : Optional[int] = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
# Test we can also load the slow version
lowercase_ : Optional[Any] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ , use_fast=lowercase_ )
lowercase_ : List[Any] = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , """NewTokenizer""" )
else:
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
try:
AutoConfig.register("""custom""" , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
AutoTokenizer.register(lowercase_ , slow_tokenizer_class=lowercase_ )
AutoProcessor.register(lowercase_ , lowercase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase_ ):
AutoProcessor.register(lowercase_ , lowercase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase_ : str = CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Optional[int] = os.path.join(lowercase_ , """vocab.txt""" )
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase_ : Any = CustomTokenizer(lowercase_ )
lowercase_ : Tuple = CustomProcessor(lowercase_ , lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(lowercase_ )
lowercase_ : Any = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = False
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = False
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''AutoFeatureExtractor'''
UpperCamelCase__ = '''AutoTokenizer'''
UpperCamelCase__ = False
try:
AutoConfig.register("""custom""" , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
AutoTokenizer.register(lowercase_ , slow_tokenizer_class=lowercase_ )
AutoProcessor.register(lowercase_ , lowercase_ )
# If remote code is not set, the default is to use local classes.
lowercase_ : Optional[Any] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
lowercase_ : List[str] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
lowercase_ : int = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Tuple = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
self.assertEqual(processor.__class__.__name__ , """BertTokenizerFast""" )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext""" )
self.assertEqual(processor.__class__.__name__ , """ConvNextImageProcessor""" )
@is_staging_test
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict ):
try:
delete_repo(token=cls._token , repo_id="""test-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-processor""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : int = WavaVecaProcessor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowercase_ , """test-processor""" ) , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : str = WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor''' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(new_processor.feature_extractor , lowercase_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Any = WavaVecaProcessor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowercase_ , """test-processor-org""" ) , push_to_hub=lowercase_ , use_auth_token=self._token , organization="""valid_org""" , )
lowercase_ : int = WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(new_processor.feature_extractor , lowercase_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
lowercase_ : Dict = CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Optional[int] = os.path.join(lowercase_ , """vocab.txt""" )
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase_ : int = CustomTokenizer(lowercase_ )
lowercase_ : Any = CustomProcessor(lowercase_ , lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(f'''{USER}/test-dynamic-processor''' , token=self._token )
lowercase_ : Optional[int] = Repository(lowercase_ , clone_from=f'''{USER}/test-dynamic-processor''' , token=self._token )
processor.save_pretrained(lowercase_ )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(lowercase_ , """tokenizer_config.json""" ) ) as f:
lowercase_ : Dict = json.load(lowercase_ )
self.assertDictEqual(
tokenizer_config["""auto_map"""] , {
"""AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , """custom_feature_extraction.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , """custom_tokenization.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , """custom_processing.py""" ) ) )
repo.push_to_hub()
lowercase_ : Optional[int] = AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor''' , trust_remote_code=lowercase_ )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , """CustomProcessor""" )
| 21 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class __magic_name__ :
def __init__( self : str , lowercase_ : List[Any]=None , lowercase_ : Tuple=None ):
# Input as list
lowercase_ : Dict = list(poly_a or [0] )[:]
lowercase_ : List[str] = list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
lowercase_ : Dict = len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
lowercase_ : str = len(self.polyB )
# Add 0 to make lengths equal a power of 2
lowercase_ : str = 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
lowercase_ : str = complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
lowercase_ : int = self.__multiply()
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Optional[int] ):
lowercase_ : Tuple = [[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
# Corner case
if len(lowercase_ ) <= 1:
return dft[0]
#
lowercase_ : Union[str, Any] = self.c_max_length // 2
while next_ncol > 0:
lowercase_ : Tuple = [[] for i in range(lowercase_ )]
lowercase_ : Optional[int] = self.root**next_ncol
# First half of next step
lowercase_ : Tuple = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(lowercase_ ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
lowercase_ : Optional[Any] = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(lowercase_ ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
lowercase_ : Any = new_dft
lowercase_ : Union[str, Any] = next_ncol // 2
return dft[0]
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[Any] = self.__dft("""A""" )
lowercase_ : str = self.__dft("""B""" )
lowercase_ : Tuple = [[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
lowercase_ : List[str] = 2
while next_ncol <= self.c_max_length:
lowercase_ : Dict = [[] for i in range(lowercase_ )]
lowercase_ : List[Any] = self.root ** (next_ncol // 2)
lowercase_ : Tuple = 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
lowercase_ : List[str] = new_inverse_c
next_ncol *= 2
# Unpack
lowercase_ : str = [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[Any] ):
lowercase_ : Tuple = """A = """ + """ + """.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
lowercase_ : Tuple = """B = """ + """ + """.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
lowercase_ : Optional[Any] = """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()
| 21 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
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
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
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
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = 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))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# 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.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > 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}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# 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
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 1 |
'''simple docstring'''
import os
from collections.abc import Iterator
def lowerCamelCase ( UpperCAmelCase__ : str = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(UpperCAmelCase__ ):
lowercase_ : Dict = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(UpperCAmelCase__ )[1] in (".py", ".ipynb"):
yield os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ).lstrip("""./""" )
def lowerCamelCase ( UpperCAmelCase__ : str ) -> List[str]:
return F'''{i * ' '}*''' if i else "\n##"
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str ) -> str:
lowercase_ : List[Any] = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(UpperCAmelCase__ ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(UpperCAmelCase__ )} {new_part.replace('_' , ' ' ).title()}''' )
return new_path
def lowerCamelCase ( UpperCAmelCase__ : str = "." ) -> None:
lowercase_ : List[str] = """"""
for filepath in sorted(good_file_paths(UpperCAmelCase__ ) ):
lowercase_ , lowercase_ : Optional[Any] = os.path.split(UpperCAmelCase__ )
if filepath != old_path:
lowercase_ : Tuple = print_path(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = (filepath.count(os.sep ) + 1) if filepath else 0
lowercase_ : int = F'''{filepath}/{filename}'''.replace(""" """ , """%20""" )
lowercase_ : List[Any] = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'''{md_prefix(UpperCAmelCase__ )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(".")
| 21 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000 ) -> int:
lowercase_ : List[Any] = -1
lowercase_ : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowercase_ : Optional[int] = (n * n - 2 * a * n) // (2 * n - 2 * a)
lowercase_ : List[Any] = n - a - b
if c * c == (a * a + b * b):
lowercase_ : int = a * b * c
if candidate >= product:
lowercase_ : Dict = candidate
return product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 1 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCamelCase ( UpperCAmelCase__ : ndarray ) -> float:
return np.dot(UpperCAmelCase__ , UpperCAmelCase__ )
class __magic_name__ :
def __init__( self : int , *,
lowercase_ : float = np.inf , lowercase_ : str = "linear" , lowercase_ : float = 0.0 , ):
lowercase_ : Any = regularization
lowercase_ : List[str] = gamma
if kernel == "linear":
lowercase_ : Any = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError("""rbf kernel requires gamma""" )
if not isinstance(self.gamma , (float, int) ):
raise ValueError("""gamma must be float or int""" )
if not self.gamma > 0:
raise ValueError("""gamma must be > 0""" )
lowercase_ : Optional[Any] = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
lowercase_ : Any = f'''Unknown kernel: {kernel}'''
raise ValueError(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : ndarray , lowercase_ : ndarray ):
return np.dot(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : ndarray , lowercase_ : ndarray ):
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : list[ndarray] , lowercase_ : ndarray ):
lowercase_ : Union[str, Any] = observations
lowercase_ : List[Any] = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((lowercase_) , ) : List[str] = np.shape(lowercase_ )
def to_minimize(lowercase_ : ndarray ) -> float:
lowercase_ : List[str] = 0
((lowercase_) , ) : Tuple = np.shape(lowercase_ )
for i in range(lowercase_ ):
for j in range(lowercase_ ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(lowercase_ )
lowercase_ : Optional[Any] = LinearConstraint(lowercase_ , 0 , 0 )
lowercase_ : Dict = Bounds(0 , self.regularization )
lowercase_ : Tuple = minimize(
lowercase_ , np.ones(lowercase_ ) , bounds=lowercase_ , constraints=[ly_contraint] ).x
lowercase_ : int = l_star
# calculating mean offset of separation plane to points
lowercase_ : List[str] = 0
for i in range(lowercase_ ):
for j in range(lowercase_ ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
lowercase_ : Tuple = s / n
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : ndarray ):
lowercase_ : List[str] = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , lowercase_ )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : list ) -> list:
def merge(UpperCAmelCase__ : list , UpperCAmelCase__ : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(UpperCAmelCase__ ) <= 1:
return collection
lowercase_ : str = len(UpperCAmelCase__ ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : Any = input("Enter numbers separated by a comma:\n").strip()
_lowercase : List[Any] = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| 21 | '''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = (UniPCMultistepScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 25),)
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , **lowercase_ : Union[str, Any] ):
lowercase_ : Any = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.00_01,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
"""solver_type""": """bh2""",
}
config.update(**lowercase_ )
return config
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any]=0 , **lowercase_ : List[Any] ):
lowercase_ : Dict = dict(self.forward_default_kwargs )
lowercase_ : str = kwargs.pop("""num_inference_steps""" , lowercase_ )
lowercase_ : List[str] = self.dummy_sample
lowercase_ : List[Any] = 0.1 * sample
lowercase_ : List[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowercase_ : Optional[Any] = self.get_scheduler_config(**lowercase_ )
lowercase_ : str = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
lowercase_ : Any = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
lowercase_ : str = scheduler_class.from_pretrained(lowercase_ )
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
lowercase_ : Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase_ , lowercase_ : str = sample, sample
for t in range(lowercase_ , time_step + scheduler.config.solver_order + 1 ):
lowercase_ : Any = scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
lowercase_ : List[str] = new_scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : Optional[int]=0 , **lowercase_ : int ):
lowercase_ : Optional[int] = dict(self.forward_default_kwargs )
lowercase_ : Optional[int] = kwargs.pop("""num_inference_steps""" , lowercase_ )
lowercase_ : List[str] = self.dummy_sample
lowercase_ : Tuple = 0.1 * sample
lowercase_ : Union[str, Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowercase_ : List[Any] = self.get_scheduler_config()
lowercase_ : int = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals (must be after setting timesteps)
lowercase_ : Tuple = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
lowercase_ : Tuple = scheduler_class.from_pretrained(lowercase_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residual (must be after setting timesteps)
lowercase_ : Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase_ : Optional[int] = scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
lowercase_ : Optional[Any] = new_scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any]=None , **lowercase_ : Any ):
if scheduler is None:
lowercase_ : str = self.scheduler_classes[0]
lowercase_ : str = self.get_scheduler_config(**lowercase_ )
lowercase_ : List[Any] = scheduler_class(**lowercase_ )
lowercase_ : Tuple = self.scheduler_classes[0]
lowercase_ : Tuple = self.get_scheduler_config(**lowercase_ )
lowercase_ : Optional[int] = scheduler_class(**lowercase_ )
lowercase_ : Tuple = 10
lowercase_ : Any = self.dummy_model()
lowercase_ : Tuple = self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.timesteps ):
lowercase_ : str = model(lowercase_ , lowercase_ )
lowercase_ : Any = scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
return sample
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Dict = dict(self.forward_default_kwargs )
lowercase_ : Union[str, Any] = kwargs.pop("""num_inference_steps""" , lowercase_ )
for scheduler_class in self.scheduler_classes:
lowercase_ : List[Any] = self.get_scheduler_config()
lowercase_ : List[Any] = scheduler_class(**lowercase_ )
lowercase_ : Union[str, Any] = self.dummy_sample
lowercase_ : Dict = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase_ , """set_timesteps""" ):
scheduler.set_timesteps(lowercase_ )
elif num_inference_steps is not None and not hasattr(lowercase_ , """set_timesteps""" ):
lowercase_ : Union[str, Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowercase_ : Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
lowercase_ : Any = dummy_past_residuals[: scheduler.config.solver_order]
lowercase_ : Dict = scheduler.timesteps[5]
lowercase_ : Dict = scheduler.timesteps[6]
lowercase_ : int = scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
lowercase_ : List[Any] = scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def SCREAMING_SNAKE_CASE_ ( self : int ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
lowercase_ : Optional[Any] = UniPCMultistepScheduler(**self.get_scheduler_config() )
lowercase_ : Tuple = self.full_loop(scheduler=lowercase_ )
lowercase_ : List[Any] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.24_64 ) < 1E-3
lowercase_ : List[Any] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowercase_ : Optional[Any] = DEISMultistepScheduler.from_config(scheduler.config )
lowercase_ : Union[str, Any] = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowercase_ : Optional[Any] = UniPCMultistepScheduler.from_config(scheduler.config )
lowercase_ : Dict = self.full_loop(scheduler=lowercase_ )
lowercase_ : Optional[int] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.24_64 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self : Any ):
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int ):
self.check_over_configs(thresholding=lowercase_ )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowercase_ , prediction_type=lowercase_ , sample_max_value=lowercase_ , solver_order=lowercase_ , solver_type=lowercase_ , )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowercase_ , solver_type=lowercase_ , prediction_type=lowercase_ , )
lowercase_ : List[Any] = self.full_loop(
solver_order=lowercase_ , solver_type=lowercase_ , prediction_type=lowercase_ , )
assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
self.check_over_configs(lower_order_final=lowercase_ )
self.check_over_configs(lower_order_final=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=lowercase_ , time_step=0 )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Dict = self.full_loop()
lowercase_ : Optional[Any] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.24_64 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[Any] = self.full_loop(prediction_type="""v_prediction""" )
lowercase_ : Tuple = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.10_14 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Optional[int] = self.scheduler_classes[0]
lowercase_ : Optional[Any] = self.get_scheduler_config(thresholding=lowercase_ , dynamic_thresholding_ratio=0 )
lowercase_ : Any = scheduler_class(**lowercase_ )
lowercase_ : str = 10
lowercase_ : Dict = self.dummy_model()
lowercase_ : List[Any] = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.timesteps ):
lowercase_ : Optional[int] = model(lowercase_ , lowercase_ )
lowercase_ : Tuple = scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
assert sample.dtype == torch.floataa
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , **lowercase_ : Tuple ):
for scheduler_class in self.scheduler_classes:
lowercase_ : Dict = self.get_scheduler_config(**lowercase_ )
lowercase_ : List[str] = scheduler_class(**lowercase_ )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 21 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
"xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
),
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''xlm-roberta'''
def __init__( self : Any , lowercase_ : Dict=30522 , lowercase_ : Optional[Any]=768 , lowercase_ : Any=12 , lowercase_ : Dict=12 , lowercase_ : Optional[int]=3072 , lowercase_ : Optional[Any]="gelu" , lowercase_ : Optional[Any]=0.1 , lowercase_ : Dict=0.1 , lowercase_ : List[Any]=512 , lowercase_ : Union[str, Any]=2 , lowercase_ : Tuple=0.02 , lowercase_ : str=1E-12 , lowercase_ : Union[str, Any]=1 , lowercase_ : Union[str, Any]=0 , lowercase_ : str=2 , lowercase_ : Any="absolute" , lowercase_ : Union[str, Any]=True , lowercase_ : Union[str, Any]=None , **lowercase_ : List[Any] , ):
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
lowercase_ : int = vocab_size
lowercase_ : List[Any] = hidden_size
lowercase_ : Optional[int] = num_hidden_layers
lowercase_ : Optional[Any] = num_attention_heads
lowercase_ : str = hidden_act
lowercase_ : List[Any] = intermediate_size
lowercase_ : Tuple = hidden_dropout_prob
lowercase_ : Any = attention_probs_dropout_prob
lowercase_ : Any = max_position_embeddings
lowercase_ : Any = type_vocab_size
lowercase_ : Dict = initializer_range
lowercase_ : Any = layer_norm_eps
lowercase_ : Dict = position_embedding_type
lowercase_ : str = use_cache
lowercase_ : Dict = classifier_dropout
class __magic_name__ ( _UpperCAmelCase):
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
if self.task == "multiple-choice":
lowercase_ : List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase_ : List[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 21 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase : Optional[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] ) -> Tuple:
# initialize config
if "resnet-50" in model_name:
lowercase_ : Tuple = ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
elif "resnet-101" in model_name:
lowercase_ : str = ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
else:
raise ValueError("""Model name should include either resnet50 or resnet101""" )
lowercase_ : Optional[int] = DetrConfig(use_timm_backbone=UpperCAmelCase__ , backbone_config=UpperCAmelCase__ )
# set label attributes
lowercase_ : Dict = """panoptic""" in model_name
if is_panoptic:
lowercase_ : Optional[int] = 250
else:
lowercase_ : Optional[Any] = 91
lowercase_ : Dict = """huggingface/label-files"""
lowercase_ : Tuple = """coco-detection-id2label.json"""
lowercase_ : str = json.load(open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type="""dataset""" ) , """r""" ) )
lowercase_ : List[str] = {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
lowercase_ : List[str] = idalabel
lowercase_ : Dict = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Dict:
# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_ : List[Any] = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""") )
rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""") )
rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""") )
rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""") )
rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
F'''encoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
F'''decoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
) )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
) )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
] )
return rename_keys
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int ) -> str:
lowercase_ : Dict = state_dict.pop(UpperCAmelCase__ )
lowercase_ : List[Any] = val
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[Any]=False ) -> Dict:
lowercase_ : Union[str, Any] = """"""
if is_panoptic:
lowercase_ : Any = """detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase_ : Any = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase_ : Tuple = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ : Any = in_proj_weight[:256, :]
lowercase_ : Union[str, Any] = in_proj_bias[:256]
lowercase_ : str = in_proj_weight[256:512, :]
lowercase_ : str = in_proj_bias[256:512]
lowercase_ : Any = in_proj_weight[-256:, :]
lowercase_ : Optional[Any] = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase_ : str = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase_ : str = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ : Tuple = in_proj_weight[:256, :]
lowercase_ : List[Any] = in_proj_bias[:256]
lowercase_ : str = in_proj_weight[256:512, :]
lowercase_ : str = in_proj_bias[256:512]
lowercase_ : List[Any] = in_proj_weight[-256:, :]
lowercase_ : Optional[Any] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase_ : Union[str, Any] = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
lowercase_ : Dict = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase_ : int = in_proj_weight_cross_attn[:256, :]
lowercase_ : Union[str, Any] = in_proj_bias_cross_attn[:256]
lowercase_ : Optional[Any] = in_proj_weight_cross_attn[256:512, :]
lowercase_ : Optional[Any] = in_proj_bias_cross_attn[256:512]
lowercase_ : str = in_proj_weight_cross_attn[-256:, :]
lowercase_ : Union[str, Any] = in_proj_bias_cross_attn[-256:]
def lowerCamelCase ( ) -> List[str]:
lowercase_ : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ : Dict = Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__ ).raw )
return im
@torch.no_grad()
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : str=False ) -> Dict:
lowercase_ , lowercase_ : str = get_detr_config(UpperCAmelCase__ )
# load original model from torch hub
lowercase_ : Optional[int] = {
"""detr-resnet-50""": """detr_resnet50""",
"""detr-resnet-101""": """detr_resnet101""",
}
logger.info(F'''Converting model {model_name}...''' )
lowercase_ : Dict = torch.hub.load("""facebookresearch/detr""" , model_name_to_original_name[model_name] , pretrained=UpperCAmelCase__ ).eval()
lowercase_ : List[str] = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(UpperCAmelCase__ ):
if is_panoptic:
lowercase_ : Optional[int] = """detr.""" + src
rename_key(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCAmelCase__ , is_panoptic=UpperCAmelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase_ : List[str] = """detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
lowercase_ : List[Any] = state_dict.pop(UpperCAmelCase__ )
lowercase_ : Dict = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
lowercase_ : Tuple = state_dict.pop(UpperCAmelCase__ )
lowercase_ : List[str] = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
lowercase_ : int = state_dict.pop(UpperCAmelCase__ )
lowercase_ : Any = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
lowercase_ : List[Any] = state_dict.pop(UpperCAmelCase__ )
lowercase_ : Dict = val
# finally, create HuggingFace model and load state dict
lowercase_ : Optional[int] = DetrForSegmentation(UpperCAmelCase__ ) if is_panoptic else DetrForObjectDetection(UpperCAmelCase__ )
model.load_state_dict(UpperCAmelCase__ )
model.eval()
# verify our conversion on an image
lowercase_ : Tuple = """coco_panoptic""" if is_panoptic else """coco_detection"""
lowercase_ : Optional[int] = DetrImageProcessor(format=UpperCAmelCase__ )
lowercase_ : Dict = processor(images=prepare_img() , return_tensors="""pt""" )
lowercase_ : str = encoding["""pixel_values"""]
lowercase_ : Optional[int] = detr(UpperCAmelCase__ )
lowercase_ : List[str] = model(UpperCAmelCase__ )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
processor.save_pretrained(UpperCAmelCase__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("""Uploading PyTorch model and image processor to the hub...""" )
model.push_to_hub(F'''nielsr/{model_name}''' )
processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
_lowercase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="detr-resnet-50",
type=str,
choices=["detr-resnet-50", "detr-resnet-101"],
help="Name of the DETR model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
_lowercase : List[Any] = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 21 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 1 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class __magic_name__ :
def __init__( self : int , lowercase_ : int , lowercase_ : Optional[int]=13 , lowercase_ : str=7 , lowercase_ : Union[str, Any]=True , lowercase_ : Optional[int]=True , lowercase_ : List[str]=True , lowercase_ : Optional[int]=True , lowercase_ : List[Any]=99 , lowercase_ : Any=64 , lowercase_ : List[Any]=32 , lowercase_ : int=5 , lowercase_ : Optional[Any]=4 , lowercase_ : int=37 , lowercase_ : Optional[int]="gelu" , lowercase_ : Union[str, Any]=0.1 , lowercase_ : Tuple=0.1 , lowercase_ : List[str]=512 , lowercase_ : Optional[int]=16 , lowercase_ : List[str]=2 , lowercase_ : Any=0.02 , lowercase_ : Dict=3 , lowercase_ : Optional[int]=4 , lowercase_ : Any=None , ):
lowercase_ : Dict = parent
lowercase_ : Any = batch_size
lowercase_ : Dict = seq_length
lowercase_ : Any = is_training
lowercase_ : List[str] = use_input_mask
lowercase_ : Tuple = use_token_type_ids
lowercase_ : Union[str, Any] = use_labels
lowercase_ : Any = vocab_size
lowercase_ : List[str] = hidden_size
lowercase_ : str = embedding_size
lowercase_ : Optional[Any] = num_hidden_layers
lowercase_ : Union[str, Any] = num_attention_heads
lowercase_ : Any = intermediate_size
lowercase_ : Optional[Any] = hidden_act
lowercase_ : Dict = hidden_dropout_prob
lowercase_ : Any = attention_probs_dropout_prob
lowercase_ : int = max_position_embeddings
lowercase_ : List[str] = type_vocab_size
lowercase_ : List[Any] = type_sequence_label_size
lowercase_ : Union[str, Any] = initializer_range
lowercase_ : Optional[Any] = num_labels
lowercase_ : Any = num_choices
lowercase_ : str = scope
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : Optional[Any] = None
if self.use_input_mask:
lowercase_ : str = random_attention_mask([self.batch_size, self.seq_length] )
lowercase_ : Optional[Any] = None
if self.use_token_type_ids:
lowercase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase_ : Optional[int] = None
lowercase_ : List[Any] = None
lowercase_ : List[str] = None
if self.use_labels:
lowercase_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self : Any ):
return MegatronBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase_ , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Any , lowercase_ : int , lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int ):
lowercase_ : Tuple = MegatronBertModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Optional[int] = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ )
lowercase_ : str = model(lowercase_ , token_type_ids=lowercase_ )
lowercase_ : Dict = model(lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
lowercase_ : int = MegatronBertForMaskedLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : List[Any] = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Any , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Any = MegatronBertForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : str = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Optional[int] , lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any ):
lowercase_ : List[str] = MegatronBertForNextSentencePrediction(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Tuple = model(
lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Dict , lowercase_ : List[str] ):
lowercase_ : Union[str, Any] = MegatronBertForPreTraining(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : List[Any] = model(
lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ , next_sentence_label=lowercase_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : str , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] ):
lowercase_ : Dict = MegatronBertForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Optional[Any] = model(
lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
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 SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Optional[Any] = self.num_labels
lowercase_ : int = MegatronBertForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : List[Any] = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Dict , lowercase_ : Any , lowercase_ : Any , lowercase_ : Tuple ):
lowercase_ : Dict = self.num_labels
lowercase_ : str = MegatronBertForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : str = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : str , lowercase_ : int , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] ):
lowercase_ : List[str] = self.num_choices
lowercase_ : Any = MegatronBertForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase_ : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase_ : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase_ : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase_ : Any = model(
lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Optional[int] = config_and_inputs
lowercase_ : Optional[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{
'''feature-extraction''': MegatronBertModel,
'''fill-mask''': MegatronBertForMaskedLM,
'''question-answering''': MegatronBertForQuestionAnswering,
'''text-classification''': MegatronBertForSequenceClassification,
'''text-generation''': MegatronBertForCausalLM,
'''token-classification''': MegatronBertForTokenClassification,
'''zero-shot''': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = True
# test_resize_embeddings = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Union[str, Any]=False ):
lowercase_ : List[str] = super()._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
if return_labels:
if model_class in get_values(lowercase_ ):
lowercase_ : Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowercase_ )
lowercase_ : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase_ )
return inputs_dict
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[int] = MegatronBertModelTester(self )
lowercase_ : Optional[Any] = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowercase_ )
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> Any:
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
_lowercase : List[Any] = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase):
@slow
@unittest.skip("""Model is not available.""" )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[str] = """nvidia/megatron-bert-uncased-345m"""
if "MYDIR" in os.environ:
lowercase_ : str = os.path.join(os.environ["""MYDIR"""] , lowercase_ )
lowercase_ : Optional[int] = MegatronBertModel.from_pretrained(lowercase_ )
model.to(lowercase_ )
model.half()
lowercase_ : List[str] = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]] )
with torch.no_grad():
lowercase_ : str = model(lowercase_ )[0]
lowercase_ : int = torch.Size((1, 9, 1024) )
self.assertEqual(output.shape , lowercase_ )
lowercase_ : Optional[int] = [-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
for ii in range(3 ):
for jj in range(3 ):
lowercase_ : Dict = output[0, ii, jj]
lowercase_ : Optional[Any] = expected[3 * ii + jj]
lowercase_ : Optional[Any] = """ii={} jj={} a={} b={}""".format(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
self.assertTrue(math.isclose(lowercase_ , lowercase_ , rel_tol=lowercase_ , abs_tol=lowercase_ ) , msg=lowercase_ )
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase : int = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : str ) -> YolosConfig:
lowercase_ : Any = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
lowercase_ : Dict = 192
lowercase_ : List[Any] = 768
lowercase_ : Dict = 12
lowercase_ : List[str] = 3
lowercase_ : List[str] = [800, 1333]
lowercase_ : Tuple = False
elif yolos_name == "yolos_s_dWr":
lowercase_ : str = 330
lowercase_ : Tuple = 14
lowercase_ : Optional[int] = 6
lowercase_ : List[Any] = 1320
elif "yolos_s" in yolos_name:
lowercase_ : Any = 384
lowercase_ : List[Any] = 1536
lowercase_ : Union[str, Any] = 12
lowercase_ : int = 6
elif "yolos_b" in yolos_name:
lowercase_ : List[Any] = [800, 1344]
lowercase_ : int = 91
lowercase_ : str = """huggingface/label-files"""
lowercase_ : List[str] = """coco-detection-id2label.json"""
lowercase_ : List[str] = json.load(open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type="""dataset""" ) , """r""" ) )
lowercase_ : List[Any] = {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
lowercase_ : Optional[Any] = idalabel
lowercase_ : List[Any] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( UpperCAmelCase__ : dict , UpperCAmelCase__ : YolosConfig , UpperCAmelCase__ : bool = False ) -> Any:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase_ : Optional[int] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
lowercase_ : str = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ : Optional[Any] = in_proj_weight[: config.hidden_size, :]
lowercase_ : Union[str, Any] = in_proj_bias[: config.hidden_size]
lowercase_ : Dict = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase_ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase_ : Tuple = in_proj_weight[-config.hidden_size :, :]
lowercase_ : List[str] = in_proj_bias[-config.hidden_size :]
def lowerCamelCase ( UpperCAmelCase__ : str ) -> str:
if "backbone" in name:
lowercase_ : Union[str, Any] = name.replace("""backbone""" , """vit""" )
if "cls_token" in name:
lowercase_ : int = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "det_token" in name:
lowercase_ : Optional[int] = name.replace("""det_token""" , """embeddings.detection_tokens""" )
if "mid_pos_embed" in name:
lowercase_ : List[str] = name.replace("""mid_pos_embed""" , """encoder.mid_position_embeddings""" )
if "pos_embed" in name:
lowercase_ : int = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
lowercase_ : Union[str, Any] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "blocks" in name:
lowercase_ : int = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
lowercase_ : Union[str, Any] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
lowercase_ : Optional[int] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
lowercase_ : Union[str, Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
lowercase_ : Any = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
lowercase_ : List[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowercase_ : Any = name.replace("""mlp.fc2""" , """output.dense""" )
if "class_embed" in name:
lowercase_ : int = name.replace("""class_embed""" , """class_labels_classifier""" )
if "bbox_embed" in name:
lowercase_ : Optional[Any] = name.replace("""bbox_embed""" , """bbox_predictor""" )
if "vit.norm" in name:
lowercase_ : Optional[int] = name.replace("""vit.norm""" , """vit.layernorm""" )
return name
def lowerCamelCase ( UpperCAmelCase__ : dict , UpperCAmelCase__ : YolosForObjectDetection ) -> dict:
for key in orig_state_dict.copy().keys():
lowercase_ : Optional[int] = orig_state_dict.pop(UpperCAmelCase__ )
if "qkv" in key:
lowercase_ : str = key.split(""".""" )
lowercase_ : Union[str, Any] = int(key_split[2] )
lowercase_ : Union[str, Any] = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
lowercase_ : Union[str, Any] = val[:dim, :]
lowercase_ : Optional[Any] = val[
dim : dim * 2, :
]
lowercase_ : Any = val[-dim:, :]
else:
lowercase_ : List[str] = val[:dim]
lowercase_ : Any = val[dim : dim * 2]
lowercase_ : Optional[Any] = val[-dim:]
else:
lowercase_ : Union[str, Any] = val
return orig_state_dict
def lowerCamelCase ( ) -> torch.Tensor:
lowercase_ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ : List[str] = Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__ ).raw )
return im
@torch.no_grad()
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : bool = False ) -> Optional[Any]:
lowercase_ : int = get_yolos_config(UpperCAmelCase__ )
# load original state_dict
lowercase_ : List[str] = torch.load(UpperCAmelCase__ , map_location="""cpu""" )["""model"""]
# load 🤗 model
lowercase_ : str = YolosForObjectDetection(UpperCAmelCase__ )
model.eval()
lowercase_ : Dict = convert_state_dict(UpperCAmelCase__ , UpperCAmelCase__ )
model.load_state_dict(UpperCAmelCase__ )
# Check outputs on an image, prepared by YolosImageProcessor
lowercase_ : Union[str, Any] = 800 if yolos_name != """yolos_ti""" else 512
lowercase_ : Optional[int] = YolosImageProcessor(format="""coco_detection""" , size=UpperCAmelCase__ )
lowercase_ : List[str] = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowercase_ : List[Any] = model(**UpperCAmelCase__ )
lowercase_ , lowercase_ : str = outputs.logits, outputs.pred_boxes
lowercase_ , lowercase_ : Dict = None, None
if yolos_name == "yolos_ti":
lowercase_ : Dict = torch.tensor(
[[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]] )
lowercase_ : Optional[Any] = torch.tensor(
[[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]] )
elif yolos_name == "yolos_s_200_pre":
lowercase_ : Any = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] )
lowercase_ : List[Any] = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] )
elif yolos_name == "yolos_s_300_pre":
lowercase_ : List[str] = torch.tensor(
[[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] )
lowercase_ : Optional[int] = torch.tensor(
[[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]] )
elif yolos_name == "yolos_s_dWr":
lowercase_ : List[str] = torch.tensor(
[[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] )
lowercase_ : str = torch.tensor(
[[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]] )
elif yolos_name == "yolos_base":
lowercase_ : Tuple = torch.tensor(
[[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] )
lowercase_ : str = torch.tensor(
[[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]] )
else:
raise ValueError(F'''Unknown yolos_name: {yolos_name}''' )
assert torch.allclose(logits[0, :3, :3] , UpperCAmelCase__ , atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , UpperCAmelCase__ , atol=1e-4 )
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase__ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase__ )
if push_to_hub:
lowercase_ : Any = {
"""yolos_ti""": """yolos-tiny""",
"""yolos_s_200_pre""": """yolos-small""",
"""yolos_s_300_pre""": """yolos-small-300""",
"""yolos_s_dWr""": """yolos-small-dwr""",
"""yolos_base""": """yolos-base""",
}
print("""Pushing to the hub...""" )
lowercase_ : List[Any] = model_mapping[yolos_name]
image_processor.push_to_hub(UpperCAmelCase__ , organization="""hustvl""" )
model.push_to_hub(UpperCAmelCase__ , organization="""hustvl""" )
if __name__ == "__main__":
_lowercase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--yolos_name",
default="yolos_s_200_pre",
type=str,
help=(
"Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
" 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
),
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
_lowercase : Tuple = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
_lowercase : Any = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default='''tab_fact''', metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''})
UpperCamelCase__ = field(
default='''tab_fact''', metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}, )
UpperCamelCase__ = field(
default=1024, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''A csv or a json file containing the training data.'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''A csv or a json file containing the validation data.'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''A csv or a json file containing the test data.'''})
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
lowercase_ : List[str] = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
lowercase_ : Optional[int] = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}, )
UpperCamelCase__ = field(
default='''main''', metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}, )
UpperCamelCase__ = field(
default=_UpperCAmelCase, metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
}, )
def lowerCamelCase ( ) -> Optional[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase_ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase_ , lowercase_ , lowercase_ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase_ , lowercase_ , lowercase_ : Any = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
lowercase_ : Tuple = training_args.get_process_log_level()
logger.setLevel(UpperCAmelCase__ )
datasets.utils.logging.set_verbosity(UpperCAmelCase__ )
transformers.utils.logging.set_verbosity(UpperCAmelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowercase_ : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase_ : Tuple = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowercase_ : List[str] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
lowercase_ : List[str] = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
lowercase_ : int = data_args.train_file.split(""".""" )[-1]
lowercase_ : Any = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
lowercase_ : List[str] = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(F'''load a local file for {key}: {data_files[key]}''' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
lowercase_ : List[str] = load_dataset("""csv""" , data_files=UpperCAmelCase__ , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
lowercase_ : Optional[Any] = load_dataset("""json""" , data_files=UpperCAmelCase__ , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
lowercase_ : List[Any] = raw_datasets["""train"""].features["""label"""].names
lowercase_ : Dict = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase_ : Dict = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
lowercase_ : str = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=UpperCAmelCase__ , )
lowercase_ : List[str] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
lowercase_ : Any = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowercase_ : int = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
lowercase_ : Dict = {"""Refused""": 0, """Entailed""": 1}
lowercase_ : Optional[int] = {0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
lowercase_ : List[Any] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(UpperCAmelCase__ : List[str] ):
# Tokenize the texts
def _convert_table_text_to_pandas(UpperCAmelCase__ : Dict ):
lowercase_ : Optional[int] = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
lowercase_ : str = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
lowercase_ : List[Any] = examples["""statement"""]
lowercase_ : Dict = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
lowercase_ : Optional[int] = tokenizer(UpperCAmelCase__ , UpperCAmelCase__ , padding=UpperCAmelCase__ , max_length=UpperCAmelCase__ , truncation=UpperCAmelCase__ )
lowercase_ : Union[str, Any] = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
lowercase_ : Optional[Any] = raw_datasets.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
lowercase_ : List[Any] = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
lowercase_ : Union[str, Any] = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
lowercase_ : Union[str, Any] = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
lowercase_ : str = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
lowercase_ : Optional[Any] = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
lowercase_ : Any = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(UpperCAmelCase__ ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(UpperCAmelCase__ : EvalPrediction ):
lowercase_ : Optional[Any] = p.predictions[0] if isinstance(p.predictions , UpperCAmelCase__ ) else p.predictions
lowercase_ : Optional[int] = np.argmax(UpperCAmelCase__ , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowercase_ : Tuple = default_data_collator
elif training_args.fpaa:
lowercase_ : int = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 )
else:
lowercase_ : Union[str, Any] = None
# Initialize our Trainer
lowercase_ : Any = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
lowercase_ : int = None
if training_args.resume_from_checkpoint is not None:
lowercase_ : str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase_ : Tuple = last_checkpoint
lowercase_ : Tuple = trainer.train(resume_from_checkpoint=UpperCAmelCase__ )
lowercase_ : Optional[Any] = train_result.metrics
lowercase_ : int = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCAmelCase__ )
)
lowercase_ : int = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , UpperCAmelCase__ )
trainer.save_metrics("""train""" , UpperCAmelCase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowercase_ : Union[str, Any] = trainer.evaluate(eval_dataset=UpperCAmelCase__ )
lowercase_ : Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) )
trainer.log_metrics("""eval""" , UpperCAmelCase__ )
trainer.save_metrics("""eval""" , UpperCAmelCase__ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
lowercase_ : Optional[Any] = predict_dataset.remove_columns("""label""" )
lowercase_ : Any = trainer.predict(UpperCAmelCase__ , metric_key_prefix="""predict""" ).predictions
lowercase_ : str = np.argmax(UpperCAmelCase__ , axis=1 )
lowercase_ : Union[str, Any] = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(UpperCAmelCase__ ):
lowercase_ : List[Any] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
lowercase_ : Optional[int] = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCAmelCase__ )
else:
trainer.create_model_card(**UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 21 | '''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | 1 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : bool , UpperCAmelCase__ : bool ) -> Optional[Any]:
def run_func(UpperCAmelCase__ : Dict ):
@wraps(UpperCAmelCase__ )
def run_in_eager_mode(*UpperCAmelCase__ : Any , **UpperCAmelCase__ : List[str] ):
return func(*UpperCAmelCase__ , **UpperCAmelCase__ )
@wraps(UpperCAmelCase__ )
@tf.function(experimental_compile=UpperCAmelCase__ )
def run_in_graph_mode(*UpperCAmelCase__ : str , **UpperCAmelCase__ : str ):
return func(*UpperCAmelCase__ , **UpperCAmelCase__ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"""Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> ["tf.Tensor"]:
lowercase_ : str = random.Random()
lowercase_ : int = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(UpperCAmelCase__ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = "TensorFlow"
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return tf.__version__
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
# initialize GPU on separate process
lowercase_ : Union[str, Any] = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowercase_ : Optional[int] = self._prepare_inference_func(lowercase_ , lowercase_ , lowercase_ )
return self._measure_speed(_inference )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
lowercase_ : int = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowercase_ : List[Any] = self._prepare_train_func(lowercase_ , lowercase_ , lowercase_ )
return self._measure_speed(_train )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowercase_ )
lowercase_ : str = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowercase_ : str = self._prepare_inference_func(lowercase_ , lowercase_ , lowercase_ )
return self._measure_memory(_inference )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowercase_ )
lowercase_ : Tuple = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
lowercase_ : List[Any] = self._prepare_train_func(lowercase_ , lowercase_ , lowercase_ )
return self._measure_memory(_train )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
lowercase_ : Any = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
lowercase_ : Optional[int] = (
hasattr(lowercase_ , """architectures""" )
and isinstance(config.architectures , lowercase_ )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
lowercase_ : int = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
lowercase_ : int = __import__("""transformers""" , fromlist=[model_class] )
lowercase_ : Optional[Any] = getattr(lowercase_ , lowercase_ )
lowercase_ : Any = model_cls(lowercase_ )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
lowercase_ : Union[str, Any] = TF_MODEL_MAPPING[config.__class__](lowercase_ )
# encoder-decoder has vocab size saved differently
lowercase_ : Tuple = config.vocab_size if hasattr(lowercase_ , """vocab_size""" ) else config.encoder.vocab_size
lowercase_ : str = random_input_ids(lowercase_ , lowercase_ , lowercase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowercase_ , decoder_input_ids=lowercase_ , training=lowercase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowercase_ , training=lowercase_ )
lowercase_ : List[Any] = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : int ):
lowercase_ : int = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
lowercase_ : str = (
hasattr(lowercase_ , """architectures""" )
and isinstance(config.architectures , lowercase_ )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
lowercase_ : str = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
lowercase_ : Dict = __import__("""transformers""" , fromlist=[model_class] )
lowercase_ : Optional[Any] = getattr(lowercase_ , lowercase_ )
lowercase_ : int = model_cls(lowercase_ )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
lowercase_ : Tuple = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase_ )
# encoder-decoder has vocab size saved differently
lowercase_ : Optional[Any] = config.vocab_size if hasattr(lowercase_ , """vocab_size""" ) else config.encoder.vocab_size
lowercase_ : str = random_input_ids(lowercase_ , lowercase_ , lowercase_ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
lowercase_ : Dict = model(lowercase_ , decoder_input_ids=lowercase_ , labels=lowercase_ , training=lowercase_ )[0]
lowercase_ : Dict = tf.gradients(lowercase_ , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
lowercase_ : Optional[Any] = model(lowercase_ , labels=lowercase_ , training=lowercase_ )[0]
lowercase_ : Any = tf.gradients(lowercase_ , model.trainable_variables )
return gradients
lowercase_ : Union[str, Any] = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : Optional[int] ):
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
timeit.repeat(lowercase_ , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
lowercase_ : Tuple = timeit.repeat(
lowercase_ , repeat=self.args.repeat , number=10 , )
return min(lowercase_ ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Callable[[], None] ):
logger.info(
"""Note that TensorFlow allocates more memory than """
"""it might need to speed up computation. """
"""The memory reported here corresponds to the memory """
"""reported by `nvidia-smi`, which can vary depending """
"""on total available memory on the GPU that is used.""" )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"""`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
""" consumption line by line.""" )
lowercase_ : int = start_memory_tracing("""transformers""" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"""Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
""" with `args.memory=False`""" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"""py3nvml not installed, we won't log GPU memory usage. """
"""Install py3nvml (pip install py3nvml) to log information about GPU.""" )
lowercase_ : Dict = """N/A"""
else:
logger.info(
"""Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
""" running on the same GPU.""" )
# init nvml
nvml.nvmlInit()
func()
lowercase_ : Union[str, Any] = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
lowercase_ : Optional[Any] = nvml.nvmlDeviceGetMemoryInfo(lowercase_ )
lowercase_ : Optional[Any] = meminfo.used
lowercase_ : Optional[Any] = Memory(lowercase_ )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"""When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
""" TensorFlow.""" )
lowercase_ : Any = None
else:
lowercase_ : Union[str, Any] = measure_peak_memory_cpu(lowercase_ )
lowercase_ : List[str] = Memory(lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else memory_bytes
if self.args.trace_memory_line_by_line:
lowercase_ : List[str] = stop_memory_tracing(lowercase_ )
if memory is None:
lowercase_ : List[Any] = summary.total
else:
lowercase_ : Optional[Any] = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 21 | '''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : List[Any] ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None ):
lowercase_ : Optional[Any] = {}
lowercase_ : Tuple = {}
if prompt is not None:
lowercase_ : Tuple = prompt
if generate_kwargs is not None:
lowercase_ : List[str] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase_ : List[Any] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase_ : str = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : Optional[int] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : Tuple=None ):
lowercase_ : List[Any] = load_image(lowercase_ )
if prompt is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowercase_ )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase_ : List[Any] = self.model.config.model_type
if model_type == "git":
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : Union[str, Any] = self.tokenizer(text=lowercase_ , add_special_tokens=lowercase_ ).input_ids
lowercase_ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase_ : List[Any] = torch.tensor(lowercase_ ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase_ : Union[str, Any] = self.image_processor(images=lowercase_ , header_text=lowercase_ , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : List[str] = self.tokenizer(lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase_ : str = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict , lowercase_ : Optional[Any]=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , lowercase_ )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase_ : Any = None
if generate_kwargs is None:
lowercase_ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase_ : Dict = model_inputs.pop(self.model.main_input_name )
lowercase_ : Any = self.model.generate(lowercase_ , **lowercase_ , **lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] ):
lowercase_ : List[str] = []
for output_ids in model_outputs:
lowercase_ : Union[str, Any] = {
"""generated_text""": self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , )
}
records.append(lowercase_ )
return records
| 21 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : int = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_lowercase : Any = 256047
_lowercase : List[str] = 256145
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = NllbTokenizer
UpperCamelCase__ = NllbTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
UpperCamelCase__ = {}
def SCREAMING_SNAKE_CASE_ ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ : Union[str, Any] = NllbTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = NllbTokenizer(lowercase_ , keep_accents=lowercase_ )
lowercase_ : Optional[int] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowercase_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowercase_ : str = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
lowercase_ : Tuple = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
lowercase_ : List[Any] = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Union[str, Any] = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase_ : Optional[int] = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase_ : Tuple = tempfile.mkdtemp()
lowercase_ : List[Any] = tokenizer_r.save_pretrained(lowercase_ )
lowercase_ : Dict = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
lowercase_ : List[Any] = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
lowercase_ : Dict = tokenizer_r.from_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
lowercase_ : List[Any] = tempfile.mkdtemp()
lowercase_ : Tuple = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
lowercase_ : Union[str, Any] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
lowercase_ : Any = tokenizer_r.from_pretrained(lowercase_ )
lowercase_ : List[Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
lowercase_ : int = tempfile.mkdtemp()
lowercase_ : Any = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
lowercase_ : Tuple = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowercase_ : List[Any] = tokenizer_r.from_pretrained(lowercase_ )
lowercase_ : Any = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : str ):
if not self.test_seqaseq:
return
lowercase_ : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Longer text that will definitely require truncation.
lowercase_ : Any = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
""" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
""" will only worsen the violence and misery for millions of people.""",
]
lowercase_ : Any = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
""" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
""" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
try:
lowercase_ : List[Any] = tokenizer.prepare_seqaseq_batch(
src_texts=lowercase_ , tgt_texts=lowercase_ , max_length=3 , max_target_length=10 , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
lowercase_ : int = tokenizer.prepare_seqaseq_batch(
lowercase_ , tgt_texts=lowercase_ , max_length=3 , return_tensors="""pt""" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
lowercase_ : List[Any] = tokenizer.prepare_seqaseq_batch(
src_texts=lowercase_ , max_length=3 , max_target_length=10 , return_tensors="""pt""" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("""decoder_input_ids""" , lowercase_ )
@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece.""" )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase_ : Dict = [AddedToken("""<special>""" , lstrip=lowercase_ )]
lowercase_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ )
lowercase_ : Tuple = tokenizer_r.encode("""Hey this is a <special> token""" )
lowercase_ : List[Any] = tokenizer_r.encode("""<special>""" , add_special_tokens=lowercase_ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
lowercase_ : int = self.rust_tokenizer_class.from_pretrained(
lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
lowercase_ : str = self.tokenizer_class.from_pretrained(
lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ )
lowercase_ : Tuple = tokenizer_p.encode("""Hey this is a <special> token""" )
lowercase_ : int = tokenizer_cr.encode("""Hey this is a <special> token""" )
self.assertEqual(lowercase_ , lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = '''facebook/nllb-200-distilled-600M'''
UpperCamelCase__ = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
UpperCamelCase__ = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
UpperCamelCase__ = [
25_6047,
1_6297,
13_4408,
8165,
24_8066,
1_4734,
950,
1135,
10_5721,
3573,
83,
2_7352,
108,
4_9486,
2,
]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[Any] ):
lowercase_ : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" )
lowercase_ : Any = 1
return cls
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""] , 256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""] , 256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""] , 256057 )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : str = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
self.assertIn(lowercase_ , self.tokenizer.all_special_ids )
# fmt: off
lowercase_ : List[str] = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
lowercase_ : Optional[Any] = self.tokenizer.decode(lowercase_ , skip_special_tokens=lowercase_ )
lowercase_ : List[str] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.assertNotIn(self.tokenizer.eos_token , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : Tuple = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , lowercase_ )
lowercase_ : List[str] = 10
lowercase_ : Dict = self.tokenizer(lowercase_ , max_length=lowercase_ , truncation=lowercase_ ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , lowercase_ )
self.assertEqual(len(lowercase_ ) , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [256203, 3] )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Optional[Any] = tempfile.mkdtemp()
lowercase_ : Any = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowercase_ )
lowercase_ : Tuple = NllbTokenizer.from_pretrained(lowercase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowercase_ )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Tuple = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowercase_ , truncation=lowercase_ , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
lowercase_ : Tuple = shift_tokens_right(
batch["""labels"""] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["""ron_Latn"""] )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
lowercase_ : Any = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowercase_ )
self.assertEqual(lowercase_ , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[Any] = self.tokenizer(self.src_text , padding=lowercase_ , truncation=lowercase_ , max_length=3 , return_tensors="""pt""" )
lowercase_ : List[Any] = self.tokenizer(
text_target=self.tgt_text , padding=lowercase_ , truncation=lowercase_ , max_length=10 , return_tensors="""pt""" )
lowercase_ : Optional[Any] = targets["""input_ids"""]
lowercase_ : int = shift_tokens_right(
lowercase_ , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
nested_simplify(lowercase_ ) , {
# A, test, EOS, en_XX
"""input_ids""": [[256047, 70, 7356, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 256057,
} , )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Any = True
lowercase_ : Optional[int] = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
lowercase_ : List[str] = False
lowercase_ : Optional[Any] = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 21 | '''simple docstring'''
class __magic_name__ :
def __init__( self : int , lowercase_ : list ):
lowercase_ : Dict = set_counts
lowercase_ : List[Any] = max(lowercase_ )
lowercase_ : str = len(lowercase_ )
lowercase_ : str = [1] * num_sets
lowercase_ : Dict = list(range(lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : int ):
lowercase_ : List[Any] = self.get_parent(lowercase_ )
lowercase_ : Union[str, Any] = self.get_parent(lowercase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase_ : List[str] = 0
lowercase_ : Optional[int] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase_ : int = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase_ : int = 0
lowercase_ : List[Any] = src_parent
lowercase_ : List[Any] = self.set_counts[src_parent]
lowercase_ : Tuple = max(self.max_set , lowercase_ )
return True
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
lowercase_ : int = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 21 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_lowercase : Optional[Any] = 16
_lowercase : str = 32
def lowerCamelCase ( UpperCAmelCase__ : Accelerator , UpperCAmelCase__ : int = 16 , UpperCAmelCase__ : str = "bert-base-cased" ) -> List[str]:
lowercase_ : List[str] = AutoTokenizer.from_pretrained(UpperCAmelCase__ )
lowercase_ : List[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCAmelCase__ : Tuple ):
# max_length=None => use the model max length (it's actually the default)
lowercase_ : Optional[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCAmelCase__ , max_length=UpperCAmelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase_ : List[str] = datasets.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=UpperCAmelCase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase_ : Union[str, Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCAmelCase__ : Tuple ):
# 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(UpperCAmelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(UpperCAmelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowercase_ : Any = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
lowercase_ : int = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
return train_dataloader, eval_dataloader
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] ) -> str:
# Initialize accelerator
lowercase_ : Union[str, Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase_ : Any = config["""lr"""]
lowercase_ : str = int(config["""num_epochs"""] )
lowercase_ : Tuple = int(config["""seed"""] )
lowercase_ : Optional[Any] = int(config["""batch_size"""] )
lowercase_ : List[Any] = args.model_name_or_path
set_seed(UpperCAmelCase__ )
lowercase_ , lowercase_ : Dict = get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase_ : int = AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__ , return_dict=UpperCAmelCase__ )
# Instantiate optimizer
lowercase_ : Dict = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase_ : List[Any] = optimizer_cls(params=model.parameters() , lr=UpperCAmelCase__ )
if accelerator.state.deepspeed_plugin is not None:
lowercase_ : Optional[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
lowercase_ : int = 1
lowercase_ : Union[str, Any] = (len(UpperCAmelCase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase_ : List[str] = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase__ , num_warmup_steps=0 , num_training_steps=UpperCAmelCase__ , )
else:
lowercase_ : Optional[int] = DummyScheduler(UpperCAmelCase__ , total_num_steps=UpperCAmelCase__ , 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.
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = accelerator.prepare(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# We need to keep track of how many total steps we have iterated over
lowercase_ : str = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase_ : str = 0
# Now we train the model
lowercase_ : int = evaluate.load("""glue""" , """mrpc""" )
lowercase_ : Tuple = 0
lowercase_ : Tuple = {}
for epoch in range(UpperCAmelCase__ , UpperCAmelCase__ ):
model.train()
for step, batch in enumerate(UpperCAmelCase__ ):
lowercase_ : Optional[Any] = model(**UpperCAmelCase__ )
lowercase_ : int = outputs.loss
lowercase_ : List[str] = loss / gradient_accumulation_steps
accelerator.backward(UpperCAmelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
lowercase_ : Tuple = 0
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase_ : Tuple = model(**UpperCAmelCase__ )
lowercase_ : int = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowercase_ , lowercase_ : Dict = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(UpperCAmelCase__ ) - 1:
lowercase_ : List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowercase_ : List[str] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=UpperCAmelCase__ , references=UpperCAmelCase__ , )
lowercase_ : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , UpperCAmelCase__ )
lowercase_ : Tuple = eval_metric["""accuracy"""]
if best_performance < eval_metric["accuracy"]:
lowercase_ : List[Any] = eval_metric["""accuracy"""]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """all_results.json""" ) , """w""" ) as f:
json.dump(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( ) -> int:
lowercase_ : int = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=UpperCAmelCase__ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=UpperCAmelCase__ , )
parser.add_argument(
"""--output_dir""" , type=UpperCAmelCase__ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--performance_lower_bound""" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" , )
parser.add_argument(
"""--num_epochs""" , type=UpperCAmelCase__ , default=3 , help="""Number of train epochs.""" , )
lowercase_ : Union[str, Any] = parser.parse_args()
lowercase_ : int = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 21 | '''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase : str = logging.get_logger(__name__)
_lowercase : str = "▁"
_lowercase : int = {"vocab_file": "sentencepiece.bpe.model"}
_lowercase : Any = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
_lowercase : int = {
"facebook/xglm-564M": 2048,
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self : str , lowercase_ : Optional[int] , lowercase_ : List[Any]="<s>" , lowercase_ : Tuple="</s>" , lowercase_ : List[Any]="</s>" , lowercase_ : List[Any]="<s>" , lowercase_ : Tuple="<unk>" , lowercase_ : Any="<pad>" , lowercase_ : Optional[Dict[str, Any]] = None , **lowercase_ : Union[str, Any] , ):
lowercase_ : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
lowercase_ : List[str] = 7
lowercase_ : Union[str, Any] = [f'''<madeupword{i}>''' for i in range(self.num_madeup_words )]
lowercase_ : Optional[Any] = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , cls_token=lowercase_ , pad_token=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
lowercase_ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowercase_ ) )
lowercase_ : str = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowercase_ : Optional[Any] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
lowercase_ : int = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
lowercase_ : Dict = len(self.sp_model )
lowercase_ : Dict = {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(lowercase_ )
lowercase_ : Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Optional[int] ):
lowercase_ : Optional[Any] = self.__dict__.copy()
lowercase_ : int = None
lowercase_ : Optional[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Optional[Any] , lowercase_ : Tuple ):
lowercase_ : int = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase_ : Union[str, Any] = {}
lowercase_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
lowercase_ : str = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None , lowercase_ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ , token_ids_a=lowercase_ , already_has_special_tokens=lowercase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowercase_ ))
return [1] + ([0] * len(lowercase_ )) + [1, 1] + ([0] * len(lowercase_ ))
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
lowercase_ : Tuple = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : str = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : str ):
return self.sp_model.encode(lowercase_ , out_type=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : List[Any] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase_ : int = self.sp_model.PieceToId(lowercase_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : List[Any] ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Union[str, Any] ):
lowercase_ : str = """""".join(lowercase_ ).replace(lowercase_ , """ """ ).strip()
return out_string
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not os.path.isdir(lowercase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase_ : Optional[int] = 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_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ , """wb""" ) as fi:
lowercase_ : List[Any] = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
| 21 | '''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 lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : Any , lowercase_ : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : str ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float ):
lowercase_ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(lowercase_ , lowercase_ , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict , lowercase_ : Tuple=None , **lowercase_ : Optional[int] ):
lowercase_ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : Any = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : List[Any]=None , **lowercase_ : Tuple ):
lowercase_ , lowercase_ : Any = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : List[Any] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
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 SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any]=None , **lowercase_ : int ):
lowercase_ , lowercase_ : Union[str, Any] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Tuple = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : List[str] = model(input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ )
lowercase_ : Union[str, Any] = after_output[0]
lowercase_ : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , lowercase_ : int , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Dict=None , **lowercase_ : Optional[Any] ):
lowercase_ , lowercase_ : Optional[int] = self.get_vision_text_model(lowercase_ , lowercase_ )
lowercase_ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase_ )
lowercase_ : Optional[int] = model(
input_ids=lowercase_ , pixel_values=lowercase_ , attention_mask=lowercase_ , output_attentions=lowercase_ )
lowercase_ : Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowercase_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase_ : List[str] = to_atuple(vision_model.config.image_size )
lowercase_ : Optional[Any] = to_atuple(vision_model.config.patch_size )
lowercase_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowercase_ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowercase_ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase_ ) , 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 SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : int ):
pt_model.to(lowercase_ )
pt_model.eval()
# prepare inputs
lowercase_ : int = inputs_dict
lowercase_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowercase_ : str = pt_model(**lowercase_ ).to_tuple()
lowercase_ : Optional[Any] = fx_model(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowercase_ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase_ )
lowercase_ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ , from_pt=lowercase_ )
lowercase_ : Dict = fx_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase_ )
lowercase_ : Union[str, Any] = VisionTextDualEncoderModel.from_pretrained(lowercase_ , from_flax=lowercase_ )
pt_model_loaded.to(lowercase_ )
pt_model_loaded.eval()
with torch.no_grad():
lowercase_ : List[Any] = pt_model_loaded(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) , """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(lowercase_ , pt_output_loaded.numpy() , 4E-2 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : List[Any] = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Union[str, Any] = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase_ )
lowercase_ : Tuple = fx_state
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] ):
lowercase_ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase_ , lowercase_ )
lowercase_ : int = VisionTextDualEncoderModel(lowercase_ )
lowercase_ : Dict = FlaxVisionTextDualEncoderModel(lowercase_ )
lowercase_ : Optional[Any] = load_flax_weights_in_pytorch_model(lowercase_ , fx_model.params )
self.check_pt_flax_equivalence(lowercase_ , lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = self.prepare_config_and_inputs()
lowercase_ : List[Any] = config_inputs_dict.pop("""vision_config""" )
lowercase_ : int = config_inputs_dict.pop("""text_config""" )
lowercase_ : Optional[int] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowercase_ , lowercase_ , lowercase_ )
self.check_equivalence_flax_to_pt(lowercase_ , lowercase_ , lowercase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ , lowercase_ : str = self.get_pretrained_model_and_inputs()
lowercase_ : Dict = model_a(**lowercase_ )
lowercase_ : str = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase_ )
lowercase_ : str = model_a(**lowercase_ )
lowercase_ : Union[str, Any] = after_outputs[0]
lowercase_ : Any = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase_ , 1E-5 )
@require_flax
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : Any = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : str = random_attention_mask([batch_size, 4] )
lowercase_ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple ):
lowercase_ : Union[str, Any] = FlaxViTModel(lowercase_ )
lowercase_ : Dict = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Any = FlaxViTModelTester(self )
lowercase_ : Optional[Any] = FlaxBertModelTester(self )
lowercase_ : Dict = vit_model_tester.prepare_config_and_inputs()
lowercase_ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : List[str] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : Union[str, Any] = 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 __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=lowercase_ , text_from_pt=lowercase_ , )
lowercase_ : List[str] = 13
lowercase_ : Optional[Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowercase_ : int = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowercase_ : Tuple = random_attention_mask([batch_size, 4] )
lowercase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
lowercase_ : Tuple = FlaxCLIPVisionModel(lowercase_ )
lowercase_ : Any = FlaxBertModel(lowercase_ )
return vision_model, text_model
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Union[str, Any] = FlaxCLIPVisionModelTester(self )
lowercase_ : Tuple = FlaxBertModelTester(self )
lowercase_ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
lowercase_ : Any = bert_model_tester.prepare_config_and_inputs()
lowercase_ , lowercase_ : Optional[Any] = vision_config_and_inputs
lowercase_ , lowercase_ , lowercase_ , lowercase_ : str = 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 __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : str ):
lowercase_ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
lowercase_ : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowercase_ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowercase_ : Optional[int] = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=lowercase_ , padding=lowercase_ , return_tensors="""np""" )
lowercase_ : List[str] = model(**lowercase_ )
# 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]) , )
lowercase_ : Optional[Any] = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowercase_ , atol=1E-3 ) )
| 21 | 1 |
'''simple docstring'''
class __magic_name__ :
def __init__( self : Optional[Any] ):
lowercase_ : Tuple = {}
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
print(self.vertex )
for i in self.vertex:
print(lowercase_ , """ -> """ , """ -> """.join([str(lowercase_ ) for j in self.vertex[i]] ) )
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : int , lowercase_ : int ):
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(lowercase_ )
else:
# else make a new vertex
lowercase_ : str = [to_vertex]
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
# visited array for storing already visited nodes
lowercase_ : Union[str, Any] = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : int , lowercase_ : list ):
# mark start vertex as visited
lowercase_ : Tuple = True
print(lowercase_ , end=""" """ )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(lowercase_ , lowercase_ )
if __name__ == "__main__":
_lowercase : Dict = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 21 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int=7 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=18 , lowercase_ : List[Any]=30 , lowercase_ : int=400 , lowercase_ : Dict=True , lowercase_ : List[Any]=None , lowercase_ : Dict=True , ):
lowercase_ : Tuple = size if size is not None else {"""height""": 18, """width""": 18}
lowercase_ : List[str] = parent
lowercase_ : Any = batch_size
lowercase_ : Optional[Any] = num_channels
lowercase_ : Tuple = image_size
lowercase_ : Optional[Any] = min_resolution
lowercase_ : Dict = max_resolution
lowercase_ : Optional[int] = do_resize
lowercase_ : Optional[Any] = size
lowercase_ : Union[str, Any] = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , """clusters""" ) )
self.assertTrue(hasattr(lowercase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase_ , """size""" ) )
self.assertTrue(hasattr(lowercase_ , """do_normalize""" ) )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
lowercase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = self.image_processing_class(**self.image_processor_dict )
lowercase_ : Union[str, Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , obj[key] ) )
else:
self.assertEqual(obj[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Union[str, Any] = os.path.join(lowercase_ , """image_processor.json""" )
image_processor_first.to_json_file(lowercase_ )
lowercase_ : Optional[Any] = self.image_processing_class.from_json_file(lowercase_ ).to_dict()
lowercase_ : Any = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(lowercase_ )
lowercase_ : Any = self.image_processing_class.from_pretrained(lowercase_ ).to_dict()
lowercase_ : List[str] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(lowercase_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , lowercase_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
pass
def lowerCamelCase ( ) -> Any:
lowercase_ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
lowercase_ : Any = Image.open(dataset[4]["""file"""] )
lowercase_ : Dict = Image.open(dataset[5]["""file"""] )
lowercase_ : int = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Optional[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
lowercase_ : Optional[int] = prepare_images()
# test non-batched
lowercase_ : str = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
lowercase_ : Tuple = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , lowercase_ )
# test batched
lowercase_ : List[str] = image_processing(lowercase_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
lowercase_ : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , lowercase_ )
| 21 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __magic_name__ :
UpperCamelCase__ = None
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = 1
UpperCamelCase__ = None
UpperCamelCase__ = False
UpperCamelCase__ = None
UpperCamelCase__ = None
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
return self.__class__(**{k: copy.deepcopy(lowercase_ ) for k, v in self.__dict__.items()} )
| 21 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCamelCase__ = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[str] = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""pt""" )
# Using `do_sample=False` to force deterministic output
lowercase_ : Union[str, Any] = text_generator("""This is a test""" , do_sample=lowercase_ )
self.assertEqual(
lowercase_ , [
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
] , )
lowercase_ : Optional[Any] = text_generator(["""This is a test""", """This is a second test"""] )
self.assertEqual(
lowercase_ , [
[
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
],
[
{
"""generated_text""": (
"""This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
""" oscope. oscope. FiliFili@@"""
)
}
],
] , )
lowercase_ : Dict = text_generator("""This is a test""" , do_sample=lowercase_ , num_return_sequences=2 , return_tensors=lowercase_ )
self.assertEqual(
lowercase_ , [
{"""generated_token_ids""": ANY(lowercase_ )},
{"""generated_token_ids""": ANY(lowercase_ )},
] , )
lowercase_ : Any = text_generator.model.config.eos_token_id
lowercase_ : List[Any] = """<pad>"""
lowercase_ : Dict = text_generator(
["""This is a test""", """This is a second test"""] , do_sample=lowercase_ , num_return_sequences=2 , batch_size=2 , return_tensors=lowercase_ , )
self.assertEqual(
lowercase_ , [
[
{"""generated_token_ids""": ANY(lowercase_ )},
{"""generated_token_ids""": ANY(lowercase_ )},
],
[
{"""generated_token_ids""": ANY(lowercase_ )},
{"""generated_token_ids""": ANY(lowercase_ )},
],
] , )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : List[str] = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""tf""" )
# Using `do_sample=False` to force deterministic output
lowercase_ : Tuple = text_generator("""This is a test""" , do_sample=lowercase_ )
self.assertEqual(
lowercase_ , [
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
] , )
lowercase_ : Optional[Any] = text_generator(["""This is a test""", """This is a second test"""] , do_sample=lowercase_ )
self.assertEqual(
lowercase_ , [
[
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
],
[
{
"""generated_text""": (
"""This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
""" Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
)
}
],
] , )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : int ):
lowercase_ : Tuple = TextGenerationPipeline(model=lowercase_ , tokenizer=lowercase_ )
return text_generator, ["This is a test", "Another test"]
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Optional[int] = """Hello I believe in"""
lowercase_ : Union[str, Any] = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""" )
lowercase_ : Union[str, Any] = text_generator(lowercase_ )
self.assertEqual(
lowercase_ , [{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}] , )
lowercase_ : int = text_generator(lowercase_ , stop_sequence=""" fe""" )
self.assertEqual(lowercase_ , [{"""generated_text""": """Hello I believe in fe"""}] )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Any , lowercase_ : Dict ):
lowercase_ : Any = text_generator.model
lowercase_ : Tuple = text_generator.tokenizer
lowercase_ : str = text_generator("""This is a test""" )
self.assertEqual(lowercase_ , [{"""generated_text""": ANY(lowercase_ )}] )
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test""" ) )
lowercase_ : str = text_generator("""This is a test""" , return_full_text=lowercase_ )
self.assertEqual(lowercase_ , [{"""generated_text""": ANY(lowercase_ )}] )
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""] )
lowercase_ : Tuple = pipeline(task="""text-generation""" , model=lowercase_ , tokenizer=lowercase_ , return_full_text=lowercase_ )
lowercase_ : Tuple = text_generator("""This is a test""" )
self.assertEqual(lowercase_ , [{"""generated_text""": ANY(lowercase_ )}] )
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""] )
lowercase_ : Optional[Any] = text_generator("""This is a test""" , return_full_text=lowercase_ )
self.assertEqual(lowercase_ , [{"""generated_text""": ANY(lowercase_ )}] )
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test""" ) )
lowercase_ : int = text_generator(["""This is great !""", """Something else"""] , num_return_sequences=2 , do_sample=lowercase_ )
self.assertEqual(
lowercase_ , [
[{"""generated_text""": ANY(lowercase_ )}, {"""generated_text""": ANY(lowercase_ )}],
[{"""generated_text""": ANY(lowercase_ )}, {"""generated_text""": ANY(lowercase_ )}],
] , )
if text_generator.tokenizer.pad_token is not None:
lowercase_ : Optional[Any] = text_generator(
["""This is great !""", """Something else"""] , num_return_sequences=2 , batch_size=2 , do_sample=lowercase_ )
self.assertEqual(
lowercase_ , [
[{"""generated_text""": ANY(lowercase_ )}, {"""generated_text""": ANY(lowercase_ )}],
[{"""generated_text""": ANY(lowercase_ )}, {"""generated_text""": ANY(lowercase_ )}],
] , )
with self.assertRaises(lowercase_ ):
lowercase_ : Dict = text_generator("""test""" , return_full_text=lowercase_ , return_text=lowercase_ )
with self.assertRaises(lowercase_ ):
lowercase_ : Union[str, Any] = text_generator("""test""" , return_full_text=lowercase_ , return_tensors=lowercase_ )
with self.assertRaises(lowercase_ ):
lowercase_ : Optional[int] = text_generator("""test""" , return_text=lowercase_ , return_tensors=lowercase_ )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
lowercase_ : Tuple = text_generator("""""" )
self.assertEqual(lowercase_ , [{"""generated_text""": ANY(lowercase_ )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
lowercase_ : List[Any] = text_generator("""""" )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
lowercase_ : Tuple = ["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
if (
tokenizer.model_max_length < 10000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator("""This is a test""" * 500 , max_new_tokens=20 )
lowercase_ : Union[str, Any] = text_generator("""This is a test""" * 500 , handle_long_generation="""hole""" , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(lowercase_ ):
text_generator(
"""This is a test""" * 500 , handle_long_generation="""hole""" , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self : Any ):
import torch
# Classic `model_kwargs`
lowercase_ : Any = pipeline(
model="""hf-internal-testing/tiny-random-bloom""" , model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
lowercase_ : int = pipe("""This is a test""" )
self.assertEqual(
lowercase_ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
lowercase_ : Tuple = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
lowercase_ : Tuple = pipe("""This is a test""" )
self.assertEqual(
lowercase_ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
lowercase_ : Any = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
lowercase_ : Optional[Any] = pipe("""This is a test""" )
self.assertEqual(
lowercase_ , [
{
"""generated_text""": (
"""This is a test test test test test test test test test test test test test test test test"""
""" test"""
)
}
] , )
@require_torch
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
import torch
lowercase_ : Optional[Any] = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device=0 , torch_dtype=torch.floataa )
pipe("""This is a test""" )
@require_torch
@require_accelerate
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
import torch
lowercase_ : Any = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.floataa )
pipe("""This is a test""" , do_sample=lowercase_ , top_p=0.5 )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : Union[str, Any] = """Hello world"""
lowercase_ : Any = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""" )
if text_generator.model.framework == "tf":
lowercase_ : int = logging.get_logger("""transformers.generation.tf_utils""" )
else:
lowercase_ : Union[str, Any] = logging.get_logger("""transformers.generation.utils""" )
lowercase_ : List[Any] = """Both `max_new_tokens`""" # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(lowercase_ ) as cl:
lowercase_ : List[Any] = text_generator(lowercase_ , max_length=10 , max_new_tokens=1 )
self.assertIn(lowercase_ , cl.out )
# The user only sets one -> no warning
with CaptureLogger(lowercase_ ) as cl:
lowercase_ : List[str] = text_generator(lowercase_ , max_new_tokens=1 )
self.assertNotIn(lowercase_ , cl.out )
with CaptureLogger(lowercase_ ) as cl:
lowercase_ : Optional[Any] = text_generator(lowercase_ , max_length=10 )
self.assertNotIn(lowercase_ , cl.out )
| 21 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ):
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : int = 1 , lowercase_ : int = 100 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[float] = None , lowercase_ : bool = True , ):
if audio_length_in_s is None:
lowercase_ : List[Any] = self.unet.config.sample_size / self.unet.config.sample_rate
lowercase_ : Dict = audio_length_in_s * self.unet.config.sample_rate
lowercase_ : Any = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowercase_ : List[Any] = int(lowercase_ )
if sample_size % down_scale_factor != 0:
lowercase_ : int = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
""" process.""" )
lowercase_ : Any = int(lowercase_ )
lowercase_ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowercase_ : List[str] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowercase_ : Any = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
# set step values
self.scheduler.set_timesteps(lowercase_ , device=audio.device )
lowercase_ : Optional[Any] = self.scheduler.timesteps.to(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ : Dict = self.unet(lowercase_ , lowercase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowercase_ : List[str] = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
lowercase_ : str = audio.clamp(-1 , 1 ).float().cpu().numpy()
lowercase_ : Union[str, Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowercase_ )
| 21 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase, unittest.TestCase):
UpperCamelCase__ = RobertaTokenizer
UpperCamelCase__ = RobertaTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = {'''cls_token''': '''<s>'''}
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase_ : List[str] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
lowercase_ : Tuple = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
lowercase_ : int = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
lowercase_ : Dict = {"""unk_token""": """<unk>"""}
lowercase_ : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowercase_ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowercase_ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : str , **lowercase_ : int ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , **lowercase_ : int ):
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Optional[Any] ):
lowercase_ : Dict = """lower newer"""
lowercase_ : int = """lower newer"""
return input_text, output_text
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : Dict = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowercase_ : Tuple = """lower newer"""
lowercase_ : Dict = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
lowercase_ : List[str] = tokenizer.tokenize(lowercase_ ) # , add_prefix_space=True)
self.assertListEqual(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = tokens + [tokenizer.unk_token]
lowercase_ : int = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : List[str] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("""Hello world!""" , add_special_tokens=lowercase_ ) , [0, 31414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("""Hello world! cécé herlolip 418""" , add_special_tokens=lowercase_ ) , [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2] , )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[Any] = self.tokenizer_class.from_pretrained("""roberta-base""" )
lowercase_ : Any = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Tuple = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.encode(
"""sequence builders""" , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
lowercase_ : Optional[Any] = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
lowercase_ : List[str] = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[str] = self.get_tokenizer()
lowercase_ : Tuple = """Encode this sequence."""
lowercase_ : str = tokenizer.byte_encoder[""" """.encode("""utf-8""" )[0]]
# Testing encoder arguments
lowercase_ : str = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
lowercase_ : Optional[int] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowercase_ , lowercase_ )
lowercase_ : List[str] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
lowercase_ : Dict = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowercase_ , lowercase_ )
tokenizer.add_special_tokens({"""bos_token""": """<s>"""} )
lowercase_ : Dict = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
lowercase_ : Tuple = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowercase_ , lowercase_ )
# Testing spaces after special tokens
lowercase_ : Dict = """<mask>"""
tokenizer.add_special_tokens(
{"""mask_token""": AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ )} ) # mask token has a left space
lowercase_ : Dict = tokenizer.convert_tokens_to_ids(lowercase_ )
lowercase_ : Dict = """Encode <mask> sequence"""
lowercase_ : int = """Encode <mask>sequence"""
lowercase_ : Optional[Any] = tokenizer.encode(lowercase_ )
lowercase_ : List[str] = encoded.index(lowercase_ )
lowercase_ : List[str] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowercase_ , lowercase_ )
lowercase_ : List[Any] = tokenizer.encode(lowercase_ )
lowercase_ : Optional[Any] = encoded.index(lowercase_ )
lowercase_ : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase_ : List[str] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase_ : Union[str, Any] = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase_ : Tuple = """A, <mask> AllenNLP sentence."""
lowercase_ : str = tokenizer_r.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
lowercase_ : Union[str, Any] = tokenizer_p.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) , sum(tokens_p["""token_type_ids"""] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) , sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) , )
lowercase_ : List[str] = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
lowercase_ : Dict = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["""input_ids"""] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(
lowercase_ , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
lowercase_ , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
lowercase_ : Dict = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowercase_ : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
lowercase_ : Optional[int] = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["""add_prefix_space"""] , lowercase_ )
self.assertEqual(post_processor_state["""add_prefix_space"""] , lowercase_ )
self.assertEqual(post_processor_state["""trim_offsets"""] , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase_ : Union[str, Any] = """hello""" # `hello` is a token in the vocabulary of `pretrained_name`
lowercase_ : List[str] = f'''{text_of_1_token} {text_of_1_token}'''
lowercase_ : Optional[int] = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowercase_ : List[Any] = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ) + 1, len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowercase_ : Tuple = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowercase_ : Optional[Any] = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ) + 1, len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowercase_ : int = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowercase_ : int = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ), len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowercase_ : List[str] = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowercase_ : Union[str, Any] = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase_ ), len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowercase_ : Union[str, Any] = f''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
lowercase_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowercase_ : List[str] = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase_ ) + 1, 1 + len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowercase_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowercase_ : List[str] = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase_ ), 1 + len(lowercase_ ) + 1 + len(lowercase_ )) , )
lowercase_ : Any = self.rust_tokenizer_class.from_pretrained(
lowercase_ , use_fast=lowercase_ , add_prefix_space=lowercase_ , trim_offsets=lowercase_ )
lowercase_ : Any = tokenizer_r(lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowercase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase_ ), 1 + len(lowercase_ ) + 1 + len(lowercase_ )) , )
| 21 | '''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
_lowercase : Union[str, Any] = "src/transformers"
_lowercase : str = "docs/source/en"
_lowercase : Union[str, Any] = "."
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] ) -> int:
with open(UpperCAmelCase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Union[str, Any] = f.readlines()
# Find the start prompt.
lowercase_ : Optional[Any] = 0
while not lines[start_index].startswith(UpperCAmelCase__ ):
start_index += 1
start_index += 1
lowercase_ : int = start_index
while not lines[end_index].startswith(UpperCAmelCase__ ):
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 |
_lowercase : int = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase : str = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase : Optional[Any] = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_lowercase : int = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
_lowercase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase ( UpperCAmelCase__ : int ) -> Any:
lowercase_ : str = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , UpperCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple ) -> List[Any]:
lowercase_ : Dict = 2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__ )
lowercase_ : List[str] = (width - text_length) // 2
lowercase_ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase ( ) -> Any:
lowercase_ : int = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowercase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowercase_ : int = {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_ : List[Any] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : List[str] = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Any = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Tuple = collections.defaultdict(UpperCAmelCase__ )
lowercase_ : Optional[int] = collections.defaultdict(UpperCAmelCase__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
lowercase_ : Optional[int] = slow_tokenizers
lowercase_ : Union[str, Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowercase_ : Optional[Any] = fast_tokenizers
lowercase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : str = tf_models
lowercase_ : str = _re_tf_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_flax_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : List[str] = flax_models
lowercase_ : int = _re_flax_models.match(UpperCAmelCase__ ).groups()[0]
elif _re_pt_models.match(UpperCAmelCase__ ) is not None:
lowercase_ : Tuple = pt_models
lowercase_ : Optional[int] = _re_pt_models.match(UpperCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(UpperCAmelCase__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowercase_ : int = True
break
# Try again after removing the last word in the name
lowercase_ : Optional[Any] = """""".join(camel_case_split(UpperCAmelCase__ )[:-1] )
# Let's build that table!
lowercase_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowercase_ : Optional[Any] = ["""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_ : Union[str, Any] = [len(UpperCAmelCase__ ) + 2 for c in columns]
lowercase_ : int = max([len(UpperCAmelCase__ ) for name in model_names] ) + 2
# Build the table per se
lowercase_ : Tuple = """|""" + """|""".join([_center_text(UpperCAmelCase__ , UpperCAmelCase__ ) for c, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowercase_ : int = {True: """✅""", False: """❌"""}
for name in model_names:
lowercase_ : str = model_name_to_prefix[name]
lowercase_ : Any = [
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(UpperCAmelCase__ , UpperCAmelCase__ ) for l, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )] ) + "|\n"
return table
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any]=False ) -> str:
lowercase_ , lowercase_ , lowercase_ , lowercase_ : List[str] = _find_text_in_file(
filename=os.path.join(UpperCAmelCase__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowercase_ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(UpperCAmelCase__ , """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__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 21 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : List[Any] ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None ):
lowercase_ : Optional[Any] = {}
lowercase_ : Tuple = {}
if prompt is not None:
lowercase_ : Tuple = prompt
if generate_kwargs is not None:
lowercase_ : List[str] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase_ : List[Any] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowercase_ : str = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[Any] , lowercase_ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ : Optional[int] ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[Any] , lowercase_ : Tuple=None ):
lowercase_ : List[Any] = load_image(lowercase_ )
if prompt is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowercase_ )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowercase_ : List[Any] = self.model.config.model_type
if model_type == "git":
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : Union[str, Any] = self.tokenizer(text=lowercase_ , add_special_tokens=lowercase_ ).input_ids
lowercase_ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase_ : List[Any] = torch.tensor(lowercase_ ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowercase_ : Union[str, Any] = self.image_processor(images=lowercase_ , header_text=lowercase_ , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase_ : Dict = self.image_processor(images=lowercase_ , return_tensors=self.framework )
lowercase_ : List[str] = self.tokenizer(lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowercase_ : List[str] = self.image_processor(images=lowercase_ , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase_ : str = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Dict , lowercase_ : Optional[Any]=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , lowercase_ )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowercase_ : Any = None
if generate_kwargs is None:
lowercase_ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase_ : Dict = model_inputs.pop(self.model.main_input_name )
lowercase_ : Any = self.model.generate(lowercase_ , **lowercase_ , **lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : List[Any] ):
lowercase_ : List[str] = []
for output_ids in model_outputs:
lowercase_ : Union[str, Any] = {
"""generated_text""": self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , )
}
records.append(lowercase_ )
return records
| 21 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : str = {
"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 __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''falcon'''
UpperCamelCase__ = ['''past_key_values''']
def __init__( self : Optional[Any] , lowercase_ : Tuple=65024 , lowercase_ : List[str]=4544 , lowercase_ : Optional[Any]=32 , lowercase_ : Any=71 , lowercase_ : Any=1E-5 , lowercase_ : Optional[Any]=0.02 , lowercase_ : Dict=True , lowercase_ : str=0.0 , lowercase_ : List[str]=0.0 , lowercase_ : Optional[Any]=None , lowercase_ : Dict=False , lowercase_ : str=False , lowercase_ : List[str]=True , lowercase_ : Union[str, Any]=True , lowercase_ : str=False , lowercase_ : Optional[int]=11 , lowercase_ : Tuple=11 , **lowercase_ : List[str] , ):
lowercase_ : Union[str, Any] = vocab_size
# Backward compatibility with n_embed kwarg
lowercase_ : Optional[int] = kwargs.pop("""n_embed""" , lowercase_ )
lowercase_ : Any = hidden_size if n_embed is None else n_embed
lowercase_ : Any = num_hidden_layers
lowercase_ : Optional[int] = num_attention_heads
lowercase_ : Union[str, Any] = layer_norm_epsilon
lowercase_ : int = initializer_range
lowercase_ : Optional[int] = use_cache
lowercase_ : int = hidden_dropout
lowercase_ : Any = attention_dropout
lowercase_ : List[str] = bos_token_id
lowercase_ : Tuple = eos_token_id
lowercase_ : Tuple = num_attention_heads if num_kv_heads is None else num_kv_heads
lowercase_ : Dict = alibi
lowercase_ : Any = new_decoder_architecture
lowercase_ : Dict = multi_query # Ignored when new_decoder_architecture is True
lowercase_ : Optional[Any] = parallel_attn
lowercase_ : List[str] = bias
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
return self.hidden_size // self.num_attention_heads
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
return not self.alibi
| 21 | '''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''blenderbot-small'''
UpperCamelCase__ = ['''past_key_values''']
UpperCamelCase__ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : Optional[int] , lowercase_ : Any=50265 , lowercase_ : Tuple=512 , lowercase_ : Tuple=8 , lowercase_ : Optional[int]=2048 , lowercase_ : Optional[int]=16 , lowercase_ : Dict=8 , lowercase_ : str=2048 , lowercase_ : List[Any]=16 , lowercase_ : Dict=0.0 , lowercase_ : int=0.0 , lowercase_ : Optional[Any]=True , lowercase_ : Tuple=True , lowercase_ : List[Any]="gelu" , lowercase_ : Optional[int]=512 , lowercase_ : Tuple=0.1 , lowercase_ : List[str]=0.0 , lowercase_ : int=0.0 , lowercase_ : Dict=0.02 , lowercase_ : Any=1 , lowercase_ : Any=False , lowercase_ : List[Any]=0 , lowercase_ : Tuple=1 , lowercase_ : int=2 , lowercase_ : Tuple=2 , **lowercase_ : List[Any] , ):
lowercase_ : Optional[int] = vocab_size
lowercase_ : Union[str, Any] = max_position_embeddings
lowercase_ : List[Any] = d_model
lowercase_ : Optional[int] = encoder_ffn_dim
lowercase_ : List[Any] = encoder_layers
lowercase_ : List[str] = encoder_attention_heads
lowercase_ : List[Any] = decoder_ffn_dim
lowercase_ : Optional[Any] = decoder_layers
lowercase_ : List[str] = decoder_attention_heads
lowercase_ : int = dropout
lowercase_ : Optional[int] = attention_dropout
lowercase_ : int = activation_dropout
lowercase_ : int = activation_function
lowercase_ : Any = init_std
lowercase_ : str = encoder_layerdrop
lowercase_ : List[str] = decoder_layerdrop
lowercase_ : Optional[int] = use_cache
lowercase_ : Any = encoder_layers
lowercase_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class __magic_name__ ( _UpperCAmelCase):
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
if self.task in ["default", "seq2seq-lm"]:
lowercase_ : List[Any] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
lowercase_ : Any = {0: """batch"""}
lowercase_ : List[Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
lowercase_ : List[str] = {0: """batch""", 1: """decoder_sequence"""}
lowercase_ : Optional[int] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase_ : List[str] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
lowercase_ , lowercase_ : Optional[Any] = self.num_layers
for i in range(lowercase_ ):
lowercase_ : List[Any] = {0: """batch""", 2: """past_sequence + sequence"""}
lowercase_ : Any = {0: """batch""", 2: """past_sequence + sequence"""}
else:
lowercase_ : str = 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
def SCREAMING_SNAKE_CASE_ ( self : str ):
if self.task in ["default", "seq2seq-lm"]:
lowercase_ : List[str] = super().outputs
else:
lowercase_ : str = super(lowercase_ , self ).outputs
if self.use_past:
lowercase_ , lowercase_ : int = self.num_layers
for i in range(lowercase_ ):
lowercase_ : str = {0: """batch""", 2: """past_sequence + sequence"""}
lowercase_ : str = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
lowercase_ : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
lowercase_ : Dict = seq_length if not self.use_past else 1
lowercase_ : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase_ : Any = {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
lowercase_ : Optional[int] = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowercase_ , lowercase_ : Optional[int] = common_inputs["""input_ids"""].shape
lowercase_ : str = common_inputs["""decoder_input_ids"""].shape[1]
lowercase_ , lowercase_ : Optional[int] = self.num_attention_heads
lowercase_ : Optional[Any] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase_ : Dict = decoder_seq_length + 3
lowercase_ : Tuple = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase_ : Optional[int] = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
lowercase_ : Union[str, Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase_ , lowercase_ : Dict = self.num_layers
lowercase_ : Any = min(lowercase_ , lowercase_ )
lowercase_ : Optional[Any] = max(lowercase_ , lowercase_ ) - min_num_layers
lowercase_ : int = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
lowercase_ : Union[str, Any] = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def SCREAMING_SNAKE_CASE_ ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
lowercase_ : Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowercase_ , lowercase_ : str = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowercase_ : List[str] = seqlen + 2
lowercase_ , lowercase_ : List[str] = self.num_layers
lowercase_ , lowercase_ : Optional[Any] = self.num_attention_heads
lowercase_ : Optional[Any] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase_ : Optional[Any] = common_inputs["""attention_mask"""].dtype
lowercase_ : List[Any] = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
lowercase_ : List[Any] = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase_ : Tuple = compute_effective_axis_dimension(
lowercase_ , 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
lowercase_ : Dict = tokenizer.num_special_tokens_to_add(lowercase_ )
lowercase_ : Optional[Any] = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
lowercase_ : List[str] = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase_ : str = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
if self.task in ["default", "seq2seq-lm"]:
lowercase_ : Tuple = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
elif self.task == "causal-lm":
lowercase_ : List[Any] = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
lowercase_ : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : str ):
if self.task in ["default", "seq2seq-lm"]:
lowercase_ : Tuple = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
lowercase_ : List[Any] = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
| 21 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
import random
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> list[Any]:
for _ in range(len(UpperCAmelCase__ ) ):
lowercase_ : Any = random.randint(0 , len(UpperCAmelCase__ ) - 1 )
lowercase_ : str = random.randint(0 , len(UpperCAmelCase__ ) - 1 )
lowercase_ , lowercase_ : Any = data[b], data[a]
return data
if __name__ == "__main__":
_lowercase : str = [0, 1, 2, 3, 4, 5, 6, 7]
_lowercase : Tuple = ["python", "says", "hello", "!"]
print("Fisher-Yates Shuffle:")
print("List", integers, strings)
print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 21 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
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
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
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
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = 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))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# 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.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > 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}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# 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
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 1 |
'''simple docstring'''
from torch import nn
class __magic_name__ ( nn.Module):
def __init__( self : Any , lowercase_ : Optional[int] , lowercase_ : Any ):
super().__init__()
lowercase_ : Optional[Any] = class_size
lowercase_ : List[str] = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
lowercase_ : Dict = nn.Linear(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : List[str] ):
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
lowercase_ : Any = self.mlp(lowercase_ )
return logits
| 21 | '''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
lowercase_ : Any = prime_factors(UpperCAmelCase__ )
if is_square_free(UpperCAmelCase__ ):
return -1 if len(UpperCAmelCase__ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
'''simple docstring'''
from math import loga
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : Optional[Any] , **lowercase_ : int ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ : Optional[int] = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase_ : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ : List[Any] = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ : List[Any] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Trace the models using torchscript'''})
UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
UpperCamelCase__ = field(
default='''O1''', metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
}, )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ : Optional[Any] = torch.device("""cpu""" )
lowercase_ : Tuple = 0
elif is_torch_tpu_available():
lowercase_ : Optional[int] = xm.xla_device()
lowercase_ : str = 0
else:
lowercase_ : int = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ : str = torch.cuda.device_count()
return device, n_gpu
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return is_torch_tpu_available() and self.tpu
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return self.n_gpu > 0
| 21 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __magic_name__ ( unittest.TestCase):
@parameterized.expand([(None,), ("""foo.json""",)] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , lowercase_ : str ):
lowercase_ : Union[str, Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : int = AutoConfig.from_pretrained("""gpt2""" )
lowercase_ : List[Any] = GenerationConfig.from_model_config(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[int] = GenerationConfig()
lowercase_ : int = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
lowercase_ : List[str] = copy.deepcopy(lowercase_ )
lowercase_ : Tuple = generation_config.update(**lowercase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {"""foo""": """bar"""} )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Dict = GenerationConfig()
lowercase_ : int = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowercase_ )
lowercase_ : Optional[int] = GenerationConfig.from_pretrained(lowercase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
lowercase_ : List[str] = GenerationConfig.from_model_config(lowercase_ )
assert not hasattr(lowercase_ , """foo""" ) # no new kwargs should be initialized if from config
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowercase_ )
self.assertEqual(default_config.num_beams , 1 )
lowercase_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowercase_ )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
lowercase_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowercase_ )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
lowercase_ : List[Any] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""test-generation-config""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
lowercase_ : List[Any] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
lowercase_ : Optional[Any] = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : int = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
| 21 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : int = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
lowercase_ : int = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase_ : Optional[Any] = model(lowercase_ )["""last_hidden_state"""]
lowercase_ : Tuple = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , lowercase_ )
# compare the actual values for a slice.
lowercase_ : Optional[int] = tf.convert_to_tensor(
[[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 21 | '''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] ) -> List[Any]:
# Initialise PyTorch model
lowercase_ : List[str] = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(F'''Building PyTorch model from configuration: {config}''' )
lowercase_ : Dict = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
_lowercase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 21 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(UpperCAmelCase__ , (list, tuple) ) or not all(
isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
lowercase_ : List[str] = numbers[0]
for i in range(1 , len(UpperCAmelCase__ ) ):
# update the maximum and minimum subarray products
lowercase_ : Tuple = numbers[i]
if number < 0:
lowercase_ , lowercase_ : Optional[Any] = min_till_now, max_till_now
lowercase_ : Union[str, Any] = max(UpperCAmelCase__ , max_till_now * number )
lowercase_ : List[Any] = min(UpperCAmelCase__ , min_till_now * number )
# update the maximum product found till now
lowercase_ : List[str] = max(UpperCAmelCase__ , UpperCAmelCase__ )
return max_prod
| 21 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Tuple , *lowercase_ : Optional[Any] , **lowercase_ : Tuple ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Optional[int] , **lowercase_ : Any ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : List[Any] , **lowercase_ : int ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : int , *lowercase_ : Any , **lowercase_ : Optional[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : List[Any] , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Dict , *lowercase_ : Dict , **lowercase_ : str ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : List[str] , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : Dict , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Optional[Any] , *lowercase_ : str , **lowercase_ : Optional[int] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : int , **lowercase_ : List[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : List[str] , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : List[Any] , *lowercase_ : int , **lowercase_ : Dict ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : Union[str, Any] , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[Any] , *lowercase_ : List[str] , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Union[str, Any] , *lowercase_ : int , **lowercase_ : Optional[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : int , **lowercase_ : Any ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Dict , *lowercase_ : Tuple , **lowercase_ : Dict ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : List[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : Optional[int] , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : int , *lowercase_ : List[str] , **lowercase_ : List[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Tuple , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Union[str, Any] , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Optional[int] , *lowercase_ : Dict , **lowercase_ : Optional[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : List[Any] , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Optional[int] , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Optional[int] , *lowercase_ : Any , **lowercase_ : Dict ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : Any , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[Any] , *lowercase_ : Optional[Any] , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Any , *lowercase_ : Any , **lowercase_ : List[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : Any , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : Union[str, Any] , **lowercase_ : int ):
requires_backends(cls , ["""torch"""] )
def lowerCamelCase ( *UpperCAmelCase__ : Tuple , **UpperCAmelCase__ : List[Any] ) -> Optional[Any]:
requires_backends(UpperCAmelCase__ , ["""torch"""] )
def lowerCamelCase ( *UpperCAmelCase__ : Dict , **UpperCAmelCase__ : Union[str, Any] ) -> str:
requires_backends(UpperCAmelCase__ , ["""torch"""] )
def lowerCamelCase ( *UpperCAmelCase__ : List[Any] , **UpperCAmelCase__ : Any ) -> Optional[Any]:
requires_backends(UpperCAmelCase__ , ["""torch"""] )
def lowerCamelCase ( *UpperCAmelCase__ : str , **UpperCAmelCase__ : Optional[int] ) -> str:
requires_backends(UpperCAmelCase__ , ["""torch"""] )
def lowerCamelCase ( *UpperCAmelCase__ : Optional[Any] , **UpperCAmelCase__ : Tuple ) -> Optional[int]:
requires_backends(UpperCAmelCase__ , ["""torch"""] )
def lowerCamelCase ( *UpperCAmelCase__ : int , **UpperCAmelCase__ : Optional[int] ) -> List[str]:
requires_backends(UpperCAmelCase__ , ["""torch"""] )
def lowerCamelCase ( *UpperCAmelCase__ : Optional[int] , **UpperCAmelCase__ : Optional[Any] ) -> int:
requires_backends(UpperCAmelCase__ , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Any , *lowercase_ : List[Any] , **lowercase_ : Any ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : str , **lowercase_ : List[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : str , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Dict , *lowercase_ : Any , **lowercase_ : Dict ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Any , **lowercase_ : int ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : str , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Tuple , *lowercase_ : Union[str, Any] , **lowercase_ : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Any , **lowercase_ : List[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : int , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : str , *lowercase_ : Dict , **lowercase_ : int ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : Tuple , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : List[Any] , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Tuple , *lowercase_ : int , **lowercase_ : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Tuple , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Any , *lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : int , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Optional[Any] , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : int , *lowercase_ : List[Any] , **lowercase_ : Tuple ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Optional[int] , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : List[Any] , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : List[str] , *lowercase_ : str , **lowercase_ : Any ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : str , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : int , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Union[str, Any] , *lowercase_ : List[Any] , **lowercase_ : Dict ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : Tuple , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Optional[int] , *lowercase_ : Any , **lowercase_ : List[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : Dict , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Union[str, Any] , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : int , *lowercase_ : int , **lowercase_ : Tuple ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : Tuple , **lowercase_ : int ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : Any , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : str , *lowercase_ : List[str] , **lowercase_ : Dict ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : int , **lowercase_ : Any ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : Dict , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Dict , *lowercase_ : Union[str, Any] , **lowercase_ : Tuple ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : Optional[Any] , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Optional[int] , **lowercase_ : Any ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Any , *lowercase_ : Optional[int] , **lowercase_ : int ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : Dict , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[Any] , *lowercase_ : List[Any] , **lowercase_ : List[Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : int , *lowercase_ : List[Any] , **lowercase_ : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : List[str] , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : Dict , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : int , *lowercase_ : List[str] , **lowercase_ : str ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : List[str] , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[Any] , *lowercase_ : List[Any] , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : str , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : Tuple , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : Optional[int] , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Tuple , *lowercase_ : List[str] , **lowercase_ : str ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : str , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : str , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Any , *lowercase_ : Tuple , **lowercase_ : List[str] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : Optional[Any] , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : List[Any] , **lowercase_ : int ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Optional[int] , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : List[Any] , **lowercase_ : Any ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : Any , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : List[Any] , *lowercase_ : List[Any] , **lowercase_ : Tuple ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : Union[str, Any] , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Tuple , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : List[str] , *lowercase_ : List[str] , **lowercase_ : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Union[str, Any] , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : Any , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Tuple , *lowercase_ : Tuple , **lowercase_ : int ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : Tuple , **lowercase_ : List[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : int , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Union[str, Any] , *lowercase_ : str , **lowercase_ : List[Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : List[Any] , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : Dict , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Optional[int] , *lowercase_ : Union[str, Any] , **lowercase_ : str ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : Optional[int] , **lowercase_ : int ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : str , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Tuple , *lowercase_ : Tuple , **lowercase_ : str ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : List[Any] , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : Tuple , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Union[str, Any] , *lowercase_ : Any , **lowercase_ : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict , *lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : Dict , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Any , *lowercase_ : List[Any] , **lowercase_ : Any ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Tuple , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Tuple , **lowercase_ : int ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Optional[int] , *lowercase_ : Any , **lowercase_ : Union[str, Any] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : Optional[Any] , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Any , *lowercase_ : Dict , **lowercase_ : List[Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : str , *lowercase_ : Union[str, Any] , **lowercase_ : Tuple ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : int , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Dict , *lowercase_ : Any , **lowercase_ : Optional[int] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : Union[str, Any] , **lowercase_ : Any ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : List[str] , **lowercase_ : Dict ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Optional[Any] , *lowercase_ : List[Any] , **lowercase_ : Any ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : int , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : str , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Union[str, Any] , *lowercase_ : List[Any] , **lowercase_ : Any ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] , *lowercase_ : Dict , **lowercase_ : str ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : Any , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : int , *lowercase_ : List[str] , **lowercase_ : int ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : Optional[Any] , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , *lowercase_ : Dict , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Union[str, Any] , *lowercase_ : List[str] , **lowercase_ : str ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : List[str] , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , *lowercase_ : str , **lowercase_ : Optional[Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Tuple , *lowercase_ : Tuple , **lowercase_ : List[str] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : str , **lowercase_ : int ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Dict , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Any , *lowercase_ : Optional[int] , **lowercase_ : Optional[int] ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[Any] , *lowercase_ : List[str] , **lowercase_ : Tuple ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Optional[Any] , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Any , *lowercase_ : List[Any] , **lowercase_ : str ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : str , **lowercase_ : Optional[int] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str , *lowercase_ : int , **lowercase_ : List[str] ):
requires_backends(cls , ["""torch"""] )
class __magic_name__ ( metaclass=_UpperCAmelCase):
UpperCamelCase__ = ['''torch''']
def __init__( self : Dict , *lowercase_ : List[Any] , **lowercase_ : Any ):
requires_backends(self , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , *lowercase_ : Tuple , **lowercase_ : List[Any] ):
requires_backends(cls , ["""torch"""] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , *lowercase_ : Any , **lowercase_ : Union[str, Any] ):
requires_backends(cls , ["""torch"""] )
| 21 | '''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = x
lowercase_ : Any = y
for step in range(UpperCAmelCase__ ): # noqa: B007
lowercase_ : Dict = a * a - b * b + x
lowercase_ : str = 2 * a * b + y
lowercase_ : Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def lowerCamelCase ( UpperCAmelCase__ : float ) -> tuple:
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def lowerCamelCase ( UpperCAmelCase__ : int = 800 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = -0.6 , UpperCAmelCase__ : float = 0 , UpperCAmelCase__ : float = 3.2 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : bool = True , ) -> Image.Image:
lowercase_ : Union[str, Any] = Image.new("""RGB""" , (image_width, image_height) )
lowercase_ : Tuple = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
lowercase_ : Any = figure_width / image_width * image_height
lowercase_ : Tuple = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase_ : Union[str, Any] = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase_ : str = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase_ : List[Any] = get_color_coded_rgb(UpperCAmelCase__ )
else:
lowercase_ : Dict = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
_lowercase : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 21 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''decision_transformer'''
UpperCamelCase__ = ['''past_key_values''']
UpperCamelCase__ = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Optional[Any] , lowercase_ : Dict=17 , lowercase_ : Tuple=4 , lowercase_ : Optional[int]=128 , lowercase_ : Optional[Any]=4096 , lowercase_ : Tuple=True , lowercase_ : Dict=1 , lowercase_ : Tuple=1024 , lowercase_ : Tuple=3 , lowercase_ : Optional[int]=1 , lowercase_ : Optional[int]=None , lowercase_ : List[str]="relu" , lowercase_ : List[str]=0.1 , lowercase_ : Dict=0.1 , lowercase_ : Tuple=0.1 , lowercase_ : List[str]=1E-5 , lowercase_ : Any=0.02 , lowercase_ : List[Any]=True , lowercase_ : Optional[int]=True , lowercase_ : Dict=50256 , lowercase_ : str=50256 , lowercase_ : Tuple=False , lowercase_ : str=False , **lowercase_ : str , ):
lowercase_ : Tuple = state_dim
lowercase_ : Optional[int] = act_dim
lowercase_ : str = hidden_size
lowercase_ : Dict = max_ep_len
lowercase_ : Any = action_tanh
lowercase_ : Union[str, Any] = vocab_size
lowercase_ : List[str] = n_positions
lowercase_ : Optional[int] = n_layer
lowercase_ : Tuple = n_head
lowercase_ : Any = n_inner
lowercase_ : Optional[Any] = activation_function
lowercase_ : List[str] = resid_pdrop
lowercase_ : str = embd_pdrop
lowercase_ : Dict = attn_pdrop
lowercase_ : List[Any] = layer_norm_epsilon
lowercase_ : Dict = initializer_range
lowercase_ : Tuple = scale_attn_weights
lowercase_ : Optional[Any] = use_cache
lowercase_ : Optional[int] = scale_attn_by_inverse_layer_idx
lowercase_ : Any = reorder_and_upcast_attn
lowercase_ : Optional[Any] = bos_token_id
lowercase_ : Optional[Any] = eos_token_id
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
| 21 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 1 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] ) -> str:
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , """IN_MEMORY_MAX_SIZE""" , UpperCAmelCase__ )
lowercase_ : List[str] = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
lowercase_ : List[Any] = dataset_size < in_memory_max_size
else:
lowercase_ : Optional[int] = False
lowercase_ : Any = is_small_dataset(UpperCAmelCase__ )
assert result == expected
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : str = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : List[str] , lowercase_ : Optional[int]=0.01 , lowercase_ : Any=1000 ):
lowercase_ : List[Any] = p_stop
lowercase_ : List[Any] = max_length
def __iter__( self : Any ):
lowercase_ : List[str] = 0
lowercase_ : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
lowercase_ : Optional[Any] = random.random() < self.p_stop
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : Union[str, Any] , lowercase_ : Tuple , lowercase_ : List[str]=False , lowercase_ : List[Any]=True ):
lowercase_ : List[str] = [
BatchSamplerShard(lowercase_ , 2 , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
for i in range(2 )
]
lowercase_ : Dict = [list(lowercase_ ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(lowercase_ ) for shard in batch_sampler_shards] , [len(lowercase_ ) for e in expected] )
self.assertListEqual(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# Check the shards when the dataset is a round multiple of total batch size.
lowercase_ : Optional[Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : Any = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
lowercase_ : Union[str, Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : int = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
lowercase_ : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
lowercase_ : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
# Check the shards when the dataset is very small.
lowercase_ : int = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Optional[Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
lowercase_ : Optional[int] = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Any = [[], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
# Check the shards when the dataset is a round multiple of batch size.
lowercase_ : Optional[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
lowercase_ : Union[str, Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size.
lowercase_ : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
lowercase_ : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
lowercase_ : str = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
lowercase_ : str = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : str = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
# Check the shards when the dataset is very small.
lowercase_ : int = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Tuple = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
lowercase_ : List[str] = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : List[str] = [[], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
# Check the shards when the dataset is a round multiple of total batch size.
lowercase_ : List[str] = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Tuple = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
lowercase_ : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
lowercase_ : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Union[str, Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
lowercase_ : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Dict = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is very small.
lowercase_ : Tuple = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : List[str] = [[[0, 1]], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
lowercase_ : Optional[int] = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase_ )
lowercase_ : Any = [[], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , even_batches=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
# Check the shards when the dataset is a round multiple of batch size.
lowercase_ : Tuple = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
lowercase_ : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size.
lowercase_ : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
lowercase_ : Optional[int] = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
lowercase_ : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : int = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
lowercase_ : Union[str, Any] = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : int = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
# Check the shards when the dataset is very small.
lowercase_ : Optional[Any] = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : int = [[[0, 1]], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
lowercase_ : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : str = [[], []]
self.check_batch_sampler_shards(lowercase_ , lowercase_ , split_batches=lowercase_ , even_batches=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : List[str] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
lowercase_ : str = [BatchSamplerShard(lowercase_ , 2 , lowercase_ , even_batches=lowercase_ ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def SCREAMING_SNAKE_CASE_ ( self : int , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Tuple=False , lowercase_ : Dict=2 , lowercase_ : Optional[int]=False ):
random.seed(lowercase_ )
lowercase_ : List[str] = list(lowercase_ )
lowercase_ : Optional[Any] = [
IterableDatasetShard(
lowercase_ , batch_size=lowercase_ , drop_last=lowercase_ , num_processes=lowercase_ , process_index=lowercase_ , split_batches=lowercase_ , )
for i in range(lowercase_ )
]
lowercase_ : List[str] = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(lowercase_ )
iterable_dataset_lists.append(list(lowercase_ ) )
lowercase_ : Dict = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
lowercase_ : str = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
self.assertTrue(len(lowercase_ ) % shard_batch_size == 0 )
lowercase_ : int = []
for idx in range(0 , len(lowercase_ ) , lowercase_ ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(lowercase_ ) < len(lowercase_ ):
reference += reference
self.assertListEqual(lowercase_ , reference[: len(lowercase_ )] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Tuple = 42
lowercase_ : Dict = RandomIterableDataset()
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
# Edge case with a very small dataset
lowercase_ : Tuple = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
self.check_iterable_dataset_shards(lowercase_ , lowercase_ , batch_size=4 , drop_last=lowercase_ , split_batches=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : List[Any] = BatchSampler(range(16 ) , batch_size=4 , drop_last=lowercase_ )
lowercase_ : int = SkipBatchSampler(lowercase_ , 2 )
self.assertListEqual(list(lowercase_ ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
lowercase_ : int = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Optional[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
lowercase_ : int = skip_first_batches(lowercase_ , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : int = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(lowercase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowercase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
Accelerator()
lowercase_ : Tuple = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(lowercase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowercase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowercase : str = 16
_lowercase : int = 32
def lowerCamelCase ( UpperCAmelCase__ : Accelerator , UpperCAmelCase__ : int = 16 ) -> str:
lowercase_ : List[str] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase_ : List[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCAmelCase__ : str ):
# max_length=None => use the model max length (it's actually the default)
lowercase_ : int = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCAmelCase__ , max_length=UpperCAmelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase_ : int = datasets.map(
UpperCAmelCase__ , batched=UpperCAmelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase_ : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCAmelCase__ : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase_ : Optional[int] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase_ : List[Any] = 16
elif accelerator.mixed_precision != "no":
lowercase_ : Optional[int] = 8
else:
lowercase_ : int = None
return tokenizer.pad(
UpperCAmelCase__ , padding="""longest""" , max_length=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase_ : str = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
lowercase_ : List[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowercase : List[str] = mocked_dataloaders # noqa: F811
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : Dict ) -> Optional[int]:
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCAmelCase__ ) == "1":
lowercase_ : Dict = 2
# Initialize accelerator
lowercase_ : List[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase_ : Optional[int] = config["""lr"""]
lowercase_ : str = int(config["""num_epochs"""] )
lowercase_ : int = int(config["""seed"""] )
lowercase_ : List[str] = int(config["""batch_size"""] )
lowercase_ : Optional[int] = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
lowercase_ : str = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowercase_ : Optional[int] = batch_size // MAX_GPU_BATCH_SIZE
lowercase_ : Dict = MAX_GPU_BATCH_SIZE
set_seed(UpperCAmelCase__ )
lowercase_ , lowercase_ : List[Any] = get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase_ : Tuple = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCAmelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase_ : Dict = model.to(accelerator.device )
# Instantiate optimizer
lowercase_ : Union[str, Any] = AdamW(params=model.parameters() , lr=UpperCAmelCase__ )
# Instantiate scheduler
lowercase_ : int = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCAmelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ : Optional[Any] = accelerator.prepare(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Now we train the model
for epoch in range(UpperCAmelCase__ ):
model.train()
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase_ : Optional[Any] = model(**UpperCAmelCase__ )
lowercase_ : Dict = outputs.loss
lowercase_ : List[Any] = loss / gradient_accumulation_steps
accelerator.backward(UpperCAmelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
lowercase_ : List[Any] = 0
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase_ : Tuple = model(**UpperCAmelCase__ )
lowercase_ : str = outputs.logits.argmax(dim=-1 )
lowercase_ , lowercase_ : Optional[Any] = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(UpperCAmelCase__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
lowercase_ : Any = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowercase_ : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=UpperCAmelCase__ , references=UpperCAmelCase__ , )
lowercase_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , UpperCAmelCase__ )
def lowerCamelCase ( ) -> Optional[Any]:
lowercase_ : Any = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowercase_ : Any = parser.parse_args()
lowercase_ : str = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 21 | '''simple docstring'''
import os
import numpy
import onnx
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple:
lowercase_ : Tuple = a.name
lowercase_ : Tuple = b.name
lowercase_ : Any = """"""
lowercase_ : List[Any] = """"""
lowercase_ : List[Any] = a == b
lowercase_ : Union[str, Any] = name_a
lowercase_ : Optional[Any] = name_b
return res
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int:
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]:
lowercase_ : int = list(model.graph.initializer )
lowercase_ : List[str] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase_ : Optional[Any] = inits[i].name
lowercase_ : List[str] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]:
lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ )
lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ )
lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase_ : List[Any] = list(model.graph.initializer )
lowercase_ : int = set()
lowercase_ : int = {}
lowercase_ : str = []
lowercase_ : int = 0
for i in range(len(UpperCAmelCase__ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase__ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase__ )
dup_set.add(UpperCAmelCase__ )
lowercase_ : Dict = inits[j].data_type
lowercase_ : List[str] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("""unexpected data type: """ , UpperCAmelCase__ )
total_reduced_size += mem_size
lowercase_ : int = inits[i].name
lowercase_ : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase__ )
else:
lowercase_ : Optional[int] = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" )
lowercase_ : Tuple = sorted(UpperCAmelCase__ )
_remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : Union[str, Any] = """optimized_""" + model_file_name
lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
onnx.save(UpperCAmelCase__ , UpperCAmelCase__ )
return new_model
| 21 | 1 |
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