code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = [int(_UpperCAmelCase ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(_UpperCAmelCase ) == 4 and all(0 <= int(_UpperCAmelCase ) <= 254 for octet in octets )
if __name__ == "__main__":
_lowerCamelCase : str = input().strip()
_lowerCamelCase : Any = 'valid' if is_ip_va_address_valid(ip) else 'invalid'
print(f'{ip} is a {valid_or_invalid} IP v4 address.')
| 167 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {
'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}
class lowercase ( __UpperCAmelCase , __UpperCAmelCase):
__lowerCAmelCase : List[Any] = """convnextv2"""
def __init__( self : int , _lowerCamelCase : str=3 , _lowerCamelCase : str=4 , _lowerCamelCase : List[Any]=4 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Optional[int]="gelu" , _lowerCamelCase : Union[str, Any]=0.02 , _lowerCamelCase : List[str]=1E-12 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Optional[int]=2_24 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Optional[Any]=None , **_lowerCamelCase : Optional[Any] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : str = num_channels
A_ : int = patch_size
A_ : Union[str, Any] = num_stages
A_ : Any = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
A_ : Any = [3, 3, 9, 3] if depths is None else depths
A_ : Optional[int] = hidden_act
A_ : Tuple = initializer_range
A_ : int = layer_norm_eps
A_ : List[Any] = drop_path_rate
A_ : Union[str, Any] = image_size
A_ : Any = ['''stem'''] + [F"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
A_ , A_ : Tuple = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
| 167 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/config.json""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/config.json""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"""
),
"""distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json""",
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"""
),
"""distilbert-base-uncased-finetuned-sst-2-english""": (
"""https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"""
),
}
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''distilbert'''
lowerCamelCase_ = {
'''hidden_size''': '''dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
}
def __init__( self , lowercase=3_0_5_2_2 , lowercase=5_1_2 , lowercase=False , lowercase=6 , lowercase=1_2 , lowercase=7_6_8 , lowercase=4 * 7_6_8 , lowercase=0.1 , lowercase=0.1 , lowercase="gelu" , lowercase=0.02 , lowercase=0.1 , lowercase=0.2 , lowercase=0 , **lowercase , ):
"""simple docstring"""
A_ : str = vocab_size
A_ : Tuple = max_position_embeddings
A_ : Dict = sinusoidal_pos_embds
A_ : Tuple = n_layers
A_ : List[Any] = n_heads
A_ : Any = dim
A_ : Union[str, Any] = hidden_dim
A_ : Dict = dropout
A_ : List[str] = attention_dropout
A_ : List[Any] = activation
A_ : str = initializer_range
A_ : Optional[Any] = qa_dropout
A_ : List[str] = seq_classif_dropout
super().__init__(**lowercase , pad_token_id=lowercase )
class UpperCAmelCase ( __A ):
'''simple docstring'''
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
A_ : Optional[int] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A_ : Optional[int] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 350 | import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
_UpperCAmelCase = """python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("""importlib_metadata""")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def UpperCamelCase ( __lowercase : str ,__lowercase : Dict=None ):
'''simple docstring'''
require_version(deps[pkg] ,__lowercase )
| 192 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[int]=False ):
"""simple docstring"""
__a = OmegaConf.load(_SCREAMING_SNAKE_CASE )
if display:
print(yaml.dump(OmegaConf.to_container(_SCREAMING_SNAKE_CASE ) ) )
return config
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict=None , _SCREAMING_SNAKE_CASE : Tuple=None ):
"""simple docstring"""
if conf_path is None:
__a = '''./model_checkpoints/vqgan_only.yaml'''
__a = load_config(_SCREAMING_SNAKE_CASE , display=_SCREAMING_SNAKE_CASE )
__a = VQModel(**config.model.params )
if ckpt_path is None:
__a = '''./model_checkpoints/vqgan_only.pt'''
__a = torch.load(_SCREAMING_SNAKE_CASE , map_location=_SCREAMING_SNAKE_CASE )
if ".ckpt" in ckpt_path:
__a = sd['''state_dict''']
model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
del sd
return model
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
__a = model.encode(_SCREAMING_SNAKE_CASE )
print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
__a = model.decode(_SCREAMING_SNAKE_CASE )
return xrec
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[Any]=False ):
"""simple docstring"""
__a = string.rsplit(""".""" , 1 )
if reload:
__a = importlib.import_module(_SCREAMING_SNAKE_CASE )
importlib.reload(_SCREAMING_SNAKE_CASE )
return getattr(importlib.import_module(_SCREAMING_SNAKE_CASE , package=_SCREAMING_SNAKE_CASE ) , cls )
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
if "target" not in config:
raise KeyError("""Expected key `target` to instantiate.""" )
return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) )
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Any=True , _SCREAMING_SNAKE_CASE : Any=True ):
"""simple docstring"""
__a = instantiate_from_config(_SCREAMING_SNAKE_CASE )
if sd is not None:
model.load_state_dict(_SCREAMING_SNAKE_CASE )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
if ckpt:
__a = torch.load(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
__a = pl_sd['''global_step''']
print(f"loaded model from global step {global_step}." )
else:
__a = {'''state_dict''': None}
__a = None
__a = load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=_SCREAMING_SNAKE_CASE , eval_mode=_SCREAMING_SNAKE_CASE )['''model''']
return model, global_step
| 302 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase_ = KandinskyVaaPriorPipeline
lowerCAmelCase_ = ['''prompt''']
lowerCAmelCase_ = ['''prompt''', '''negative_prompt''']
lowerCAmelCase_ = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
lowerCAmelCase_ = False
@property
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
return 32
@property
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
return 32
@property
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
return self.time_input_dim
@property
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
return 100
@property
def UpperCAmelCase__ ( self : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(_A )
@property
def UpperCAmelCase__ ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Dict = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 12,
'''embedding_dim''': self.text_embedder_hidden_size,
'''num_layers''': 1,
}
__SCREAMING_SNAKE_CASE : Optional[Any] = PriorTransformer(**_A )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : int = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__SCREAMING_SNAKE_CASE : str = CLIPVisionModelWithProjection(_A )
return model
@property
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = CLIPImageProcessor(
crop_size=224 , do_center_crop=_A , do_normalize=_A , do_resize=_A , image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , resample=3 , size=224 , )
return image_processor
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = self.dummy_prior
__SCREAMING_SNAKE_CASE : str = self.dummy_image_encoder
__SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_text_encoder
__SCREAMING_SNAKE_CASE : List[Any] = self.dummy_tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_image_processor
__SCREAMING_SNAKE_CASE : str = UnCLIPScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1000 , clip_sample=_A , clip_sample_range=10.0 , )
__SCREAMING_SNAKE_CASE : int = {
'''prior''': prior,
'''image_encoder''': image_encoder,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''scheduler''': scheduler,
'''image_processor''': image_processor,
}
return components
def UpperCAmelCase__ ( self : Union[str, Any] , _A : int , _A : Dict=0 ):
"""simple docstring"""
if str(_A ).startswith('''mps''' ):
__SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(_A )
else:
__SCREAMING_SNAKE_CASE : str = torch.Generator(device=_A ).manual_seed(_A )
__SCREAMING_SNAKE_CASE : List[str] = {
'''prompt''': '''horse''',
'''generator''': generator,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = '''cpu'''
__SCREAMING_SNAKE_CASE : Any = self.get_dummy_components()
__SCREAMING_SNAKE_CASE : Any = self.pipeline_class(**_A )
__SCREAMING_SNAKE_CASE : List[Any] = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__SCREAMING_SNAKE_CASE : int = pipe(**self.get_dummy_inputs(_A ) )
__SCREAMING_SNAKE_CASE : Tuple = output.image_embeds
__SCREAMING_SNAKE_CASE : Optional[Any] = pipe(
**self.get_dummy_inputs(_A ) , return_dict=_A , )[0]
__SCREAMING_SNAKE_CASE : Tuple = image[0, -10:]
__SCREAMING_SNAKE_CASE : List[Any] = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__SCREAMING_SNAKE_CASE : List[str] = np.array(
[-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = torch_device == '''cpu'''
__SCREAMING_SNAKE_CASE : Any = True
__SCREAMING_SNAKE_CASE : int = False
self._test_inference_batch_single_identical(
test_max_difference=_A , relax_max_difference=_A , test_mean_pixel_difference=_A , )
@skip_mps
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = torch_device == '''cpu'''
__SCREAMING_SNAKE_CASE : List[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=_A , test_mean_pixel_difference=_A , )
| 303 | 0 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def A ( snake_case :List[Any] , snake_case :Dict=1 ) -> Optional[int]:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_prefix_segments:] )
else:
return ".".join(path.split('.' )[:n_shave_prefix_segments] )
def A ( snake_case :Dict , snake_case :int=0 ) -> Optional[int]:
__UpperCamelCase = []
for old_item in old_list:
__UpperCamelCase = old_item.replace('in_layers.0' , 'norm1' )
__UpperCamelCase = new_item.replace('in_layers.2' , 'conv1' )
__UpperCamelCase = new_item.replace('out_layers.0' , 'norm2' )
__UpperCamelCase = new_item.replace('out_layers.3' , 'conv2' )
__UpperCamelCase = new_item.replace('emb_layers.1' , 'time_emb_proj' )
__UpperCamelCase = new_item.replace('skip_connection' , 'conv_shortcut' )
__UpperCamelCase = shave_segments(snake_case , n_shave_prefix_segments=snake_case )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def A ( snake_case :Optional[Any] , snake_case :Tuple=0 ) -> Tuple:
__UpperCamelCase = []
for old_item in old_list:
__UpperCamelCase = old_item
__UpperCamelCase = new_item.replace('norm.weight' , 'group_norm.weight' )
__UpperCamelCase = new_item.replace('norm.bias' , 'group_norm.bias' )
__UpperCamelCase = new_item.replace('proj_out.weight' , 'proj_attn.weight' )
__UpperCamelCase = new_item.replace('proj_out.bias' , 'proj_attn.bias' )
__UpperCamelCase = shave_segments(snake_case , n_shave_prefix_segments=snake_case )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def A ( snake_case :int , snake_case :List[str] , snake_case :List[str] , snake_case :Any=None , snake_case :Optional[int]=None , snake_case :Union[str, Any]=None ) -> Optional[int]:
assert isinstance(snake_case , snake_case ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
__UpperCamelCase = old_checkpoint[path]
__UpperCamelCase = old_tensor.shape[0] // 3
__UpperCamelCase = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
__UpperCamelCase = old_tensor.shape[0] // config['num_head_channels'] // 3
__UpperCamelCase = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = old_tensor.split(channels // num_heads , dim=1 )
__UpperCamelCase = query.reshape(snake_case )
__UpperCamelCase = key.reshape(snake_case )
__UpperCamelCase = value.reshape(snake_case )
for path in paths:
__UpperCamelCase = path['new']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
__UpperCamelCase = new_path.replace('middle_block.0' , 'mid_block.resnets.0' )
__UpperCamelCase = new_path.replace('middle_block.1' , 'mid_block.attentions.0' )
__UpperCamelCase = new_path.replace('middle_block.2' , 'mid_block.resnets.1' )
if additional_replacements is not None:
for replacement in additional_replacements:
__UpperCamelCase = new_path.replace(replacement['old'] , replacement['new'] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
__UpperCamelCase = old_checkpoint[path['old']][:, :, 0]
else:
__UpperCamelCase = old_checkpoint[path['old']]
def A ( snake_case :Optional[Any] , snake_case :Dict ) -> Optional[Any]:
__UpperCamelCase = {}
__UpperCamelCase = checkpoint['time_embed.0.weight']
__UpperCamelCase = checkpoint['time_embed.0.bias']
__UpperCamelCase = checkpoint['time_embed.2.weight']
__UpperCamelCase = checkpoint['time_embed.2.bias']
__UpperCamelCase = checkpoint['input_blocks.0.0.weight']
__UpperCamelCase = checkpoint['input_blocks.0.0.bias']
__UpperCamelCase = checkpoint['out.0.weight']
__UpperCamelCase = checkpoint['out.0.bias']
__UpperCamelCase = checkpoint['out.2.weight']
__UpperCamelCase = checkpoint['out.2.bias']
# Retrieves the keys for the input blocks only
__UpperCamelCase = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'input_blocks' in layer} )
__UpperCamelCase = {
layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
for layer_id in range(snake_case )
}
# Retrieves the keys for the middle blocks only
__UpperCamelCase = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'middle_block' in layer} )
__UpperCamelCase = {
layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
for layer_id in range(snake_case )
}
# Retrieves the keys for the output blocks only
__UpperCamelCase = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'output_blocks' in layer} )
__UpperCamelCase = {
layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
for layer_id in range(snake_case )
}
for i in range(1 , snake_case ):
__UpperCamelCase = (i - 1) // (config['num_res_blocks'] + 1)
__UpperCamelCase = (i - 1) % (config['num_res_blocks'] + 1)
__UpperCamelCase = [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
__UpperCamelCase = [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
if f'input_blocks.{i}.0.op.weight' in checkpoint:
__UpperCamelCase = checkpoint[
f'input_blocks.{i}.0.op.weight'
]
__UpperCamelCase = checkpoint[
f'input_blocks.{i}.0.op.bias'
]
continue
__UpperCamelCase = renew_resnet_paths(snake_case )
__UpperCamelCase = {'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
__UpperCamelCase = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
assign_to_checkpoint(
snake_case , snake_case , snake_case , additional_replacements=[meta_path, resnet_op] , config=snake_case )
if len(snake_case ):
__UpperCamelCase = renew_attention_paths(snake_case )
__UpperCamelCase = {
'old': f'input_blocks.{i}.1',
'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
}
__UpperCamelCase = {
f'input_blocks.{i}.1.qkv.bias': {
'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
f'input_blocks.{i}.1.qkv.weight': {
'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
snake_case , snake_case , snake_case , additional_replacements=[meta_path] , attention_paths_to_split=snake_case , config=snake_case , )
__UpperCamelCase = middle_blocks[0]
__UpperCamelCase = middle_blocks[1]
__UpperCamelCase = middle_blocks[2]
__UpperCamelCase = renew_resnet_paths(snake_case )
assign_to_checkpoint(snake_case , snake_case , snake_case , config=snake_case )
__UpperCamelCase = renew_resnet_paths(snake_case )
assign_to_checkpoint(snake_case , snake_case , snake_case , config=snake_case )
__UpperCamelCase = renew_attention_paths(snake_case )
__UpperCamelCase = {
'middle_block.1.qkv.bias': {
'key': 'mid_block.attentions.0.key.bias',
'query': 'mid_block.attentions.0.query.bias',
'value': 'mid_block.attentions.0.value.bias',
},
'middle_block.1.qkv.weight': {
'key': 'mid_block.attentions.0.key.weight',
'query': 'mid_block.attentions.0.query.weight',
'value': 'mid_block.attentions.0.value.weight',
},
}
assign_to_checkpoint(
snake_case , snake_case , snake_case , attention_paths_to_split=snake_case , config=snake_case )
for i in range(snake_case ):
__UpperCamelCase = i // (config['num_res_blocks'] + 1)
__UpperCamelCase = i % (config['num_res_blocks'] + 1)
__UpperCamelCase = [shave_segments(snake_case , 2 ) for name in output_blocks[i]]
__UpperCamelCase = {}
for layer in output_block_layers:
__UpperCamelCase , __UpperCamelCase = layer.split('.' )[0], shave_segments(snake_case , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(snake_case )
else:
__UpperCamelCase = [layer_name]
if len(snake_case ) > 1:
__UpperCamelCase = [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
__UpperCamelCase = [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
__UpperCamelCase = renew_resnet_paths(snake_case )
__UpperCamelCase = renew_resnet_paths(snake_case )
__UpperCamelCase = {'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
assign_to_checkpoint(snake_case , snake_case , snake_case , additional_replacements=[meta_path] , config=snake_case )
if ["conv.weight", "conv.bias"] in output_block_list.values():
__UpperCamelCase = list(output_block_list.values() ).index(['conv.weight', 'conv.bias'] )
__UpperCamelCase = checkpoint[
f'output_blocks.{i}.{index}.conv.weight'
]
__UpperCamelCase = checkpoint[
f'output_blocks.{i}.{index}.conv.bias'
]
# Clear attentions as they have been attributed above.
if len(snake_case ) == 2:
__UpperCamelCase = []
if len(snake_case ):
__UpperCamelCase = renew_attention_paths(snake_case )
__UpperCamelCase = {
'old': f'output_blocks.{i}.1',
'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
}
__UpperCamelCase = {
f'output_blocks.{i}.1.qkv.bias': {
'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
f'output_blocks.{i}.1.qkv.weight': {
'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
snake_case , snake_case , snake_case , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any('qkv' in key for key in attentions ) else None , config=snake_case , )
else:
__UpperCamelCase = renew_resnet_paths(snake_case , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
__UpperCamelCase = '.'.join(['output_blocks', str(snake_case ), path['old']] )
__UpperCamelCase = '.'.join(['up_blocks', str(snake_case ), 'resnets', str(snake_case ), path['new']] )
__UpperCamelCase = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : Optional[Any] = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
UpperCamelCase : int = json.loads(f.read())
UpperCamelCase : Dict = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
UpperCamelCase : Optional[Any] = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
UpperCamelCase : List[Any] = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
UpperCamelCase : Optional[int] = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))
UpperCamelCase : Dict = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 351 |
"""simple docstring"""
from math import isqrt
def A ( snake_case :int ) -> list[int]:
__UpperCamelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , snake_case ):
__UpperCamelCase = False
return [i for i in range(2 , snake_case ) if is_prime[i]]
def A ( snake_case :int = 1_0**8 ) -> int:
__UpperCamelCase = calculate_prime_numbers(max_number // 2 )
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = len(snake_case ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f'''{solution() = }''')
| 263 | 0 |
import datasets
__A = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n and Stoyanov, Veselin\",\n title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n in Natural Language Processing\",\n year = \"2018\",\n publisher = \"Association for Computational Linguistics\",\n location = \"Brussels, Belgium\",\n}\n"
__A = "\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"
__A = "\nComputes XNLI score which is just simple accuracy.\nArgs:\n predictions: Predicted labels.\n references: Ground truth labels.\nReturns:\n 'accuracy': accuracy\nExamples:\n\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> xnli_metric = datasets.load_metric(\"xnli\")\n >>> results = xnli_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n"
def lowerCAmelCase_ ( __a , __a ) -> List[str]:
"""simple docstring"""
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : int) ->Union[str, Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
"references": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
}) , codebase_urls=[] , reference_urls=[] , format="numpy" , )
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str]) ->Union[str, Any]:
'''simple docstring'''
return {"accuracy": simple_accuracy(UpperCAmelCase_ , UpperCAmelCase_)}
| 10 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _UpperCamelCase ( ):
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
UpperCAmelCase__ = """__test_patch_submodule_mock__"""
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def _UpperCamelCase ( ):
'''simple docstring'''
assert _test_patching.open is open
UpperCAmelCase__ = """__test_patch_submodule_builtin_mock__"""
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = """__test_patch_submodule_missing_mock__"""
with patch_submodule(_test_patching , """pandas.read_csv""" , SCREAMING_SNAKE_CASE__ ):
pass
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = """__test_patch_submodule_missing_builtin_mock__"""
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ) is None
with patch_submodule(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = """__test_patch_submodule_start_and_stop_mock__"""
UpperCAmelCase__ = patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def _UpperCamelCase ( ):
'''simple docstring'''
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
UpperCAmelCase__ = """__test_patch_submodule_successive_join__"""
UpperCAmelCase__ = """__test_patch_submodule_successive_dirname__"""
UpperCAmelCase__ = """__test_patch_submodule_successive_rename__"""
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ):
with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ):
with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ):
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ):
with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = """__test_patch_submodule_doesnt_exist_mock__"""
with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ):
pass
with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ):
pass
| 346 | 0 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
A : int = logging.getLogger(__name__)
require_version("pytorch_lightning>=1.0.4")
A : Union[str, Any] = {
"base": AutoModel,
"sequence-classification": AutoModelForSequenceClassification,
"question-answering": AutoModelForQuestionAnswering,
"pretraining": AutoModelForPreTraining,
"token-classification": AutoModelForTokenClassification,
"language-modeling": AutoModelWithLMHead,
"summarization": AutoModelForSeqaSeqLM,
"translation": AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
A : Tuple = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
A : Optional[int] = sorted(arg_to_scheduler.keys())
A : str = "{" + ", ".join(arg_to_scheduler_choices) + "}"
class lowerCamelCase (pl.LightningModule ):
"""simple docstring"""
def __init__( self : int , __magic_name__ : argparse.Namespace , __magic_name__ : List[Any]=None , __magic_name__ : Union[str, Any]="base" , __magic_name__ : List[str]=None , __magic_name__ : List[Any]=None , __magic_name__ : str=None , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(__magic_name__ )
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = Path(self.hparams.output_dir )
SCREAMING_SNAKE_CASE_ = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({"num_labels": num_labels} if num_labels is not None else {}) , cache_dir=__magic_name__ , **__magic_name__ , )
else:
SCREAMING_SNAKE_CASE_ = config
SCREAMING_SNAKE_CASE_ = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(self.hparams , __magic_name__ , __magic_name__ ):
assert hasattr(self.config , __magic_name__ ), F'''model config doesn\'t have a `{p}` attribute'''
setattr(self.config , __magic_name__ , getattr(self.hparams , __magic_name__ ) )
if tokenizer is None:
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=__magic_name__ , )
else:
SCREAMING_SNAKE_CASE_ = tokenizer
SCREAMING_SNAKE_CASE_ = MODEL_MODES[mode]
if model is None:
SCREAMING_SNAKE_CASE_ = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool(".ckpt" in self.hparams.model_name_or_path ) , config=self.config , cache_dir=__magic_name__ , )
else:
SCREAMING_SNAKE_CASE_ = model
def __A ( self : int , *__magic_name__ : List[Any] , **__magic_name__ : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = self.model_type.from_pretrained(*__magic_name__ , **__magic_name__ )
def __A ( self : Optional[Any] ) -> Dict:
SCREAMING_SNAKE_CASE_ = arg_to_scheduler[self.hparams.lr_scheduler]
SCREAMING_SNAKE_CASE_ = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
SCREAMING_SNAKE_CASE_ = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return scheduler
def __A ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.model
SCREAMING_SNAKE_CASE_ = ["bias", "LayerNorm.weight"]
SCREAMING_SNAKE_CASE_ = [
{
"params": [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
"weight_decay": self.hparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
if self.hparams.adafactor:
SCREAMING_SNAKE_CASE_ = Adafactor(
__magic_name__ , lr=self.hparams.learning_rate , scale_parameter=__magic_name__ , relative_step=__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = AdamW(
__magic_name__ , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
SCREAMING_SNAKE_CASE_ = optimizer
SCREAMING_SNAKE_CASE_ = self.get_lr_scheduler()
return [optimizer], [scheduler]
def __A ( self : int , __magic_name__ : List[Any] , __magic_name__ : Any ) -> Optional[int]:
return self.validation_step(__magic_name__ , __magic_name__ )
def __A ( self : Dict , __magic_name__ : Any ) -> List[Any]:
return self.validation_end(__magic_name__ )
def __A ( self : Dict ) -> int:
SCREAMING_SNAKE_CASE_ = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
SCREAMING_SNAKE_CASE_ = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def __A ( self : Dict , __magic_name__ : str ) -> Union[str, Any]:
if stage == "test":
SCREAMING_SNAKE_CASE_ = len(self.test_dataloader().dataset )
else:
SCREAMING_SNAKE_CASE_ = self.get_dataloader("train" , self.hparams.train_batch_size , shuffle=__magic_name__ )
SCREAMING_SNAKE_CASE_ = len(self.train_dataloader().dataset )
def __A ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : int , __magic_name__ : bool = False ) -> Dict:
raise NotImplementedError("You must implement this for your task" )
def __A ( self : Tuple ) -> List[str]:
return self.train_loader
def __A ( self : Optional[int] ) -> Optional[int]:
return self.get_dataloader("dev" , self.hparams.eval_batch_size , shuffle=__magic_name__ )
def __A ( self : str ) -> Union[str, Any]:
return self.get_dataloader("test" , self.hparams.eval_batch_size , shuffle=__magic_name__ )
def __A ( self : Optional[int] , __magic_name__ : Tuple ) -> Tuple:
return os.path.join(
self.hparams.data_dir , "cached_{}_{}_{}".format(
__magic_name__ , list(filter(__magic_name__ , self.hparams.model_name_or_path.split("/" ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def __A ( self : Optional[Any] , __magic_name__ : Dict[str, Any] ) -> None:
SCREAMING_SNAKE_CASE_ = self.output_dir.joinpath("best_tfmr" )
SCREAMING_SNAKE_CASE_ = self.step_count
self.model.save_pretrained(__magic_name__ )
self.tokenizer.save_pretrained(__magic_name__ )
@staticmethod
def __A ( __magic_name__ : Optional[int] , __magic_name__ : Tuple ) -> Union[str, Any]:
parser.add_argument(
"--model_name_or_path" , default=__magic_name__ , type=__magic_name__ , required=__magic_name__ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--config_name" , default="" , type=__magic_name__ , help="Pretrained config name or path if not the same as model_name" )
parser.add_argument(
"--tokenizer_name" , default=__magic_name__ , type=__magic_name__ , help="Pretrained tokenizer name or path if not the same as model_name" , )
parser.add_argument(
"--cache_dir" , default=str(Path(__magic_name__ ).parent / "test_run" / "cache" ) , type=__magic_name__ , help="Where do you want to store the pre-trained models downloaded from huggingface.co" , )
parser.add_argument(
"--encoder_layerdrop" , type=__magic_name__ , help="Encoder layer dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--decoder_layerdrop" , type=__magic_name__ , help="Decoder layer dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--dropout" , type=__magic_name__ , help="Dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--attention_dropout" , type=__magic_name__ , help="Attention dropout probability (Optional). Goes into model.config" , )
parser.add_argument("--learning_rate" , default=5e-5 , type=__magic_name__ , help="The initial learning rate for Adam." )
parser.add_argument(
"--lr_scheduler" , default="linear" , choices=__magic_name__ , metavar=__magic_name__ , type=__magic_name__ , help="Learning rate scheduler" , )
parser.add_argument("--weight_decay" , default=0.0 , type=__magic_name__ , help="Weight decay if we apply some." )
parser.add_argument("--adam_epsilon" , default=1e-8 , type=__magic_name__ , help="Epsilon for Adam optimizer." )
parser.add_argument("--warmup_steps" , default=0 , type=__magic_name__ , help="Linear warmup over warmup_steps." )
parser.add_argument("--num_workers" , default=4 , type=__magic_name__ , help="kwarg passed to DataLoader" )
parser.add_argument("--num_train_epochs" , dest="max_epochs" , default=3 , type=__magic_name__ )
parser.add_argument("--train_batch_size" , default=32 , type=__magic_name__ )
parser.add_argument("--eval_batch_size" , default=32 , type=__magic_name__ )
parser.add_argument("--adafactor" , action="store_true" )
class lowerCamelCase (pl.Callback ):
"""simple docstring"""
def __A ( self : str , __magic_name__ : Dict , __magic_name__ : Optional[int] ) -> Dict:
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class lowerCamelCase (pl.Callback ):
"""simple docstring"""
def __A ( self : Tuple , __magic_name__ : Union[str, Any] , __magic_name__ : List[str] ) -> int:
# print(pl_module.model.rag)
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(__magic_name__ )
class lowerCamelCase (pl.Callback ):
"""simple docstring"""
def __A ( self : List[str] , __magic_name__ : Dict , __magic_name__ : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = trainer.lr_schedulers[0]["scheduler"]
SCREAMING_SNAKE_CASE_ = {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(__magic_name__ )
def __A ( self : Tuple , __magic_name__ : pl.Trainer , __magic_name__ : pl.LightningModule ) -> Union[str, Any]:
rank_zero_info("***** Validation results *****" )
SCREAMING_SNAKE_CASE_ = trainer.callback_metrics
# Log results
for key in sorted(__magic_name__ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(__magic_name__ , str(metrics[key] ) ) )
def __A ( self : Optional[Any] , __magic_name__ : pl.Trainer , __magic_name__ : pl.LightningModule ) -> str:
rank_zero_info("***** Test results *****" )
SCREAMING_SNAKE_CASE_ = trainer.callback_metrics
# Log and save results to file
SCREAMING_SNAKE_CASE_ = os.path.join(pl_module.hparams.output_dir , "test_results.txt" )
with open(__magic_name__ , "w" ) as writer:
for key in sorted(__magic_name__ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(__magic_name__ , str(metrics[key] ) ) )
writer.write("{} = {}\n".format(__magic_name__ , str(metrics[key] ) ) )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
"--output_dir" , default=str(Path(__UpperCamelCase ).parent / "test_run" / "model_checkpoints" ) , type=__UpperCamelCase , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=__UpperCamelCase , default="O2" , help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_tpu_cores" , dest="tpu_cores" , type=__UpperCamelCase )
parser.add_argument("--max_grad_norm" , dest="gradient_clip_val" , default=1.0 , type=__UpperCamelCase , help="Max gradient norm" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_predict" , action="store_true" , help="Whether to run predictions on the test set." )
parser.add_argument(
"--gradient_accumulation_steps" , dest="accumulate_grad_batches" , type=__UpperCamelCase , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--seed" , type=__UpperCamelCase , default=4_2 , help="random seed for initialization" )
parser.add_argument(
"--data_dir" , default=str(Path(__UpperCamelCase ).parent / "test_run" / "dummy-train-data" ) , type=__UpperCamelCase , help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=[] , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , ):
pl.seed_everything(args.seed )
# init model
SCREAMING_SNAKE_CASE_ = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=__UpperCamelCase )
# add custom checkpoints
if checkpoint_callback is None:
SCREAMING_SNAKE_CASE_ = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix="checkpoint" , monitor="val_loss" , mode="min" , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(__UpperCamelCase )
if logging_callback is None:
SCREAMING_SNAKE_CASE_ = LoggingCallback()
SCREAMING_SNAKE_CASE_ = {}
if args.fpaa:
SCREAMING_SNAKE_CASE_ = 1_6
if args.gpus > 1:
SCREAMING_SNAKE_CASE_ = "auto"
SCREAMING_SNAKE_CASE_ = "ddp"
SCREAMING_SNAKE_CASE_ = args.accumulate_grad_batches
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = "auto"
SCREAMING_SNAKE_CASE_ = pl.Trainer.from_argparse_args(
__UpperCamelCase , weights_summary=__UpperCamelCase , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=__UpperCamelCase , val_check_interval=1 , num_sanity_val_steps=2 , **__UpperCamelCase , )
if args.do_train:
trainer.fit(__UpperCamelCase )
else:
print("RAG modeling tests with new set functions successfuly executed!" )
return trainer
| 305 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
A : int = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Union[str, Any] = {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))
| 305 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class lowerCAmelCase_:
'''simple docstring'''
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Optional[Any]:
return None
class lowerCAmelCase_:
'''simple docstring'''
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple:
return None
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
__lowercase : Dict = [
# (model_name, model_kwargs)
('''bert-base-cased''', {}),
('''gpt2''', {'''use_cache''': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def UpperCAmelCase_ ( self ) -> int:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__UpperCAmelCase ,"""tf""" ,12 ,**__UpperCAmelCase )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,**__UpperCAmelCase )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> Any:
from transformers import BertModel
lowerCAmelCase__ : Optional[int] = ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
with NamedTemporaryFile(mode="""w+t""" ) as vocab_file:
vocab_file.write("""\n""".join(__UpperCAmelCase ) )
vocab_file.flush()
lowerCAmelCase__ : Dict = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowerCAmelCase__ : Tuple = BertModel(BertConfig(vocab_size=len(__UpperCAmelCase ) ) )
model.save_pretrained(__UpperCAmelCase )
self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,__UpperCAmelCase )
@require_tf
@slow
def UpperCAmelCase_ ( self ) -> List[str]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCAmelCase__ : Dict = self._test_export(__UpperCAmelCase ,"""tf""" ,12 ,**__UpperCAmelCase )
lowerCAmelCase__ : List[str] = quantize(Path(__UpperCAmelCase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__UpperCAmelCase ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> List[Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCAmelCase__ : Any = self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,**__UpperCAmelCase )
lowerCAmelCase__ : Dict = quantize(__UpperCAmelCase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__UpperCAmelCase ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase=None ,**__UpperCAmelCase ) -> Optional[Any]:
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowerCAmelCase__ : Optional[int] = Path(__UpperCAmelCase ).joinpath("""model.onnx""" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,**__UpperCAmelCase )
return path
except Exception as e:
self.fail(__UpperCAmelCase )
@require_torch
@require_tokenizers
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
from transformers import BertModel
lowerCAmelCase__ : List[Any] = BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
lowerCAmelCase__ : Union[str, Any] = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(__UpperCAmelCase ,__UpperCAmelCase ,"""pt""" )
@require_tf
@require_tokenizers
@slow
def UpperCAmelCase_ ( self ) -> Optional[int]:
from transformers import TFBertModel
lowerCAmelCase__ : int = TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
lowerCAmelCase__ : Optional[int] = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(__UpperCAmelCase ,__UpperCAmelCase ,"""tf""" )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple:
lowerCAmelCase__ : Any = FeatureExtractionPipeline(__UpperCAmelCase ,__UpperCAmelCase )
lowerCAmelCase__ : List[str] = ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = infer_shapes(__UpperCAmelCase ,__UpperCAmelCase )
# Assert all variables are present
self.assertEqual(len(__UpperCAmelCase ) ,len(__UpperCAmelCase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] ,__UpperCAmelCase )
self.assertSequenceEqual(variable_names[3:] ,__UpperCAmelCase )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] ,{0: """batch""", 1: """sequence"""} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["""output_0"""] ,{0: """batch""", 1: """sequence"""} )
self.assertDictEqual(shapes["""output_1"""] ,{0: """batch"""} )
def UpperCAmelCase_ ( self ) -> Optional[int]:
lowerCAmelCase__ : List[str] = ["""input_ids""", """attention_mask""", """token_type_ids"""]
lowerCAmelCase__ : Union[str, Any] = {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = ensure_valid_input(FuncContiguousArgs() ,__UpperCAmelCase ,__UpperCAmelCase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__UpperCAmelCase ) ,3 )
# Should have exactly the same input names
self.assertEqual(set(__UpperCAmelCase ) ,set(__UpperCAmelCase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__UpperCAmelCase ,(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowerCAmelCase__ , lowerCAmelCase__ : int = ensure_valid_input(FuncNonContiguousArgs() ,__UpperCAmelCase ,__UpperCAmelCase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__UpperCAmelCase ) ,1 )
self.assertEqual(len(__UpperCAmelCase ) ,1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] ,tokens["""input_ids"""] )
self.assertEqual(ordered_input_names[0] ,"""input_ids""" )
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : Dict = generate_identified_filename(Path("""/home/something/my_fake_model.onnx""" ) ,"""-test""" )
self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,generated.as_posix() )
| 37 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowerCAmelCase__ : str = set()
# Replace all the whitespace in our sentence
lowerCAmelCase__ : Tuple = input_str.replace(""" """ , """""" )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(UpperCamelCase ) == 26
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
lowerCAmelCase__ : Any = [False] * 26
for char in input_str:
if char.islower():
lowerCAmelCase__ : Optional[Any] = True
elif char.isupper():
lowerCAmelCase__ : Any = True
return all(UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def _SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
from timeit import timeit
lowerCAmelCase__ : Union[str, Any] = """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
print(timeit("""is_pangram()""" , setup=UpperCamelCase ) )
print(timeit("""is_pangram_faster()""" , setup=UpperCamelCase ) )
print(timeit("""is_pangram_fastest()""" , setup=UpperCamelCase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 37 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : int = 'switch_transformers'
A_ : Tuple = ['past_key_values']
A_ : Dict = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self , __UpperCAmelCase=32128 , __UpperCAmelCase=768 , __UpperCAmelCase=64 , __UpperCAmelCase=2048 , __UpperCAmelCase=64 , __UpperCAmelCase=12 , __UpperCAmelCase=3 , __UpperCAmelCase=12 , __UpperCAmelCase=3 , __UpperCAmelCase=12 , __UpperCAmelCase=8 , __UpperCAmelCase=False , __UpperCAmelCase=0.01 , __UpperCAmelCase="float32" , __UpperCAmelCase=False , __UpperCAmelCase=32 , __UpperCAmelCase=128 , __UpperCAmelCase=0.1 , __UpperCAmelCase=1e-6 , __UpperCAmelCase=0.001 , __UpperCAmelCase=0.001 , __UpperCAmelCase=1.0 , __UpperCAmelCase="relu" , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=0 , __UpperCAmelCase=1 , **__UpperCAmelCase , ) -> List[Any]:
_a = vocab_size
_a = d_model
_a = d_kv
_a = d_ff
_a = num_sparse_encoder_layers
_a = num_layers
_a = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_a = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
_a = self.num_layers // self.num_sparse_encoder_layers
else:
_a = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
_a = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
_a = self.num_decoder_layers # HACK: this will create 0 sparse layers
_a = num_heads
_a = num_experts
_a = expert_capacity
_a = router_bias
_a = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
_a = router_dtype
_a = router_ignore_padding_tokens
_a = relative_attention_num_buckets
_a = relative_attention_max_distance
_a = dropout_rate
_a = layer_norm_epsilon
_a = initializer_factor
_a = feed_forward_proj
_a = use_cache
_a = add_router_probs
_a = router_z_loss_coef
_a = router_aux_loss_coef
_a = self.feed_forward_proj.split('''-''' )
_a = act_info[-1]
_a = act_info[0] == '''gated'''
if len(__UpperCAmelCase ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase ) > 2:
raise ValueError(
F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
_a = '''gelu_new'''
super().__init__(
pad_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , is_encoder_decoder=__UpperCAmelCase , **__UpperCAmelCase , ) | 153 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a__ )
class __lowerCamelCase ( a__ ):
'''simple docstring'''
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
A_ : str = field(default='summarization' , metadata={'include_in_asdict_even_if_is_default': True} )
A_ : ClassVar[Features] = Features({'text': Value('string' )} )
A_ : ClassVar[Features] = Features({'summary': Value('string' )} )
A_ : str = "text"
A_ : str = "summary"
@property
def _UpperCAmelCase ( self ) -> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"} | 153 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
lowerCamelCase : Union[str, Any] =logging.get_logger(__name__)
lowerCamelCase : Dict ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowerCamelCase : Tuple ={
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
lowerCamelCase : int ={
'squeezebert/squeezebert-uncased': 512,
'squeezebert/squeezebert-mnli': 512,
'squeezebert/squeezebert-mnli-headless': 512,
}
lowerCamelCase : str ={
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class __a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
_lowerCAmelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
_lowerCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Union[str, Any] = SqueezeBertTokenizer
def __init__( self : Tuple , SCREAMING_SNAKE_CASE : Dict=None , SCREAMING_SNAKE_CASE : int=None , SCREAMING_SNAKE_CASE : List[str]=True , SCREAMING_SNAKE_CASE : str="[UNK]" , SCREAMING_SNAKE_CASE : Union[str, Any]="[SEP]" , SCREAMING_SNAKE_CASE : List[Any]="[PAD]" , SCREAMING_SNAKE_CASE : str="[CLS]" , SCREAMING_SNAKE_CASE : Dict="[MASK]" , SCREAMING_SNAKE_CASE : Optional[int]=True , SCREAMING_SNAKE_CASE : Dict=None , **SCREAMING_SNAKE_CASE : str , ):
'''simple docstring'''
super().__init__(
_lowerCAmelCase , tokenizer_file=_lowerCAmelCase , do_lower_case=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , tokenize_chinese_chars=_lowerCAmelCase , strip_accents=_lowerCAmelCase , **_lowerCAmelCase , )
UpperCamelCase__ : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , _lowerCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , _lowerCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , _lowerCAmelCase ) != tokenize_chinese_chars
):
UpperCamelCase__ : Dict = getattr(_lowerCAmelCase , normalizer_state.pop("type" ) )
UpperCamelCase__ : Union[str, Any] = do_lower_case
UpperCamelCase__ : Optional[int] = strip_accents
UpperCamelCase__ : Optional[Any] = tokenize_chinese_chars
UpperCamelCase__ : Optional[int] = normalizer_class(**_lowerCAmelCase )
UpperCamelCase__ : List[str] = do_lower_case
def __lowercase ( self : Tuple , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[int]=None ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE : List[int] , SCREAMING_SNAKE_CASE : Optional[List[int]] = None ):
'''simple docstring'''
UpperCamelCase__ : Any = [self.sep_token_id]
UpperCamelCase__ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowercase ( self : Tuple , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[str] = None ):
'''simple docstring'''
UpperCamelCase__ : Dict = self._tokenizer.model.save(_lowerCAmelCase , name=_lowerCAmelCase )
return tuple(_lowerCAmelCase ) | 189 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : List[str] = logging.get_logger(__name__)
lowerCamelCase__ : List[str] = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "speech_to_text"
lowercase_ = ["past_key_values"]
lowercase_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Tuple , _lowerCAmelCase : List[Any]=10_000 , _lowerCAmelCase : List[Any]=12 , _lowerCAmelCase : Union[str, Any]=2_048 , _lowerCAmelCase : Optional[int]=4 , _lowerCAmelCase : Union[str, Any]=6 , _lowerCAmelCase : Optional[int]=2_048 , _lowerCAmelCase : Optional[Any]=4 , _lowerCAmelCase : Any=0.0 , _lowerCAmelCase : Optional[Any]=0.0 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : List[Any]=True , _lowerCAmelCase : int="relu" , _lowerCAmelCase : Union[str, Any]=256 , _lowerCAmelCase : List[str]=0.1 , _lowerCAmelCase : str=0.0 , _lowerCAmelCase : Tuple=0.0 , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : List[str]=2 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : List[str]=1 , _lowerCAmelCase : str=0 , _lowerCAmelCase : str=2 , _lowerCAmelCase : Union[str, Any]=6_000 , _lowerCAmelCase : List[str]=1_024 , _lowerCAmelCase : str=2 , _lowerCAmelCase : Optional[Any]=(5, 5) , _lowerCAmelCase : str=1_024 , _lowerCAmelCase : str=80 , _lowerCAmelCase : Tuple=1 , **_lowerCAmelCase : Any , ):
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE_ = max_source_positions
SCREAMING_SNAKE_CASE_ = max_target_positions
SCREAMING_SNAKE_CASE_ = num_conv_layers
SCREAMING_SNAKE_CASE_ = list(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = conv_channels
SCREAMING_SNAKE_CASE_ = input_feat_per_channel
SCREAMING_SNAKE_CASE_ = input_channels
if len(self.conv_kernel_sizes ) != self.num_conv_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes )}`, "
F"`config.num_conv_layers = {self.num_conv_layers}`." )
super().__init__(
pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , is_encoder_decoder=_lowerCAmelCase , decoder_start_token_id=_lowerCAmelCase , **_lowerCAmelCase , ) | 225 | 0 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class __magic_name__ :
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=30 , _a=2 , _a=3 , _a=True , _a=True , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=10 , _a=0.02 , _a=3 , _a=None , _a=2 , ):
"""simple docstring"""
lowerCamelCase = parent
lowerCamelCase = batch_size
lowerCamelCase = image_size
lowerCamelCase = patch_size
lowerCamelCase = num_channels
lowerCamelCase = is_training
lowerCamelCase = use_labels
lowerCamelCase = hidden_size
lowerCamelCase = num_hidden_layers
lowerCamelCase = num_attention_heads
lowerCamelCase = intermediate_size
lowerCamelCase = hidden_act
lowerCamelCase = hidden_dropout_prob
lowerCamelCase = attention_probs_dropout_prob
lowerCamelCase = type_sequence_label_size
lowerCamelCase = initializer_range
lowerCamelCase = scope
lowerCamelCase = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowerCamelCase = (image_size // patch_size) ** 2
lowerCamelCase = num_patches + 2
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase = None
if self.use_labels:
lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase = self.get_config()
return config, pixel_values, labels
def _lowerCAmelCase ( self ):
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_a , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _lowerCAmelCase ( self , _a , _a , _a ):
"""simple docstring"""
lowerCamelCase = DeiTModel(config=_a )
model.to(_a )
model.eval()
lowerCamelCase = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self , _a , _a , _a ):
"""simple docstring"""
lowerCamelCase = DeiTForMaskedImageModeling(config=_a )
model.to(_a )
model.eval()
lowerCamelCase = model(_a )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCamelCase = 1
lowerCamelCase = DeiTForMaskedImageModeling(_a )
model.to(_a )
model.eval()
lowerCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase = model(_a )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _lowerCAmelCase ( self , _a , _a , _a ):
"""simple docstring"""
lowerCamelCase = self.type_sequence_label_size
lowerCamelCase = DeiTForImageClassification(_a )
model.to(_a )
model.eval()
lowerCamelCase = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase = 1
lowerCamelCase = DeiTForImageClassification(_a )
model.to(_a )
model.eval()
lowerCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.prepare_config_and_inputs()
(
(
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) ,
) = config_and_inputs
lowerCamelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (
{
"feature-extraction": DeiTModel,
"image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = DeiTModelTester(self )
lowerCamelCase = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def _lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def _lowerCAmelCase ( self ):
"""simple docstring"""
pass
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase = model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase = model_class(_a )
lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase = [*signature.parameters.keys()]
lowerCamelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
def _lowerCAmelCase ( self , _a , _a , _a=False ):
"""simple docstring"""
lowerCamelCase = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _lowerCAmelCase ( self ):
"""simple docstring"""
if not self.model_tester.is_training:
return
lowerCamelCase , lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_a )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
lowerCamelCase = model_class(_a )
model.to(_a )
model.train()
lowerCamelCase = self._prepare_for_class(_a , _a , return_labels=_a )
lowerCamelCase = model(**_a ).loss
loss.backward()
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowerCamelCase = False
lowerCamelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(_a ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
lowerCamelCase = model_class(_a )
model.gradient_checkpointing_enable()
model.to(_a )
model.train()
lowerCamelCase = self._prepare_for_class(_a , _a , return_labels=_a )
lowerCamelCase = model(**_a ).loss
loss.backward()
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase = [
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_a ),
*get_values(_a ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}' ):
lowerCamelCase = problem_type["""title"""]
lowerCamelCase = problem_type["""num_labels"""]
lowerCamelCase = model_class(_a )
model.to(_a )
model.train()
lowerCamelCase = self._prepare_for_class(_a , _a , return_labels=_a )
if problem_type["num_labels"] > 1:
lowerCamelCase = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
lowerCamelCase = inputs["""labels"""].to(problem_type["""dtype"""] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_a ) as warning_list:
lowerCamelCase = model(**_a ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f'Something is going wrong in the regression problem: intercepted {w.message}' )
loss.backward()
@slow
def _lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase = DeiTModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def a__ ( ) -> int:
lowerCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" ).to(
_a )
lowerCamelCase = self.default_image_processor
lowerCamelCase = prepare_img()
lowerCamelCase = image_processor(images=_a , return_tensors="""pt""" ).to(_a )
# forward pass
with torch.no_grad():
lowerCamelCase = model(**_a )
# verify the logits
lowerCamelCase = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _a )
lowerCamelCase = torch.tensor([-1.0_266, 0.1_912, -1.2_861] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = DeiTModel.from_pretrained(
"""facebook/deit-base-distilled-patch16-224""" , torch_dtype=torch.floataa , device_map="""auto""" )
lowerCamelCase = self.default_image_processor
lowerCamelCase = prepare_img()
lowerCamelCase = image_processor(images=_a , return_tensors="""pt""" )
lowerCamelCase = inputs.pixel_values.to(_a )
# forward pass to make sure inference works in fp16
with torch.no_grad():
lowerCamelCase = model(_a )
| 168 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a__ ( snake_case__ ) -> Optional[Any]:
lowerCamelCase = {}
lowerCamelCase = job["""started_at"""]
lowerCamelCase = job["""completed_at"""]
lowerCamelCase = date_parser.parse(snake_case__ )
lowerCamelCase = date_parser.parse(snake_case__ )
lowerCamelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
lowerCamelCase = start
lowerCamelCase = end
lowerCamelCase = duration_in_min
return job_info
def a__ ( snake_case__ , snake_case__=None ) -> Optional[Any]:
lowerCamelCase = None
if token is not None:
lowerCamelCase = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'Bearer {token}'}
lowerCamelCase = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
lowerCamelCase = requests.get(snake_case__ , headers=snake_case__ ).json()
lowerCamelCase = {}
try:
job_time.update({job["""name"""]: extract_time_from_single_job(snake_case__ ) for job in result["""jobs"""]} )
lowerCamelCase = math.ceil((result["""total_count"""] - 1_00) / 1_00 )
for i in range(snake_case__ ):
lowerCamelCase = requests.get(url + F'&page={i + 2}' , headers=snake_case__ ).json()
job_time.update({job["""name"""]: extract_time_from_single_job(snake_case__ ) for job in result["""jobs"""]} )
return job_time
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
if __name__ == "__main__":
lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
lowerCAmelCase : Any = parser.parse_args()
lowerCAmelCase : Optional[int] = get_job_time(args.workflow_run_id)
lowerCAmelCase : Dict = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F"""{k}: {v['duration']}""")
| 168 | 1 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = 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()
| 31 | '''simple docstring'''
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Tuple , A : Any , A : str , A : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = graph
self._normalize_graph(A , A )
_UpperCAmelCase : List[str] = len(A )
_UpperCAmelCase : Tuple = None
def _A ( self : Any , A : List[Any] , A : str ):
if sources is int:
_UpperCAmelCase : List[Any] = [sources]
if sinks is int:
_UpperCAmelCase : List[Any] = [sinks]
if len(A ) == 0 or len(A ) == 0:
return
_UpperCAmelCase : str = sources[0]
_UpperCAmelCase : Union[str, Any] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(A ) > 1 or len(A ) > 1:
_UpperCAmelCase : Dict = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_UpperCAmelCase : Optional[Any] = max_input_flow
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_UpperCAmelCase : Dict = max_input_flow
_UpperCAmelCase : List[Any] = size - 1
def _A ( self : Union[str, Any] ):
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _A ( self : Tuple , A : Dict ):
_UpperCAmelCase : str = algorithm(self )
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , A : str ):
_UpperCAmelCase : Optional[int] = flow_network
_UpperCAmelCase : Any = flow_network.verticesCount
_UpperCAmelCase : List[str] = flow_network.sourceIndex
_UpperCAmelCase : Union[str, Any] = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_UpperCAmelCase : Any = flow_network.graph
_UpperCAmelCase : Union[str, Any] = False
def _A ( self : List[str] ):
if not self.executed:
self._algorithm()
_UpperCAmelCase : int = True
def _A ( self : List[Any] ):
pass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[str, Any] ):
super().__init__(A )
# use this to save your result
_UpperCAmelCase : Any = -1
def _A ( self : Union[str, Any] ):
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Tuple , A : int ):
super().__init__(A )
_UpperCAmelCase : List[str] = [[0] * self.verticies_count for i in range(self.verticies_count )]
_UpperCAmelCase : Union[str, Any] = [0] * self.verticies_count
_UpperCAmelCase : int = [0] * self.verticies_count
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_UpperCAmelCase : Optional[int] = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_UpperCAmelCase : Any = 0
while i < len(A ):
_UpperCAmelCase : int = vertices_list[i]
_UpperCAmelCase : int = self.heights[vertex_index]
self.process_vertex(A )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(A ) )
_UpperCAmelCase : Union[str, Any] = 0
else:
i += 1
_UpperCAmelCase : List[Any] = sum(self.preflow[self.source_index] )
def _A ( self : Union[str, Any] , A : str ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(A , A )
self.relabel(A )
def _A ( self : int , A : Dict , A : List[str] ):
_UpperCAmelCase : int = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _A ( self : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : str = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_UpperCAmelCase : Tuple = self.heights[to_index]
if min_height is not None:
_UpperCAmelCase : Optional[Any] = min_height + 1
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = [0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__SCREAMING_SNAKE_CASE : List[Any] = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__SCREAMING_SNAKE_CASE : Union[str, Any] = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__SCREAMING_SNAKE_CASE : Optional[Any] = flow_network.find_maximum_flow()
print(F'maximum flow is {maximum_flow}')
| 31 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class lowerCAmelCase__ ( unittest.TestCase ):
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Tuple=7 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE__ : Optional[Any]=18 , SCREAMING_SNAKE_CASE__ : Optional[int]=30 , SCREAMING_SNAKE_CASE__ : Dict=4_00 , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=[0.48145466, 0.4578275, 0.40821073] , SCREAMING_SNAKE_CASE__ : Any=[0.26862954, 0.26130258, 0.27577711] , SCREAMING_SNAKE_CASE__ : Optional[int]=True , ) -> Any:
__lowerCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
__lowerCamelCase = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = num_channels
__lowerCamelCase = image_size
__lowerCamelCase = min_resolution
__lowerCamelCase = max_resolution
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = do_center_crop
__lowerCamelCase = crop_size
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean
__lowerCamelCase = image_std
__lowerCamelCase = do_convert_rgb
def __A ( self : int ) -> Union[str, Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def __A ( self : List[str] , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : Tuple=False ) -> List[Any]:
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
__lowerCamelCase = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
2_55 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
__lowerCamelCase = []
for i in range(self.batch_size ):
__lowerCamelCase , __lowerCamelCase = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(2_55 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
__lowerCamelCase = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
if torchify:
__lowerCamelCase = [torch.from_numpy(SCREAMING_SNAKE_CASE__ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class lowerCAmelCase__ ( __lowercase , unittest.TestCase ):
a__ : int = ChineseCLIPImageProcessor if is_vision_available() else None
def __A ( self : Optional[Any] ) -> str:
__lowerCamelCase = ChineseCLIPImageProcessingTester(self , do_center_crop=SCREAMING_SNAKE_CASE__ )
@property
def __A ( self : str ) -> Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self : Optional[Any] ) -> Any:
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_resize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''size''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''center_crop''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''image_mean''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''image_std''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_convert_rgb''' ) )
def __A ( self : Dict ) -> Any:
__lowerCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 2_24, '''width''': 2_24} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
__lowerCamelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def __A ( self : Union[str, Any] ) -> int:
pass
def __A ( self : Tuple ) -> Optional[int]:
# Initialize image_processing
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
__lowerCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__lowerCamelCase = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def __A ( self : List[str] ) -> int:
# Initialize image_processing
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCamelCase = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ , numpify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , np.ndarray )
# Test not batched input
__lowerCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__lowerCamelCase = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def __A ( self : Optional[Any] ) -> Optional[Any]:
# Initialize image_processing
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCamelCase = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ , torchify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , torch.Tensor )
# Test not batched input
__lowerCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__lowerCamelCase = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
@require_torch
@require_vision
class lowerCAmelCase__ ( __lowercase , unittest.TestCase ):
a__ : Optional[Any] = ChineseCLIPImageProcessor if is_vision_available() else None
def __A ( self : Tuple ) -> Union[str, Any]:
__lowerCamelCase = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = 3
@property
def __A ( self : Dict ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def __A ( self : Any ) -> Tuple:
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_resize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''size''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''center_crop''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''image_mean''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''image_std''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_convert_rgb''' ) )
def __A ( self : Any ) -> List[str]:
pass
def __A ( self : Optional[Any] ) -> Union[str, Any]:
# Initialize image_processing
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
__lowerCamelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__lowerCamelCase = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 339 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
SCREAMING_SNAKE_CASE__ : str = {
"vocab_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
},
"merges_file": {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
},
"tokenizer_config_file": {
"facebook/blenderbot_small-90M": (
"https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
)
},
}
SCREAMING_SNAKE_CASE__ : int = {"facebook/blenderbot_small-90M": 512}
def __magic_name__ ( __lowerCAmelCase : List[str] ) -> Tuple:
__lowerCamelCase = set()
__lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowerCamelCase = char
__lowerCamelCase = set(__lowerCAmelCase )
return pairs
class lowerCAmelCase__ ( __lowercase ):
a__ : List[Any] = VOCAB_FILES_NAMES
a__ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Dict = ["""input_ids""", """attention_mask"""]
def __init__( self : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple="__start__" , SCREAMING_SNAKE_CASE__ : Tuple="__end__" , SCREAMING_SNAKE_CASE__ : List[str]="__unk__" , SCREAMING_SNAKE_CASE__ : str="__null__" , **SCREAMING_SNAKE_CASE__ : Optional[Any] , ) -> Optional[Any]:
super().__init__(unk_token=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , encoding='''utf-8''' ) as vocab_handle:
__lowerCamelCase = json.load(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = {v: k for k, v in self.encoder.items()}
with open(SCREAMING_SNAKE_CASE__ , encoding='''utf-8''' ) as merges_handle:
__lowerCamelCase = merges_handle.read().split('''\n''' )[1:-1]
__lowerCamelCase = [tuple(merge.split() ) for merge in merges]
__lowerCamelCase = dict(zip(SCREAMING_SNAKE_CASE__ , range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
__lowerCamelCase = {}
@property
def __A ( self : Dict ) -> int:
return len(self.encoder )
def __A ( self : str ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def __A ( self : Any , SCREAMING_SNAKE_CASE__ : str ) -> str:
if token in self.cache:
return self.cache[token]
__lowerCamelCase = re.sub('''([.,!?()])''' , R''' \1''' , SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = re.sub('''(\')''' , R''' \1 ''' , SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = re.sub(R'''\s{2,}''' , ''' ''' , SCREAMING_SNAKE_CASE__ )
if "\n" in token:
__lowerCamelCase = token.replace('''\n''' , ''' __newln__''' )
__lowerCamelCase = token.split(''' ''' )
__lowerCamelCase = []
for token in tokens:
if not len(SCREAMING_SNAKE_CASE__ ):
continue
__lowerCamelCase = token.lower()
__lowerCamelCase = tuple(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
__lowerCamelCase = get_pairs(SCREAMING_SNAKE_CASE__ )
if not pairs:
words.append(SCREAMING_SNAKE_CASE__ )
continue
while True:
__lowerCamelCase = min(SCREAMING_SNAKE_CASE__ , key=lambda SCREAMING_SNAKE_CASE__ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__lowerCamelCase , __lowerCamelCase = bigram
__lowerCamelCase = []
__lowerCamelCase = 0
while i < len(SCREAMING_SNAKE_CASE__ ):
try:
__lowerCamelCase = word.index(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
new_word.extend(word[i:j] )
__lowerCamelCase = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(SCREAMING_SNAKE_CASE__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowerCamelCase = tuple(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = new_word
if len(SCREAMING_SNAKE_CASE__ ) == 1:
break
else:
__lowerCamelCase = get_pairs(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = '''@@ '''.join(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = word[:-4]
__lowerCamelCase = word
words.append(SCREAMING_SNAKE_CASE__ )
return " ".join(SCREAMING_SNAKE_CASE__ )
def __A ( self : Tuple , SCREAMING_SNAKE_CASE__ : str ) -> List[str]:
__lowerCamelCase = []
__lowerCamelCase = re.findall(R'''\S+\n?''' , SCREAMING_SNAKE_CASE__ )
for token in words:
split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE__ ).split(''' ''' ) ) )
return split_tokens
def __A ( self : str , SCREAMING_SNAKE_CASE__ : str ) -> int:
__lowerCamelCase = token.lower()
return self.encoder.get(SCREAMING_SNAKE_CASE__ , self.encoder.get(self.unk_token ) )
def __A ( self : Dict , SCREAMING_SNAKE_CASE__ : int ) -> str:
return self.decoder.get(SCREAMING_SNAKE_CASE__ , self.unk_token )
def __A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] ) -> str:
__lowerCamelCase = ''' '''.join(SCREAMING_SNAKE_CASE__ ).replace('''@@ ''' , '''''' ).strip()
return out_string
def __A ( self : Tuple , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__lowerCamelCase = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__lowerCamelCase = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ ) + '''\n''' )
__lowerCamelCase = 0
with open(SCREAMING_SNAKE_CASE__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE__ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
__lowerCamelCase = token_index
writer.write(''' '''.join(SCREAMING_SNAKE_CASE__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 339 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : List[str] = logging.get_logger(__name__)
lowerCAmelCase_ : List[Any] = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __SCREAMING_SNAKE_CASE (_UpperCAmelCase ):
"""simple docstring"""
__a ='sew-d'
def __init__( self : str , __a : Union[str, Any]=32 , __a : Optional[int]=7_68 , __a : str=12 , __a : List[Any]=12 , __a : Dict=30_72 , __a : Union[str, Any]=2 , __a : List[str]=5_12 , __a : Optional[Any]=2_56 , __a : int=True , __a : int=True , __a : Optional[int]=("p2c", "c2p") , __a : Optional[Any]="layer_norm" , __a : int="gelu_python" , __a : Union[str, Any]=0.1 , __a : Any=0.1 , __a : Tuple=0.1 , __a : Optional[Any]=0.0 , __a : Any=0.1 , __a : List[Any]=0.02 , __a : Optional[Any]=1e-7 , __a : Union[str, Any]=1e-5 , __a : Tuple="group" , __a : Optional[int]="gelu" , __a : str=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) , __a : List[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __a : Optional[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __a : Optional[int]=False , __a : List[str]=1_28 , __a : Optional[int]=16 , __a : List[Any]=True , __a : int=0.05 , __a : List[str]=10 , __a : Dict=2 , __a : str=0.0 , __a : List[Any]=10 , __a : Any=0 , __a : int="mean" , __a : str=False , __a : Optional[Any]=False , __a : List[str]=2_56 , __a : List[Any]=0 , __a : int=1 , __a : Union[str, Any]=2 , **__a : Any , ):
super().__init__(**A_ , pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ )
_a = hidden_size
_a = feat_extract_norm
_a = feat_extract_activation
_a = list(A_ )
_a = list(A_ )
_a = list(A_ )
_a = conv_bias
_a = num_conv_pos_embeddings
_a = num_conv_pos_embedding_groups
_a = len(self.conv_dim )
_a = num_hidden_layers
_a = intermediate_size
_a = squeeze_factor
_a = max_position_embeddings
_a = position_buckets
_a = share_att_key
_a = relative_attention
_a = norm_rel_ebd
_a = list(A_ )
_a = hidden_act
_a = num_attention_heads
_a = hidden_dropout
_a = attention_dropout
_a = activation_dropout
_a = feat_proj_dropout
_a = final_dropout
_a = layer_norm_eps
_a = feature_layer_norm_eps
_a = initializer_range
_a = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect."
"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
f'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
f'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_a = apply_spec_augment
_a = mask_time_prob
_a = mask_time_length
_a = mask_time_min_masks
_a = mask_feature_prob
_a = mask_feature_length
_a = mask_feature_min_masks
# ctc loss
_a = ctc_loss_reduction
_a = ctc_zero_infinity
# sequence classification
_a = use_weighted_layer_sum
_a = classifier_proj_size
@property
def UpperCamelCase__ ( self : str ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 63 |
def _lowercase ( lowercase__ , lowercase__ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCAmelCase : int = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
__lowerCAmelCase : Any = str(bin(lowercase__ ) )[2:]
__lowerCAmelCase : List[str] = max(len(lowercase__ ) , len(lowercase__ ) )
return "0b" + "".join(
str(int('''1''' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(lowercase__ ) , b_binary.zfill(lowercase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 275 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : List[str] = CanineTokenizer
_lowerCAmelCase : Optional[Any] = False
def snake_case ( self ):
"""simple docstring"""
super().setUp()
snake_case = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def snake_case ( self ):
"""simple docstring"""
return CanineTokenizer.from_pretrained('google/canine-s' )
def snake_case ( self , **lowerCAmelCase ):
"""simple docstring"""
snake_case = self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase )
snake_case = 10_24
return tokenizer
@require_torch
def snake_case ( self ):
"""simple docstring"""
snake_case = self.canine_tokenizer
snake_case = ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
# fmt: off
snake_case = [5_73_44, 76, 1_05, 1_02, 1_01, 32, 1_05, 1_15, 32, 1_08, 1_05, 1_07, 1_01, 32, 97, 32, 98, 1_11, 1_20, 32, 1_11, 1_02, 32, 99, 1_04, 1_11, 99, 1_11, 1_08, 97, 1_16, 1_01, 1_15, 46, 5_73_45, 0, 0, 0, 0]
# fmt: on
snake_case = tokenizer(lowerCAmelCase , padding=lowerCAmelCase , return_tensors='pt' )
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase )
snake_case = list(batch.input_ids.numpy()[0] )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
self.assertEqual((2, 39) , batch.input_ids.shape )
self.assertEqual((2, 39) , batch.attention_mask.shape )
@require_torch
def snake_case ( self ):
"""simple docstring"""
snake_case = self.canine_tokenizer
snake_case = ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
snake_case = tokenizer(lowerCAmelCase , padding=lowerCAmelCase , return_tensors='pt' )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn('input_ids' , lowerCAmelCase )
self.assertIn('attention_mask' , lowerCAmelCase )
self.assertIn('token_type_ids' , lowerCAmelCase )
@require_torch
def snake_case ( self ):
"""simple docstring"""
snake_case = self.canine_tokenizer
snake_case = [
'What\'s the weater?',
'It\'s about 25 degrees.',
]
snake_case = tokenizer(
text_target=lowerCAmelCase , max_length=32 , padding='max_length' , truncation=lowerCAmelCase , return_tensors='pt' )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
snake_case = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case = tempfile.mkdtemp()
snake_case = ' He is very happy, UNwant\u00E9d,running'
snake_case = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
tokenizer.save_pretrained(lowerCAmelCase )
snake_case = tokenizer.__class__.from_pretrained(lowerCAmelCase )
snake_case = after_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
shutil.rmtree(lowerCAmelCase )
snake_case = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case = tempfile.mkdtemp()
snake_case = ' He is very happy, UNwant\u00E9d,running'
snake_case = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
snake_case = chr(0xE_0_0_7 )
additional_special_tokens.append(lowerCAmelCase )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
snake_case = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
tokenizer.save_pretrained(lowerCAmelCase )
snake_case = tokenizer.__class__.from_pretrained(lowerCAmelCase )
snake_case = after_tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
self.assertIn(lowerCAmelCase , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
snake_case = tokenizer.__class__.from_pretrained(lowerCAmelCase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case ,snake_case = self.get_clean_sequence(lowerCAmelCase )
# a special token for Canine can be defined as follows:
snake_case = 0xE_0_0_5
snake_case = chr(lowerCAmelCase )
tokenizer.add_special_tokens({'cls_token': special_token} )
snake_case = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(len(lowerCAmelCase ) , 1 )
snake_case = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=lowerCAmelCase )
snake_case = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
snake_case = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
snake_case = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase , input_encoded + special_token_id )
snake_case = tokenizer.decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase )
self.assertTrue(special_token not in decoded )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case = chr(0xE_0_0_5 )
snake_case = chr(0xE_0_0_6 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=lowerCAmelCase )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]} )
snake_case = tokenizer.tokenize(lowerCAmelCase )
snake_case = tokenizer.tokenize(lowerCAmelCase )
self.assertEqual(len(lowerCAmelCase ) , 1 )
self.assertEqual(len(lowerCAmelCase ) , 1 )
self.assertEqual(token_a[0] , lowerCAmelCase )
self.assertEqual(token_a[0] , lowerCAmelCase )
@require_tokenizers
def snake_case ( self ):
"""simple docstring"""
snake_case = self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# a special token for Canine can be defined as follows:
snake_case = 0xE_0_0_6
snake_case = chr(lowerCAmelCase )
snake_case = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase )
tokenizer.add_special_tokens({'additional_special_tokens': [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(lowerCAmelCase )
tokenizer.from_pretrained(lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCAmelCase )
with open(os.path.join(lowerCAmelCase , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
snake_case = json.load(lowerCAmelCase )
with open(os.path.join(lowerCAmelCase , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
snake_case = json.load(lowerCAmelCase )
# a special token for Canine can be defined as follows:
snake_case = 0xE_0_0_6
snake_case = chr(lowerCAmelCase )
snake_case = [new_token_a]
snake_case = [new_token_a]
with open(os.path.join(lowerCAmelCase , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(lowerCAmelCase , lowerCAmelCase )
with open(os.path.join(lowerCAmelCase , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(lowerCAmelCase , lowerCAmelCase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
snake_case = tokenizer_class.from_pretrained(lowerCAmelCase , extra_ids=0 )
self.assertIn(lowerCAmelCase , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
snake_case = 0xE_0_0_7
snake_case = chr(lowerCAmelCase )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
snake_case = [AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase )]
snake_case = tokenizer_class.from_pretrained(
lowerCAmelCase , additional_special_tokens=lowerCAmelCase , extra_ids=0 )
self.assertIn(lowerCAmelCase , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def snake_case ( self ):
"""simple docstring"""
snake_case = self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case = 'hello world'
if self.space_between_special_tokens:
snake_case = '[CLS] hello world [SEP]'
else:
snake_case = input
snake_case = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
snake_case = tokenizer.decode(lowerCAmelCase , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(lowerCAmelCase , [output, output.lower()] )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
snake_case = 'a'
snake_case = ord(lowerCAmelCase )
for attr in attributes_list:
setattr(lowerCAmelCase , attr + '_id' , lowerCAmelCase )
self.assertEqual(getattr(lowerCAmelCase , lowerCAmelCase ) , lowerCAmelCase )
self.assertEqual(getattr(lowerCAmelCase , attr + '_id' ) , lowerCAmelCase )
setattr(lowerCAmelCase , attr + '_id' , lowerCAmelCase )
self.assertEqual(getattr(lowerCAmelCase , lowerCAmelCase ) , lowerCAmelCase )
self.assertEqual(getattr(lowerCAmelCase , attr + '_id' ) , lowerCAmelCase )
setattr(lowerCAmelCase , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(lowerCAmelCase , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(lowerCAmelCase , 'additional_special_tokens_ids' ) , [] )
snake_case = 0xE_0_0_6
snake_case = chr(lowerCAmelCase )
setattr(lowerCAmelCase , 'additional_special_tokens_ids' , [additional_special_token_id] )
self.assertListEqual(getattr(lowerCAmelCase , 'additional_special_tokens' ) , [additional_special_token] )
self.assertListEqual(getattr(lowerCAmelCase , 'additional_special_tokens_ids' ) , [additional_special_token_id] )
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
pass
| 149 | """simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def lowerCAmelCase__ ( _UpperCamelCase : Tuple , _UpperCamelCase : Optional[int]=False ) -> Any:
"""simple docstring"""
try:
snake_case = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
snake_case = default
else:
# KEY is set, convert it to True or False.
try:
snake_case = strtobool(_UpperCamelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"""If set, {key} must be yes or no.""" )
return _value
SCREAMING_SNAKE_CASE__ = parse_flag_from_env("RUN_SLOW", default=False)
def lowerCAmelCase__ ( _UpperCamelCase : List[Any] ) -> str:
"""simple docstring"""
return unittest.skip('Test was skipped' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : List[Any] ) -> List[str]:
"""simple docstring"""
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : List[Any] ) -> int:
"""simple docstring"""
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Tuple ) -> Tuple:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Dict ) -> Optional[int]:
"""simple docstring"""
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Dict ) -> Any:
"""simple docstring"""
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Tuple ) -> str:
"""simple docstring"""
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : List[str] ) -> Dict:
"""simple docstring"""
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : List[str] ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Dict ) -> int:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : str ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : str ) -> List[str]:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : str ) -> str:
"""simple docstring"""
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Dict ) -> List[str]:
"""simple docstring"""
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Dict=None , _UpperCamelCase : Dict=None ) -> int:
"""simple docstring"""
if test_case is None:
return partial(_UpperCamelCase , version=_UpperCamelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCamelCase ) , f"""test requires torch version >= {version}""" )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> int:
"""simple docstring"""
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> Any:
"""simple docstring"""
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCamelCase )
SCREAMING_SNAKE_CASE__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def lowerCAmelCase__ ( _UpperCamelCase : str ) -> List[str]:
"""simple docstring"""
return unittest.skipUnless(
_atleast_one_tracker_available , 'test requires at least one tracker to be available and for `comet_ml` to not be installed' , )(_UpperCamelCase )
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : Union[str, Any] = True
@classmethod
def snake_case ( cls ):
"""simple docstring"""
snake_case = tempfile.mkdtemp()
@classmethod
def snake_case ( cls ):
"""simple docstring"""
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def snake_case ( self ):
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob('**/*' ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(lowerCAmelCase )
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case = mocks if isinstance(lowerCAmelCase , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> Any:
"""simple docstring"""
snake_case = AcceleratorState()
snake_case = tensor[None].clone().to(state.device )
snake_case = gather(_UpperCamelCase ).cpu()
snake_case = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCamelCase ):
return False
return True
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = returncode
snake_case = stdout
snake_case = stderr
async def lowerCAmelCase__ ( _UpperCamelCase : Dict , _UpperCamelCase : Any ) -> List[Any]:
"""simple docstring"""
while True:
snake_case = await stream.readline()
if line:
callback(_UpperCamelCase )
else:
break
async def lowerCAmelCase__ ( _UpperCamelCase : Optional[Any] , _UpperCamelCase : Optional[Any]=None , _UpperCamelCase : List[Any]=None , _UpperCamelCase : Optional[Any]=None , _UpperCamelCase : List[str]=False , _UpperCamelCase : Optional[int]=False ) -> _RunOutput:
"""simple docstring"""
if echo:
print('\nRunning: ' , ' '.join(_UpperCamelCase ) )
snake_case = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCamelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCamelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
snake_case = []
snake_case = []
def tee(_UpperCamelCase : List[Any] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[str]="" ):
snake_case = line.decode('utf-8' ).rstrip()
sink.append(_UpperCamelCase )
if not quiet:
print(_UpperCamelCase , _UpperCamelCase , file=_UpperCamelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCamelCase : tee(_UpperCamelCase , _UpperCamelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCamelCase : tee(_UpperCamelCase , _UpperCamelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCamelCase , )
return _RunOutput(await p.wait() , _UpperCamelCase , _UpperCamelCase )
def lowerCAmelCase__ ( _UpperCamelCase : List[Any] , _UpperCamelCase : str=None , _UpperCamelCase : List[str]=None , _UpperCamelCase : Tuple=1_8_0 , _UpperCamelCase : Dict=False , _UpperCamelCase : Optional[Any]=True ) -> _RunOutput:
"""simple docstring"""
snake_case = asyncio.get_event_loop()
snake_case = loop.run_until_complete(
_stream_subprocess(_UpperCamelCase , env=_UpperCamelCase , stdin=_UpperCamelCase , timeout=_UpperCamelCase , quiet=_UpperCamelCase , echo=_UpperCamelCase ) )
snake_case = ' '.join(_UpperCamelCase )
if result.returncode > 0:
snake_case = '\n'.join(result.stderr )
raise RuntimeError(
f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
f"""The combined stderr from workers follows:\n{stderr}""" )
return result
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
pass
def lowerCAmelCase__ ( _UpperCamelCase : List[str] , _UpperCamelCase : Optional[Any]=False ) -> Optional[Any]:
"""simple docstring"""
try:
snake_case = subprocess.check_output(_UpperCamelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCamelCase , 'decode' ):
snake_case = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f"""Command `{" ".join(_UpperCamelCase )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 149 | 1 |
import math
import os
import sys
def lowercase( UpperCamelCase_ ) -> str:
'''simple docstring'''
UpperCamelCase = """"""
try:
with open(__lowerCAmelCase , """rb""" ) as binary_file:
UpperCamelCase = binary_file.read()
for dat in data:
UpperCamelCase = f"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print("""File not accessible""" )
sys.exit()
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> None:
'''simple docstring'''
lexicon.pop(__lowerCAmelCase )
UpperCamelCase = last_match_id
if math.loga(__lowerCAmelCase ).is_integer():
for curr_key in lexicon:
UpperCamelCase = """0""" + lexicon[curr_key]
UpperCamelCase = bin(__lowerCAmelCase )[2:]
def lowercase( UpperCamelCase_ ) -> str:
'''simple docstring'''
UpperCamelCase = {"""0""": """0""", """1""": """1"""}
UpperCamelCase , UpperCamelCase = """""", """"""
UpperCamelCase = len(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
UpperCamelCase = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
index += 1
UpperCamelCase = """"""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
UpperCamelCase = lexicon[curr_string]
result += last_match_id
return result
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> str:
'''simple docstring'''
UpperCamelCase = os.path.getsize(__lowerCAmelCase )
UpperCamelCase = bin(__lowerCAmelCase )[2:]
UpperCamelCase = len(__lowerCAmelCase )
return "0" * (length_length - 1) + file_length_binary + compressed
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> None:
'''simple docstring'''
UpperCamelCase = 8
try:
with open(__lowerCAmelCase , """wb""" ) as opened_file:
UpperCamelCase = [
to_write[i : i + byte_length]
for i in range(0 , len(__lowerCAmelCase ) , __lowerCAmelCase )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("""10000000""" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(__lowerCAmelCase , 2 ).to_bytes(1 , byteorder="""big""" ) )
except OSError:
print("""File not accessible""" )
sys.exit()
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> None:
'''simple docstring'''
UpperCamelCase = read_file_binary(__lowerCAmelCase )
UpperCamelCase = compress_data(__lowerCAmelCase )
UpperCamelCase = add_file_length(__lowerCAmelCase , __lowerCAmelCase )
write_file_binary(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 343 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=True ) -> List[Any]:
'''simple docstring'''
model.train()
lowercase_ = model(__lowerCAmelCase )
lowercase_ = F.mse_loss(__lowerCAmelCase , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=False ) -> List[Any]:
'''simple docstring'''
set_seed(42 )
lowercase_ = RegressionModel()
lowercase_ = deepcopy(__lowerCAmelCase )
lowercase_ = RegressionDataset(length=80 )
lowercase_ = DataLoader(__lowerCAmelCase , batch_size=16 )
model.to(accelerator.device )
if sched:
lowercase_ = AdamW(params=model.parameters() , lr=1E-3 )
lowercase_ = AdamW(params=ddp_model.parameters() , lr=1E-3 )
lowercase_ = LambdaLR(__lowerCAmelCase , lr_lambda=lambda __lowerCAmelCase : epoch**0.65 )
lowercase_ = LambdaLR(__lowerCAmelCase , lr_lambda=lambda __lowerCAmelCase : epoch**0.65 )
# Make a copy of `model`
if sched:
lowercase_ , lowercase_ , lowercase_ , lowercase_ = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
lowercase_ , lowercase_ = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ , lowercase_ , lowercase_ = get_training_setup(__lowerCAmelCase )
# Use a single batch
lowercase_ , lowercase_ = next(iter(__lowerCAmelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
lowercase_ , lowercase_ = accelerator.gather((ddp_input, ddp_target) )
lowercase_ , lowercase_ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(__lowerCAmelCase ):
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
# Sync grads
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
lowercase_ = ddp_input[torch.randperm(len(__lowerCAmelCase ) )]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ , lowercase_ , lowercase_ = get_training_setup(__lowerCAmelCase )
# Use a single batch
lowercase_ , lowercase_ = next(iter(__lowerCAmelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
lowercase_ , lowercase_ = accelerator.gather((ddp_input, ddp_target) )
lowercase_ , lowercase_ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(__lowerCAmelCase ):
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
# Sync grads
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
lowercase_ = ddp_input[torch.randperm(len(__lowerCAmelCase ) )]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase=False , __lowerCAmelCase=False ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = Accelerator(
split_batches=__lowerCAmelCase , dispatch_batches=__lowerCAmelCase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
lowercase_ , lowercase_ , lowercase_ = get_training_setup(__lowerCAmelCase )
for iteration, batch in enumerate(__lowerCAmelCase ):
lowercase_ , lowercase_ = batch.values()
# Gather the distributed inputs and targs for the base model
lowercase_ , lowercase_ = accelerator.gather((ddp_input, ddp_target) )
lowercase_ , lowercase_ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(__lowerCAmelCase ):
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(__lowerCAmelCase ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
lowercase_ = ddp_input[torch.randperm(len(__lowerCAmelCase ) )]
GradientState._reset_state()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase=False , __lowerCAmelCase=False ) -> Optional[int]:
'''simple docstring'''
lowercase_ = Accelerator(
split_batches=__lowerCAmelCase , dispatch_batches=__lowerCAmelCase , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = get_training_setup(__lowerCAmelCase , __lowerCAmelCase )
for iteration, batch in enumerate(__lowerCAmelCase ):
lowercase_ , lowercase_ = batch.values()
# Gather the distributed inputs and targs for the base model
lowercase_ , lowercase_ = accelerator.gather((ddp_input, ddp_target) )
lowercase_ , lowercase_ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__lowerCAmelCase )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(__lowerCAmelCase ):
step_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
lowercase_ = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__lowerCAmelCase ))
if accelerator.num_processes > 1:
check_model_parameters(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
GradientState._reset_state()
def _SCREAMING_SNAKE_CASE () -> Optional[Any]:
'''simple docstring'''
lowercase_ = Accelerator()
lowercase_ = RegressionDataset(length=80 )
lowercase_ = DataLoader(__lowerCAmelCase , batch_size=16 )
lowercase_ = RegressionDataset(length=96 )
lowercase_ = DataLoader(__lowerCAmelCase , batch_size=16 )
lowercase_ , lowercase_ = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(__lowerCAmelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(__lowerCAmelCase )
if iteration < len(__lowerCAmelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(__lowerCAmelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(__lowerCAmelCase )
if batch_num < len(__lowerCAmelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def _SCREAMING_SNAKE_CASE () -> List[str]:
'''simple docstring'''
lowercase_ = Accelerator()
lowercase_ = accelerator.state
if state.local_process_index == 0:
print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("""**Test NOOP `no_sync` context manager**""" )
test_noop_sync(__lowerCAmelCase )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("""**Test Distributed `no_sync` context manager**""" )
test_distributed_sync(__lowerCAmelCase )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation, """ , F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , )
test_gradient_accumulation(__lowerCAmelCase , __lowerCAmelCase )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("""<""" , """2.0""" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , """`split_batches=False`, `dispatch_batches=False`**""" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , )
test_gradient_accumulation_with_opt_and_scheduler(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 136 | 0 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__A : Any = logging.get_logger(__name__)
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
@staticmethod
def lowercase__ ( )->List[str]:
raise NotImplementedError
def lowercase__ ( self : List[str] , __UpperCamelCase : str , __UpperCamelCase : int , __UpperCamelCase : str , **__UpperCamelCase : Any )->str:
raise NotImplementedError
def lowercase__ ( self : Any , __UpperCamelCase : List[str] )->List[str]:
raise NotImplementedError
def lowercase__ ( self : List[Any] )->int:
if not self.is_available():
raise RuntimeError(
F'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def lowercase__ ( cls : Optional[Any] )->Optional[Any]:
return F'`pip install {cls.pip_package or cls.name}`'
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """optuna"""
@staticmethod
def lowercase__ ( )->int:
return is_optuna_available()
def lowercase__ ( self : Tuple , __UpperCamelCase : Any , __UpperCamelCase : int , __UpperCamelCase : str , **__UpperCamelCase : int )->Optional[Any]:
return run_hp_search_optuna(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : List[Any] , __UpperCamelCase : List[str] )->List[Any]:
return default_hp_space_optuna(__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """ray"""
UpperCamelCase__ = """'ray[tune]'"""
@staticmethod
def lowercase__ ( )->Optional[Any]:
return is_ray_available()
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : str , **__UpperCamelCase : str )->Union[str, Any]:
return run_hp_search_ray(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : Dict , __UpperCamelCase : List[str] )->Optional[int]:
return default_hp_space_ray(__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """sigopt"""
@staticmethod
def lowercase__ ( )->Optional[Any]:
return is_sigopt_available()
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : int , __UpperCamelCase : str , **__UpperCamelCase : List[str] )->List[Any]:
return run_hp_search_sigopt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : Optional[int] , __UpperCamelCase : Any )->Any:
return default_hp_space_sigopt(__UpperCamelCase )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """wandb"""
@staticmethod
def lowercase__ ( )->int:
return is_wandb_available()
def lowercase__ ( self : str , __UpperCamelCase : Tuple , __UpperCamelCase : int , __UpperCamelCase : str , **__UpperCamelCase : int )->Optional[Any]:
return run_hp_search_wandb(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
def lowercase__ ( self : List[str] , __UpperCamelCase : Union[str, Any] )->Tuple:
return default_hp_space_wandb(__UpperCamelCase )
__A : List[Any] = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_SCREAMING_SNAKE_CASE ) > 0:
_UpperCAmelCase = available_backends[0].name
if len(_SCREAMING_SNAKE_CASE ) > 1:
logger.info(
f'{len(_SCREAMING_SNAKE_CASE )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
f' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 368 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = CTRLTokenizer
UpperCamelCase__ = False
UpperCamelCase__ = False
def lowercase__ ( self : Dict )->str:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_UpperCAmelCase = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_UpperCAmelCase = dict(zip(__UpperCamelCase , range(len(__UpperCamelCase ) ) ) )
_UpperCAmelCase = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_UpperCAmelCase = {'''unk_token''': '''<unk>'''}
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_UpperCAmelCase = 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(__UpperCamelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__UpperCamelCase ) )
def lowercase__ ( self : str , **__UpperCamelCase : Union[str, Any] )->Any:
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **__UpperCamelCase )
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : Optional[int] )->Tuple:
_UpperCAmelCase = '''adapt react readapt apt'''
_UpperCAmelCase = '''adapt react readapt apt'''
return input_text, output_text
def lowercase__ ( self : Dict )->Optional[int]:
_UpperCAmelCase = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_UpperCAmelCase = '''adapt react readapt apt'''
_UpperCAmelCase = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_UpperCAmelCase = tokenizer.tokenize(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = tokens + [tokenizer.unk_token]
_UpperCAmelCase = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , __UpperCamelCase )
| 326 | 0 |
import numpy
# List of input, output pairs
lowercase__ : Tuple = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
lowercase__ : Any = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
lowercase__ : Optional[int] = [2, 4, 1, 5]
lowercase__ : List[Any] = len(train_data)
lowercase__ : Dict = 0.0_0_9
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__="train" ) -> Tuple:
return calculate_hypothesis_value(lowerCAmelCase_ , lowerCAmelCase_ ) - output(
lowerCAmelCase_ , lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> Dict:
lowerCAmelCase = 0
for i in range(len(lowerCAmelCase_ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> str:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__=m ) -> List[str]:
lowerCAmelCase = 0
for i in range(lowerCAmelCase_ ):
if index == -1:
summation_value += _error(lowerCAmelCase_ )
else:
summation_value += _error(lowerCAmelCase_ ) * train_data[i][0][index]
return summation_value
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> Tuple:
lowerCAmelCase = summation_of_cost_derivative(lowerCAmelCase_ , lowerCAmelCase_ ) / m
return cost_derivative_value
def SCREAMING_SNAKE_CASE_ ( ) -> List[str]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
lowerCAmelCase = 0.00_00_02
lowerCAmelCase = 0
lowerCAmelCase = 0
while True:
j += 1
lowerCAmelCase = [0, 0, 0, 0]
for i in range(0 , len(lowerCAmelCase_ ) ):
lowerCAmelCase = get_cost_derivative(i - 1 )
lowerCAmelCase = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
lowerCAmelCase_ , lowerCAmelCase_ , atol=lowerCAmelCase_ , rtol=lowerCAmelCase_ , ):
break
lowerCAmelCase = temp_parameter_vector
print(('''Number of iterations:''', j) )
def SCREAMING_SNAKE_CASE_ ( ) -> str:
for i in range(len(lowerCAmelCase_ ) ):
print(('''Actual output value:''', output(lowerCAmelCase_ , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(lowerCAmelCase_ , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print('''\nTesting gradient descent for a linear hypothesis function.\n''')
test_gradient_descent()
| 338 |
'''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
__lowerCAmelCase = None
__lowerCAmelCase = '''<''' 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
__lowerCAmelCase = [
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__ :
lowerCAmelCase : bool = True
lowerCAmelCase : Optional[str] = None
# Automatically constructed
lowerCAmelCase : ClassVar[str] = "PIL.Image.Image"
lowerCAmelCase : ClassVar[Any] = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase : str = field(default='Image' , init=_UpperCamelCase , repr=_UpperCamelCase )
def __call__( self : Union[str, Any] ):
return self.pa_type
def __lowercase ( self : Any ,_UpperCAmelCase : 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(_UpperCAmelCase ,_UpperCAmelCase ):
_a : Optional[Any] = np.array(_UpperCAmelCase )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
return {"path": value, "bytes": None}
elif isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
return {"path": None, "bytes": value}
elif isinstance(_UpperCAmelCase ,np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(_UpperCAmelCase )
elif isinstance(_UpperCAmelCase ,PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(_UpperCAmelCase )
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 __lowercase ( self : Optional[Any] ,_UpperCAmelCase : dict ,_UpperCAmelCase : Optional[int]=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:
_a : Dict = {}
_a , _a : str = 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(_UpperCAmelCase ):
_a : Any = PIL.Image.open(_UpperCAmelCase )
else:
_a : List[Any] = path.split('::' )[-1]
try:
_a : str = string_to_dict(_UpperCAmelCase ,config.HUB_DATASETS_URL )['repo_id']
_a : Optional[Any] = token_per_repo_id.get(_UpperCAmelCase )
except ValueError:
_a : int = None
with xopen(_UpperCAmelCase ,'rb' ,use_auth_token=_UpperCAmelCase ) as f:
_a : Tuple = BytesIO(f.read() )
_a : Union[str, Any] = PIL.Image.open(bytes_ )
else:
_a : Optional[int] = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def __lowercase ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value('binary' ),
"path": Value('string' ),
}
)
def __lowercase ( self : str ,_UpperCAmelCase : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
_a : Union[str, Any] = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.binary() )
_a : Union[str, Any] = pa.StructArray.from_arrays([bytes_array, storage] ,['bytes', 'path'] ,mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_a : List[str] = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : 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:
_a : Union[str, Any] = storage.field('bytes' )
else:
_a : Tuple = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.binary() )
if storage.type.get_field_index('path' ) >= 0:
_a : Union[str, Any] = storage.field('path' )
else:
_a : Dict = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : Optional[Any] = pa.StructArray.from_arrays([bytes_array, path_array] ,['bytes', 'path'] ,mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
_a : List[str] = pa.array(
[encode_np_array(np.array(_UpperCAmelCase ) )['bytes'] if arr is not None else None for arr in storage.to_pylist()] ,type=pa.binary() ,)
_a : int = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : Optional[Any] = pa.StructArray.from_arrays(
[bytes_array, path_array] ,['bytes', 'path'] ,mask=bytes_array.is_null() )
return array_cast(_UpperCAmelCase ,self.pa_type )
def __lowercase ( self : Dict ,_UpperCAmelCase : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(_UpperCAmelCase : Tuple ):
with xopen(_UpperCAmelCase ,'rb' ) as f:
_a : int = f.read()
return bytes_
_a : 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() ,)
_a : Optional[Any] = pa.array(
[os.path.basename(_UpperCAmelCase ) if path is not None else None for path in storage.field('path' ).to_pylist()] ,type=pa.string() ,)
_a : Dict = pa.StructArray.from_arrays([bytes_array, path_array] ,['bytes', 'path'] ,mask=bytes_array.is_null() )
return array_cast(_UpperCAmelCase ,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()
_a : Dict = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def __lowerCamelCase ( lowerCAmelCase_ ) -> bytes:
_a : Optional[int] = BytesIO()
if image.format in list_image_compression_formats():
_a : Optional[Any] = image.format
else:
_a : str = 'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
image.save(lowerCAmelCase_ , format=lowerCAmelCase_ )
return buffer.getvalue()
def __lowerCamelCase ( lowerCAmelCase_ ) -> dict:
if hasattr(lowerCAmelCase_ , 'filename' ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_ )}
def __lowerCamelCase ( lowerCAmelCase_ ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
_a : List[Any] = array.dtype
_a : Optional[int] = dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
_a : Union[str, Any] = dtype.kind
_a : Union[str, Any] = dtype.itemsize
_a : List[Any] = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
_a : Optional[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:
_a : Union[str, Any] = 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:
_a : str = dtype_byteorder + dtype_kind + str(lowerCAmelCase_ )
_a : List[Any] = np.dtype(lowerCAmelCase_ )
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}""" )
_a : Union[str, Any] = PIL.Image.fromarray(array.astype(lowerCAmelCase_ ) )
return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_ )}
def __lowerCamelCase ( lowerCAmelCase_ ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if objs:
_a , _a : Optional[Any] = first_non_null_value(lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(lowerCAmelCase_ , np.ndarray ):
_a : List[str] = no_op_if_value_is_null(lowerCAmelCase_ )
return [obj_to_image_dict_func(lowerCAmelCase_ ) for obj in objs]
elif isinstance(lowerCAmelCase_ , PIL.Image.Image ):
_a : List[str] = no_op_if_value_is_null(lowerCAmelCase_ )
return [obj_to_image_dict_func(lowerCAmelCase_ ) for obj in objs]
else:
return objs
else:
return objs
| 89 | 0 |
from __future__ import annotations
def __lowerCamelCase (UpperCAmelCase__ : int ):
SCREAMING_SNAKE_CASE = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def __lowerCamelCase ():
for base_num in range(9_9_9_9 , 4_9_9_9 , -1 ):
SCREAMING_SNAKE_CASE = 1_0_0_0_0_2 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_3_3 , 9_9 , -1 ):
SCREAMING_SNAKE_CASE = 1_0_0_2_0_0_3 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(f"""{solution() = }""")
| 206 | from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class lowercase :
lowercase__ : Dict = LEDConfig
lowercase__ : List[str] = {}
lowercase__ : Union[str, Any] = """gelu"""
def __init__( self : Tuple , _UpperCamelCase : List[str] , _UpperCamelCase : Dict=13 , _UpperCamelCase : Optional[int]=7 , _UpperCamelCase : int=True , _UpperCamelCase : List[Any]=False , _UpperCamelCase : Dict=99 , _UpperCamelCase : Optional[Any]=32 , _UpperCamelCase : Any=2 , _UpperCamelCase : List[str]=4 , _UpperCamelCase : Union[str, Any]=37 , _UpperCamelCase : str=0.1 , _UpperCamelCase : List[Any]=0.1 , _UpperCamelCase : Union[str, Any]=20 , _UpperCamelCase : str=2 , _UpperCamelCase : Optional[Any]=1 , _UpperCamelCase : Optional[int]=0 , _UpperCamelCase : int=4 , ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = eos_token_id
SCREAMING_SNAKE_CASE = pad_token_id
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
SCREAMING_SNAKE_CASE = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
SCREAMING_SNAKE_CASE = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __snake_case( self : int ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
SCREAMING_SNAKE_CASE = tf.concat([input_ids, eos_tensor] , axis=1 )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
SCREAMING_SNAKE_CASE = prepare_led_inputs_dict(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
SCREAMING_SNAKE_CASE = tf.concat(
[tf.zeros_like(_UpperCamelCase )[:, :-1], tf.ones_like(_UpperCamelCase )[:, -1:]] , axis=-1 , )
SCREAMING_SNAKE_CASE = global_attention_mask
return config, inputs_dict
def __snake_case( self : Any , _UpperCamelCase : Optional[int] , _UpperCamelCase : Tuple ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TFLEDModel(config=_UpperCamelCase ).get_decoder()
SCREAMING_SNAKE_CASE = inputs_dict["input_ids"]
SCREAMING_SNAKE_CASE = input_ids[:1, :]
SCREAMING_SNAKE_CASE = inputs_dict["attention_mask"][:1, :]
SCREAMING_SNAKE_CASE = 1
# first forward pass
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , attention_mask=_UpperCamelCase , use_cache=_UpperCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
SCREAMING_SNAKE_CASE = tf.concat([input_ids, next_tokens] , axis=-1 )
SCREAMING_SNAKE_CASE = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , attention_mask=_UpperCamelCase )[0]
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , attention_mask=_UpperCamelCase , past_key_values=_UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
SCREAMING_SNAKE_CASE = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
SCREAMING_SNAKE_CASE = output_from_no_past[:, -3:, random_slice_idx]
SCREAMING_SNAKE_CASE = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_UpperCamelCase , _UpperCamelCase , rtol=1e-3 )
def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Union[str, Any]=None , ):
if attention_mask is None:
SCREAMING_SNAKE_CASE = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
SCREAMING_SNAKE_CASE = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class lowercase ( a , a , unittest.TestCase ):
lowercase__ : Optional[int] = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
lowercase__ : List[Any] = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
lowercase__ : int = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase__ : List[Any] = True
lowercase__ : List[str] = False
lowercase__ : List[str] = False
lowercase__ : Union[str, Any] = False
def __snake_case( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TFLEDModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=_UpperCamelCase )
def __snake_case( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def __snake_case( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase )
def __snake_case( self : Dict ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = tf.zeros_like(inputs_dict["attention_mask"] )
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["global_attention_mask"] , )
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = self.model_tester.seq_length
SCREAMING_SNAKE_CASE = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_UpperCamelCase : Dict ):
SCREAMING_SNAKE_CASE = outputs.decoder_attentions
self.assertEqual(len(_UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_UpperCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = [t.numpy() for t in outputs.encoder_attentions]
SCREAMING_SNAKE_CASE = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = model_class(_UpperCamelCase )
SCREAMING_SNAKE_CASE = model(self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
SCREAMING_SNAKE_CASE = len(_UpperCamelCase )
self.assertEqual(config.output_hidden_states , _UpperCamelCase )
check_encoder_attentions_output(_UpperCamelCase )
if self.is_encoder_decoder:
SCREAMING_SNAKE_CASE = model_class(_UpperCamelCase )
SCREAMING_SNAKE_CASE = model(self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , _UpperCamelCase )
check_decoder_attentions_output(_UpperCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = model_class(_UpperCamelCase )
SCREAMING_SNAKE_CASE = model(self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , _UpperCamelCase )
check_encoder_attentions_output(_UpperCamelCase )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = model_class(_UpperCamelCase )
SCREAMING_SNAKE_CASE = model(self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_UpperCamelCase ) )
self.assertEqual(model.config.output_hidden_states , _UpperCamelCase )
check_encoder_attentions_output(_UpperCamelCase )
@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
def __snake_case( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
pass
def __snake_case( self : str ) -> str:
'''simple docstring'''
pass
def __lowerCamelCase (UpperCAmelCase__ : Optional[int] ):
return tf.constant(UpperCAmelCase__ , dtype=tf.intaa )
_lowerCamelCase : str = 1e-4
@slow
@require_tf
class lowercase ( unittest.TestCase ):
def __snake_case( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led
# change to intended input here
SCREAMING_SNAKE_CASE = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
SCREAMING_SNAKE_CASE = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
SCREAMING_SNAKE_CASE = prepare_led_inputs_dict(model.config , _UpperCamelCase , _UpperCamelCase )
SCREAMING_SNAKE_CASE = model(**_UpperCamelCase )[0]
SCREAMING_SNAKE_CASE = (1, 1_024, 768)
self.assertEqual(output.shape , _UpperCamelCase )
# change to expected output here
SCREAMING_SNAKE_CASE = tf.convert_to_tensor(
[[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]] , )
tf.debugging.assert_near(output[:, :3, :3] , _UpperCamelCase , atol=1e-3 )
def __snake_case( self : Any ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )
# change to intended input here
SCREAMING_SNAKE_CASE = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
SCREAMING_SNAKE_CASE = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
SCREAMING_SNAKE_CASE = prepare_led_inputs_dict(model.config , _UpperCamelCase , _UpperCamelCase )
SCREAMING_SNAKE_CASE = model(**_UpperCamelCase )[0]
SCREAMING_SNAKE_CASE = (1, 1_024, model.config.vocab_size)
self.assertEqual(output.shape , _UpperCamelCase )
# change to expected output here
SCREAMING_SNAKE_CASE = tf.convert_to_tensor(
[[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]] , )
tf.debugging.assert_near(output[:, :3, :3] , _UpperCamelCase , atol=1e-3 , rtol=1e-3 )
| 206 | 1 |
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 A_ ( _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __lowercase :
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : int):
pass
def _SCREAMING_SNAKE_CASE ( self : str):
pass
def _SCREAMING_SNAKE_CASE ( self : Dict):
pass
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Dict=None , **lowerCAmelCase__ : Tuple):
SCREAMING_SNAKE_CASE_: int = VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = TFVisionTextDualEncoderModel(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim))
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim))
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : int , lowerCAmelCase__ : str , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any=None , **lowerCAmelCase__ : List[str]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: int = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase__ , text_model=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim))
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim))
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int]=None , **lowerCAmelCase__ : str):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Dict = {"vision_model": vision_model, "text_model": text_model}
SCREAMING_SNAKE_CASE_: Dict = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim))
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim))
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : int , lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Union[str, Any]=None , **lowerCAmelCase__ : Dict):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase__ , text_model=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = after_output[0].numpy()
SCREAMING_SNAKE_CASE_: Union[str, Any] = np.amax(np.abs(out_a - out_a))
self.assertLessEqual(lowerCAmelCase__ , 1E-5)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[Any]=None , **lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase__ , text_model=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Tuple = model(
input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , output_attentions=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Tuple = output.vision_model_output.attentions
self.assertEqual(len(lowerCAmelCase__) , vision_config.num_hidden_layers)
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE_: List[str] = to_atuple(vision_model.config.image_size)
SCREAMING_SNAKE_CASE_: Dict = to_atuple(vision_model.config.patch_size)
SCREAMING_SNAKE_CASE_: int = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
SCREAMING_SNAKE_CASE_: str = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len))
SCREAMING_SNAKE_CASE_: Dict = output.text_model_output.attentions
self.assertEqual(len(lowerCAmelCase__) , text_config.num_hidden_layers)
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : float):
SCREAMING_SNAKE_CASE_: int = np.abs((a - b)).max()
self.assertLessEqual(lowerCAmelCase__ , lowerCAmelCase__ , F"Difference between torch and flax is {diff} (>= {tol}).")
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : str):
SCREAMING_SNAKE_CASE_: Tuple = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: List[Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : str):
SCREAMING_SNAKE_CASE_: Any = self.prepare_config_and_inputs()
self.check_save_load(**lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_: Any = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowerCAmelCase__)
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: int = self.get_pretrained_model_and_inputs()
SCREAMING_SNAKE_CASE_: Dict = model_a(**lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Tuple = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Any = model_a(**lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = after_outputs[0].numpy()
SCREAMING_SNAKE_CASE_: Optional[Any] = np.amax(np.abs(out_a - out_a))
self.assertLessEqual(lowerCAmelCase__ , 1E-5)
@require_tf
class __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: Tuple = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert")
SCREAMING_SNAKE_CASE_: List[str] = 13
SCREAMING_SNAKE_CASE_: int = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
])
SCREAMING_SNAKE_CASE_: List[str] = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size)
SCREAMING_SNAKE_CASE_: Union[str, Any] = random_attention_mask([batch_size, 4])
SCREAMING_SNAKE_CASE_: Dict = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any]):
SCREAMING_SNAKE_CASE_: Any = TFViTModel(lowerCAmelCase__ , name="vision_model")
SCREAMING_SNAKE_CASE_: Optional[Any] = TFBertModel(lowerCAmelCase__ , name="text_model")
return vision_model, text_model
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_: Union[str, Any] = TFViTModelTester(self)
SCREAMING_SNAKE_CASE_: Union[str, Any] = TFBertModelTester(self)
SCREAMING_SNAKE_CASE_: str = vit_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_: List[Any] = bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] = vision_config_and_inputs
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
): List[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 __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Tuple):
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
SCREAMING_SNAKE_CASE_: Any = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta")
SCREAMING_SNAKE_CASE_: Optional[int] = 13
SCREAMING_SNAKE_CASE_: Optional[int] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
])
SCREAMING_SNAKE_CASE_: Union[str, Any] = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size)
SCREAMING_SNAKE_CASE_: Dict = random_attention_mask([batch_size, 4])
SCREAMING_SNAKE_CASE_: Optional[int] = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[Any]=None , **lowerCAmelCase__ : str):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase__ , text_model=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = model(
input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , output_attentions=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = output.vision_model_output.attentions
self.assertEqual(len(lowerCAmelCase__) , 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)
SCREAMING_SNAKE_CASE_: Dict = to_atuple(vision_model.config.image_size)
SCREAMING_SNAKE_CASE_: int = to_atuple(vision_model.config.patch_size)
SCREAMING_SNAKE_CASE_: List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
SCREAMING_SNAKE_CASE_: Union[str, Any] = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len))
SCREAMING_SNAKE_CASE_: List[Any] = output.text_model_output.attentions
self.assertEqual(len(lowerCAmelCase__) , text_config.num_hidden_layers)
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase__ : int , lowerCAmelCase__ : str):
SCREAMING_SNAKE_CASE_: Optional[int] = TFDeiTModel(lowerCAmelCase__ , name="vision_model")
SCREAMING_SNAKE_CASE_: Optional[int] = TFRobertaModel(lowerCAmelCase__ , name="text_model")
return vision_model, text_model
def _SCREAMING_SNAKE_CASE ( self : Any):
SCREAMING_SNAKE_CASE_: Dict = TFDeiTModelTester(self)
SCREAMING_SNAKE_CASE_: List[str] = TFRobertaModelTester(self)
SCREAMING_SNAKE_CASE_: Optional[Any] = vit_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_: Union[str, Any] = bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str = vision_config_and_inputs
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
): 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 __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Tuple):
SCREAMING_SNAKE_CASE_: str = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert")
SCREAMING_SNAKE_CASE_: List[str] = 13
SCREAMING_SNAKE_CASE_: Tuple = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
])
SCREAMING_SNAKE_CASE_: Tuple = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size)
SCREAMING_SNAKE_CASE_: Dict = random_attention_mask([batch_size, 4])
SCREAMING_SNAKE_CASE_: Any = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: List[str] = TFCLIPVisionModel(lowerCAmelCase__ , name="vision_model")
SCREAMING_SNAKE_CASE_: List[str] = TFBertModel(lowerCAmelCase__ , name="text_model")
return vision_model, text_model
def _SCREAMING_SNAKE_CASE ( self : List[str]):
SCREAMING_SNAKE_CASE_: Optional[int] = TFCLIPVisionModelTester(self)
SCREAMING_SNAKE_CASE_: Any = TFBertModelTester(self)
SCREAMING_SNAKE_CASE_: Optional[int] = clip_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_: str = bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] = vision_config_and_inputs
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
): List[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_vision
@require_tf
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
SCREAMING_SNAKE_CASE_: Optional[int] = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")
SCREAMING_SNAKE_CASE_: Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
SCREAMING_SNAKE_CASE_: int = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=lowerCAmelCase__ , padding=lowerCAmelCase__ , return_tensors="np")
SCREAMING_SNAKE_CASE_: List[str] = model(**lowerCAmelCase__)
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
SCREAMING_SNAKE_CASE_: Optional[Any] = np.array([[1.228_4727, 0.310_4122]])
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , lowerCAmelCase__ , atol=1E-3))
| 13 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase__ : int = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'PatchingSpec',
],
'convert': ['export', 'validate_model_outputs'],
'features': ['FeaturesManager'],
'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
UpperCamelCase__ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 344 | 0 |
import os
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
with open(os.path.dirname(_UpperCamelCase ) + '''/grid.txt''' ) as f:
snake_case_ : Optional[int] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_UpperCamelCase ) for x in f.readline().split()] )
snake_case_ : Dict = 0
# right
for i in range(20 ):
for j in range(17 ):
snake_case_ : Dict = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
snake_case_ : Any = temp
# down
for i in range(17 ):
for j in range(20 ):
snake_case_ : Tuple = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
snake_case_ : Union[str, Any] = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
snake_case_ : Any = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
snake_case_ : List[Any] = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
snake_case_ : Tuple = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
snake_case_ : str = temp
return maximum
if __name__ == "__main__":
print(solution())
| 279 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __lowerCAmelCase ( nn.Module ):
lowerCamelCase_ : int
lowerCamelCase_ : int
lowerCamelCase_ : float = 0.0
lowerCamelCase_ : int = 1
lowerCamelCase_ : int = 1
lowerCamelCase_ : bool = True
lowerCamelCase_ : bool = False
lowerCamelCase_ : bool = False
lowerCamelCase_ : bool = False
lowerCamelCase_ : jnp.dtype = jnp.floataa
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Tuple = []
snake_case_ : List[str] = []
for i in range(self.num_layers ):
snake_case_ : Tuple = self.in_channels if i == 0 else self.out_channels
snake_case_ : Dict = FlaxResnetBlockaD(
in_channels=__magic_name__ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
snake_case_ : str = 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(__magic_name__ )
snake_case_ : Union[str, Any] = resnets
snake_case_ : Union[str, Any] = attentions
if self.add_downsample:
snake_case_ : List[str] = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=True ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = ()
for resnet, attn in zip(self.resnets , self.attentions ):
snake_case_ : Optional[Any] = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
snake_case_ : List[str] = attn(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
output_states += (hidden_states,)
if self.add_downsample:
snake_case_ : Union[str, Any] = self.downsamplers_a(__magic_name__ )
output_states += (hidden_states,)
return hidden_states, output_states
class __lowerCAmelCase ( nn.Module ):
lowerCamelCase_ : int
lowerCamelCase_ : int
lowerCamelCase_ : float = 0.0
lowerCamelCase_ : int = 1
lowerCamelCase_ : bool = True
lowerCamelCase_ : jnp.dtype = jnp.floataa
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[Any] = []
for i in range(self.num_layers ):
snake_case_ : List[Any] = self.in_channels if i == 0 else self.out_channels
snake_case_ : Tuple = FlaxResnetBlockaD(
in_channels=__magic_name__ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
snake_case_ : Dict = resnets
if self.add_downsample:
snake_case_ : Optional[int] = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__(self , __magic_name__ , __magic_name__ , __magic_name__=True ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = ()
for resnet in self.resnets:
snake_case_ : List[Any] = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
output_states += (hidden_states,)
if self.add_downsample:
snake_case_ : str = self.downsamplers_a(__magic_name__ )
output_states += (hidden_states,)
return hidden_states, output_states
class __lowerCAmelCase ( nn.Module ):
lowerCamelCase_ : int
lowerCamelCase_ : int
lowerCamelCase_ : int
lowerCamelCase_ : float = 0.0
lowerCamelCase_ : int = 1
lowerCamelCase_ : int = 1
lowerCamelCase_ : bool = True
lowerCamelCase_ : bool = False
lowerCamelCase_ : bool = False
lowerCamelCase_ : bool = False
lowerCamelCase_ : jnp.dtype = jnp.floataa
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = []
snake_case_ : Optional[Any] = []
for i in range(self.num_layers ):
snake_case_ : List[Any] = self.in_channels if (i == self.num_layers - 1) else self.out_channels
snake_case_ : Optional[int] = self.prev_output_channel if i == 0 else self.out_channels
snake_case_ : Dict = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
snake_case_ : List[str] = 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(__magic_name__ )
snake_case_ : List[Any] = resnets
snake_case_ : Tuple = attentions
if self.add_upsample:
snake_case_ : List[Any] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=True ) -> Union[str, Any]:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
snake_case_ : Dict = res_hidden_states_tuple[-1]
snake_case_ : List[Any] = res_hidden_states_tuple[:-1]
snake_case_ : Optional[int] = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
snake_case_ : Tuple = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
snake_case_ : Tuple = attn(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
if self.add_upsample:
snake_case_ : Optional[Any] = self.upsamplers_a(__magic_name__ )
return hidden_states
class __lowerCAmelCase ( nn.Module ):
lowerCamelCase_ : int
lowerCamelCase_ : int
lowerCamelCase_ : int
lowerCamelCase_ : float = 0.0
lowerCamelCase_ : int = 1
lowerCamelCase_ : bool = True
lowerCamelCase_ : jnp.dtype = jnp.floataa
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Union[str, Any] = []
for i in range(self.num_layers ):
snake_case_ : Tuple = self.in_channels if (i == self.num_layers - 1) else self.out_channels
snake_case_ : Optional[Any] = self.prev_output_channel if i == 0 else self.out_channels
snake_case_ : int = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
snake_case_ : Tuple = resnets
if self.add_upsample:
snake_case_ : List[str] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=True ) -> List[Any]:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
snake_case_ : Tuple = res_hidden_states_tuple[-1]
snake_case_ : List[Any] = res_hidden_states_tuple[:-1]
snake_case_ : Dict = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
snake_case_ : Optional[Any] = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
if self.add_upsample:
snake_case_ : Optional[int] = self.upsamplers_a(__magic_name__ )
return hidden_states
class __lowerCAmelCase ( nn.Module ):
lowerCamelCase_ : int
lowerCamelCase_ : float = 0.0
lowerCamelCase_ : int = 1
lowerCamelCase_ : int = 1
lowerCamelCase_ : bool = False
lowerCamelCase_ : bool = False
lowerCamelCase_ : jnp.dtype = jnp.floataa
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
snake_case_ : int = []
for _ in range(self.num_layers ):
snake_case_ : 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(__magic_name__ )
snake_case_ : Dict = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
snake_case_ : Optional[Any] = resnets
snake_case_ : Optional[int] = attentions
def __call__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=True ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.resnets[0](__magic_name__ , __magic_name__ )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
snake_case_ : Tuple = attn(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
snake_case_ : Union[str, Any] = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
return hidden_states
| 279 | 1 |
'''simple docstring'''
import numpy as np
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
snake_case_ = np.array(lowercase_ )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
# compute the shape of the output matrix
snake_case_ = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
snake_case_ = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
snake_case_ = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
snake_case_ = 0
snake_case_ = 0
return updated_arr
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
snake_case_ = np.array(lowercase_ )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
snake_case_ = 0
# compute the shape of the output matrix
snake_case_ = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
snake_case_ = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
snake_case_ = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
snake_case_ = 0
snake_case_ = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name='avgpooling', verbose=True)
# Loading the image
a : List[Any] = Image.open('path_to_image')
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 56 |
import argparse
import os
import re
A_ : List[str] = 'src/diffusers'
# Pattern that looks at the indentation in a line.
A_ : Union[str, Any] = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A_ : int = re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
A_ : Optional[int] = re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
A_ : List[Any] = re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
A_ : List[str] = re.compile(r'\[([^\]]+)\]')
def UpperCamelCase (lowercase_: List[str] ) -> Dict:
A__ : Optional[Any] = _re_indent.search(lowercase_ )
return "" if search is None else search.groups()[0]
def UpperCamelCase (lowercase_: Dict , lowercase_: Any="" , lowercase_: Any=None , lowercase_: Any=None ) -> Tuple:
A__ : Optional[Any] = 0
A__ : str = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(lowercase_ ):
index += 1
A__ : Tuple = ["""\n""".join(lines[:index] )]
else:
A__ : Optional[Any] = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
A__ : Union[str, Any] = [lines[index]]
index += 1
while index < len(lowercase_ ) and (end_prompt is None or not lines[index].startswith(lowercase_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(lowercase_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(lowercase_ ) )
if index < len(lowercase_ ) - 1:
A__ : Union[str, Any] = [lines[index + 1]]
index += 1
else:
A__ : List[Any] = []
else:
blocks.append("""\n""".join(lowercase_ ) )
A__ : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(lowercase_ ) > 0:
blocks.append("""\n""".join(lowercase_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(lowercase_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def UpperCamelCase (lowercase_: str ) -> str:
def _inner(lowercase_: Union[str, Any] ):
return key(lowercase_ ).lower().replace("""_""" , """""" )
return _inner
def UpperCamelCase (lowercase_: int , lowercase_: Any=None ) -> str:
# If no key is provided, we use a noop.
def noop(lowercase_: Any ):
return x
if key is None:
A__ : Optional[Any] = noop
# Constants are all uppercase, they go first.
A__ : Optional[int] = [obj for obj in objects if key(lowercase_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
A__ : List[Any] = [obj for obj in objects if key(lowercase_ )[0].isupper() and not key(lowercase_ ).isupper()]
# Functions begin with a lowercase, they go last.
A__ : Tuple = [obj for obj in objects if not key(lowercase_ )[0].isupper()]
A__ : Any = ignore_underscore(lowercase_ )
return sorted(lowercase_ , key=lowercase_ ) + sorted(lowercase_ , key=lowercase_ ) + sorted(lowercase_ , key=lowercase_ )
def UpperCamelCase (lowercase_: List[Any] ) -> List[Any]:
# This inner function sort imports between [ ].
def _replace(lowercase_: List[Any] ):
A__ : Tuple = match.groups()[0]
if "," not in imports:
return f"""[{imports}]"""
A__ : Optional[int] = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
A__ : Any = keys[:-1]
return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(lowercase_ )] ) + "]"
A__ : Dict = import_statement.split("""\n""" )
if len(lowercase_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
A__ : List[str] = 2 if lines[1].strip() == """[""" else 1
A__ : Any = [(i, _re_strip_line.search(lowercase_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
A__ : Any = sort_objects(lowercase_ , key=lambda lowercase_ : x[1] )
A__ : int = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(lowercase_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
A__ : Union[str, Any] = _re_bracket_content.sub(_replace , lines[1] )
else:
A__ : Any = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
A__ : Tuple = keys[:-1]
A__ : List[Any] = get_indent(lines[1] ) + """, """.join([f"""\"{k}\"""" for k in sort_objects(lowercase_ )] )
return "\n".join(lowercase_ )
else:
# Finally we have to deal with imports fitting on one line
A__ : int = _re_bracket_content.sub(_replace , lowercase_ )
return import_statement
def UpperCamelCase (lowercase_: Optional[int] , lowercase_: str=True ) -> Any:
with open(lowercase_ , """r""" ) as f:
A__ : Optional[int] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
A__ : Tuple = split_code_in_indented_blocks(
lowercase_ , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(lowercase_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
A__ : int = main_blocks[block_idx]
A__ : Optional[Any] = block.split("""\n""" )
# Get to the start of the imports.
A__ : Any = 0
while line_idx < len(lowercase_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
A__ : Optional[Any] = len(lowercase_ )
else:
line_idx += 1
if line_idx >= len(lowercase_ ):
continue
# Ignore beginning and last line: they don't contain anything.
A__ : Union[str, Any] = """\n""".join(block_lines[line_idx:-1] )
A__ : List[Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
A__ : Union[str, Any] = split_code_in_indented_blocks(lowercase_ , indent_level=lowercase_ )
# We have two categories of import key: list or _import_structure[key].append/extend
A__ : Optional[Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
A__ : int = [(pattern.search(lowercase_ ).groups()[0] if pattern.search(lowercase_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
A__ : int = [(i, key) for i, key in enumerate(lowercase_ ) if key is not None]
A__ : List[Any] = [x[0] for x in sorted(lowercase_ , key=lambda lowercase_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
A__ : Optional[int] = 0
A__ : Any = []
for i in range(len(lowercase_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
A__ : Any = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(lowercase_ )
count += 1
# And we put our main block back together with its first and last line.
A__ : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(lowercase_ ):
if check_only:
return True
else:
print(f"""Overwriting {file}.""" )
with open(lowercase_ , """w""" ) as f:
f.write("""\n""".join(lowercase_ ) )
def UpperCamelCase (lowercase_: Any=True ) -> Any:
A__ : Dict = []
for root, _, files in os.walk(lowercase_ ):
if "__init__.py" in files:
A__ : List[Any] = sort_imports(os.path.join(lowercase_ , """__init__.py""" ) , check_only=lowercase_ )
if result:
A__ : Optional[int] = [os.path.join(lowercase_ , """__init__.py""" )]
if len(lowercase_ ) > 0:
raise ValueError(f"""Would overwrite {len(lowercase_ )} files, run `make style`.""" )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
A_ : List[str] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 192 | 0 |
'''simple docstring'''
def a ( __a , __a , __a ) -> list:
'''simple docstring'''
UpperCamelCase__ :Tuple = len(__a )
UpperCamelCase__ :Tuple = [[0] * n for i in range(__a )]
for i in range(__a ):
UpperCamelCase__ :Optional[Any] = y_points[i]
for i in range(2 , __a ):
for j in range(__a , __a ):
UpperCamelCase__ :int = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod() | 219 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file # noqa: F401
from .default import default_command_parser
from .update import update_command_parser
def a ( __a=None ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = argparse.ArgumentParser(add_help=__a , allow_abbrev=__a )
# The main config parser
UpperCamelCase__ :str = config_command_parser(__a )
# The subparser to add commands to
UpperCamelCase__ :Union[str, Any] = config_parser.add_subparsers(title='''subcommands''' , dest='''subcommand''' )
# Then add other parsers with the parent parser
default_command_parser(__a , parents=[parent_parser] )
update_command_parser(__a , parents=[parent_parser] )
return config_parser
def a ( ) -> Any:
'''simple docstring'''
UpperCamelCase__ :int = get_config_parser()
UpperCamelCase__ :List[Any] = config_parser.parse_args()
if not hasattr(__a , '''func''' ):
config_parser.print_help()
exit(1 )
# Run
args.func(__a )
if __name__ == "__main__":
main() | 219 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__UpperCamelCase = logging.getLogger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> List[str]:
# save results
if os.path.exists(UpperCamelCase__ ):
if os.path.exists(os.path.join(UpperCamelCase__ , 'config.json' ) ) and os.path.isfile(
os.path.join(UpperCamelCase__ , 'config.json' ) ):
os.remove(os.path.join(UpperCamelCase__ , 'config.json' ) )
if os.path.exists(os.path.join(UpperCamelCase__ , 'pytorch_model.bin' ) ) and os.path.isfile(
os.path.join(UpperCamelCase__ , 'pytorch_model.bin' ) ):
os.remove(os.path.join(UpperCamelCase__ , 'pytorch_model.bin' ) )
else:
os.makedirs(UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase=False ) -> Tuple:
snake_case_ = 2
if unlogit:
snake_case_ = torch.pow(UpperCamelCase__ , UpperCamelCase__ )
snake_case_ = p * torch.log(UpperCamelCase__ )
snake_case_ = 0
return -plogp.sum(dim=-1 )
def UpperCAmelCase ( UpperCAmelCase ) -> Optional[Any]:
logger.info('lv, h >\t' + '\t'.join(f'{x + 1}' for x in range(len(UpperCamelCase__ ) ) ) )
for row in range(len(UpperCamelCase__ ) ):
if tensor.dtype != torch.long:
logger.info(f'layer {row + 1}:\t' + '\t'.join(f'{x:.5f}' for x in tensor[row].cpu().data ) )
else:
logger.info(f'layer {row + 1}:\t' + '\t'.join(f'{x:d}' for x in tensor[row].cpu().data ) )
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=None , UpperCAmelCase=False ) -> Optional[Any]:
snake_case_ = model.config.num_hidden_layers, model.config.num_attention_heads
snake_case_ = torch.zeros(UpperCamelCase__ , UpperCamelCase__ ).to(args.device )
snake_case_ = torch.zeros(UpperCamelCase__ , UpperCamelCase__ ).to(args.device )
if head_mask is None:
snake_case_ = torch.ones(UpperCamelCase__ , UpperCamelCase__ ).to(args.device )
head_mask.requires_grad_(requires_grad=UpperCamelCase__ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
snake_case_ = None
snake_case_ = 0.0
snake_case_ = 0.0
for step, inputs in enumerate(tqdm(UpperCamelCase__ , desc='Iteration' , disable=args.local_rank not in [-1, 0] ) ):
snake_case_ = tuple(t.to(args.device ) for t in inputs )
(snake_case_ ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
snake_case_ = model(UpperCamelCase__ , labels=UpperCamelCase__ , head_mask=UpperCamelCase__ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
snake_case_ = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(UpperCamelCase__ ):
snake_case_ = entropy(attn.detach() , UpperCamelCase__ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(UpperCamelCase__ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
snake_case_ = 2
snake_case_ = torch.pow(torch.pow(UpperCamelCase__ , UpperCamelCase__ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
snake_case_ = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('Attention entropies' )
print_ad_tensor(UpperCamelCase__ )
if compute_importance:
logger.info('Head importance scores' )
print_ad_tensor(UpperCamelCase__ )
logger.info('Head ranked by importance scores' )
snake_case_ = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
snake_case_ = torch.arange(
head_importance.numel() , device=args.device )
snake_case_ = head_ranks.view_as(UpperCamelCase__ )
print_ad_tensor(UpperCamelCase__ )
return attn_entropy, head_importance, total_loss
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
snake_case_ = compute_heads_importance(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , compute_entropy=UpperCamelCase__ )
snake_case_ = 1 / loss # instead of downsteam score use the LM loss
logger.info('Pruning: original score: %f, threshold: %f' , UpperCamelCase__ , original_score * args.masking_threshold )
snake_case_ = torch.ones_like(UpperCamelCase__ )
snake_case_ = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
snake_case_ = original_score
while current_score >= original_score * args.masking_threshold:
snake_case_ = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
snake_case_ = float('Inf' )
snake_case_ = head_importance.view(-1 ).sort()[1]
if len(UpperCamelCase__ ) <= num_to_mask:
print('BREAK BY num_to_mask' )
break
# mask heads
snake_case_ = current_heads_to_mask[:num_to_mask]
logger.info('Heads to mask: %s' , str(current_heads_to_mask.tolist() ) )
snake_case_ = new_head_mask.view(-1 )
snake_case_ = 0.0
snake_case_ = new_head_mask.view_as(UpperCamelCase__ )
snake_case_ = new_head_mask.clone().detach()
print_ad_tensor(UpperCamelCase__ )
# Compute metric and head importance again
snake_case_ = compute_heads_importance(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , compute_entropy=UpperCamelCase__ , head_mask=UpperCamelCase__ )
snake_case_ = 1 / loss
logger.info(
'Masking: current score: %f, remaining heads %d (%.1f percents)' , UpperCamelCase__ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('Final head mask' )
print_ad_tensor(UpperCamelCase__ )
np.save(os.path.join(args.output_dir , 'head_mask.npy' ) , head_mask.detach().cpu().numpy() )
return head_mask
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Dict:
snake_case_ = datetime.now()
snake_case_ = compute_heads_importance(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , compute_entropy=UpperCamelCase__ , compute_importance=UpperCamelCase__ , head_mask=UpperCamelCase__ )
snake_case_ = 1 / loss
snake_case_ = datetime.now() - before_time
snake_case_ = sum(p.numel() for p in model.parameters() )
snake_case_ = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(UpperCamelCase__ ) )
}
for k, v in heads_to_prune.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
snake_case_ = [
v,
]
assert sum(len(UpperCamelCase__ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(UpperCamelCase__ )
snake_case_ = sum(p.numel() for p in model.parameters() )
snake_case_ = datetime.now()
snake_case_ = compute_heads_importance(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , compute_entropy=UpperCamelCase__ , compute_importance=UpperCamelCase__ , head_mask=UpperCamelCase__ , actually_pruned=UpperCamelCase__ , )
snake_case_ = 1 / loss
snake_case_ = datetime.now() - before_time
logger.info(
'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)' , UpperCamelCase__ , UpperCamelCase__ , pruned_num_params / original_num_params * 100 , )
logger.info('Pruning: score with masking: %f score with pruning: %f' , UpperCamelCase__ , UpperCamelCase__ )
logger.info('Pruning: speed ratio (original timing / new timing): %f percents' , original_time / new_time * 100 )
save_model(UpperCamelCase__ , args.output_dir )
def UpperCAmelCase ( ) -> Union[str, Any]:
snake_case_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--data_dir' , default=UpperCamelCase__ , type=UpperCamelCase__ , required=UpperCamelCase__ , help='The input data dir. Should contain the .tsv files (or other data files) for the task.' , )
parser.add_argument(
'--model_name_or_path' , default=UpperCamelCase__ , type=UpperCamelCase__ , required=UpperCamelCase__ , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--output_dir' , default=UpperCamelCase__ , type=UpperCamelCase__ , required=UpperCamelCase__ , help='The output directory where the model predictions and checkpoints will be written.' , )
# Other parameters
parser.add_argument(
'--config_name' , default='' , type=UpperCamelCase__ , help='Pretrained config name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--tokenizer_name' , default='' , type=UpperCamelCase__ , help='Pretrained tokenizer name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--cache_dir' , default=UpperCamelCase__ , type=UpperCamelCase__ , help='Where do you want to store the pre-trained models downloaded from s3' , )
parser.add_argument(
'--data_subset' , type=UpperCamelCase__ , default=-1 , help='If > 0: limit the data to a subset of data_subset instances.' )
parser.add_argument(
'--overwrite_output_dir' , action='store_true' , help='Whether to overwrite data in output directory' )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
parser.add_argument(
'--dont_normalize_importance_by_layer' , action='store_true' , help='Don\'t normalize importance score by layers' )
parser.add_argument(
'--dont_normalize_global_importance' , action='store_true' , help='Don\'t normalize all importance scores between 0 and 1' , )
parser.add_argument(
'--try_masking' , action='store_true' , help='Whether to try to mask head until a threshold of accuracy.' )
parser.add_argument(
'--masking_threshold' , default=0.9 , type=UpperCamelCase__ , help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).' , )
parser.add_argument(
'--masking_amount' , default=0.1 , type=UpperCamelCase__ , help='Amount to heads to masking at each masking step.' )
parser.add_argument('--metric_name' , default='acc' , type=UpperCamelCase__ , help='Metric to use for head masking.' )
parser.add_argument(
'--max_seq_length' , default=128 , type=UpperCamelCase__ , help=(
'The maximum total input sequence length after WordPiece tokenization. \n'
'Sequences longer than this will be truncated, sequences shorter padded.'
) , )
parser.add_argument('--batch_size' , default=1 , type=UpperCamelCase__ , help='Batch size.' )
parser.add_argument('--seed' , type=UpperCamelCase__ , default=42 )
parser.add_argument('--local_rank' , type=UpperCamelCase__ , default=-1 , help='local_rank for distributed training on gpus' )
parser.add_argument('--no_cuda' , action='store_true' , help='Whether not to use CUDA when available' )
parser.add_argument('--server_ip' , type=UpperCamelCase__ , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=UpperCamelCase__ , default='' , help='Can be used for distant debugging.' )
snake_case_ = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=UpperCamelCase__ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
snake_case_ = torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
snake_case_ = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
snake_case_ = torch.device('cuda' , args.local_rank )
snake_case_ = 1
torch.distributed.init_process_group(backend='nccl' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
snake_case_ = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
snake_case_ = nn.parallel.DistributedDataParallel(
UpperCamelCase__ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=UpperCamelCase__ )
elif args.n_gpu > 1:
snake_case_ = nn.DataParallel(UpperCamelCase__ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=UpperCamelCase__ )
torch.save(UpperCamelCase__ , os.path.join(args.output_dir , 'run_args.bin' ) )
logger.info('Training/evaluation parameters %s' , UpperCamelCase__ )
# Prepare dataset
snake_case_ = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
snake_case_ = (torch.from_numpy(UpperCamelCase__ ),)
snake_case_ = TensorDataset(*UpperCamelCase__ )
snake_case_ = RandomSampler(UpperCamelCase__ )
snake_case_ = DataLoader(UpperCamelCase__ , sampler=UpperCamelCase__ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
snake_case_ = mask_heads(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
prune_heads(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 69 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase :List[Any] = {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase :Any = [
'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'OPTForCausalLM',
'OPTModel',
'OPTPreTrainedModel',
'OPTForSequenceClassification',
'OPTForQuestionAnswering',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase :Optional[int] = ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase :Any = [
'FlaxOPTForCausalLM',
'FlaxOPTModel',
'FlaxOPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
_lowerCAmelCase :int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 263 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
__A = datasets.utils.logging.get_logger(__name__)
__A = ['''names''', '''prefix''']
__A = ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__A = ['''encoding_errors''', '''on_bad_lines''']
__A = ['''date_format''']
@dataclass
class lowercase ( datasets.BuilderConfig):
"""simple docstring"""
a__ : str = ","
a__ : Optional[str] = None
a__ : Optional[Union[int, List[int], str]] = "infer"
a__ : Optional[List[str]] = None
a__ : Optional[List[str]] = None
a__ : Optional[Union[int, str, List[int], List[str]]] = None
a__ : Optional[Union[List[int], List[str]]] = None
a__ : Optional[str] = None
a__ : bool = True
a__ : Optional[Literal["c", "python", "pyarrow"]] = None
a__ : Dict[Union[int, str], Callable[[Any], Any]] = None
a__ : Optional[list] = None
a__ : Optional[list] = None
a__ : bool = False
a__ : Optional[Union[int, List[int]]] = None
a__ : Optional[int] = None
a__ : Optional[Union[str, List[str]]] = None
a__ : bool = True
a__ : bool = True
a__ : bool = False
a__ : bool = True
a__ : Optional[str] = None
a__ : str = "."
a__ : Optional[str] = None
a__ : str = '"'
a__ : int = 0
a__ : Optional[str] = None
a__ : Optional[str] = None
a__ : Optional[str] = None
a__ : Optional[str] = None
a__ : bool = True
a__ : bool = True
a__ : int = 0
a__ : bool = True
a__ : bool = False
a__ : Optional[str] = None
a__ : int = 1_0000
a__ : Optional[datasets.Features] = None
a__ : Optional[str] = "strict"
a__ : Literal["error", "warn", "skip"] = "error"
a__ : Optional[str] = None
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
if self.delimiter is not None:
UpperCAmelCase_= self.delimiter
if self.column_names is not None:
UpperCAmelCase_= self.column_names
@property
def _SCREAMING_SNAKE_CASE ( self : str ) -> Tuple:
UpperCAmelCase_= {
"""sep""": self.sep,
"""header""": self.header,
"""names""": self.names,
"""index_col""": self.index_col,
"""usecols""": self.usecols,
"""prefix""": self.prefix,
"""mangle_dupe_cols""": self.mangle_dupe_cols,
"""engine""": self.engine,
"""converters""": self.converters,
"""true_values""": self.true_values,
"""false_values""": self.false_values,
"""skipinitialspace""": self.skipinitialspace,
"""skiprows""": self.skiprows,
"""nrows""": self.nrows,
"""na_values""": self.na_values,
"""keep_default_na""": self.keep_default_na,
"""na_filter""": self.na_filter,
"""verbose""": self.verbose,
"""skip_blank_lines""": self.skip_blank_lines,
"""thousands""": self.thousands,
"""decimal""": self.decimal,
"""lineterminator""": self.lineterminator,
"""quotechar""": self.quotechar,
"""quoting""": self.quoting,
"""escapechar""": self.escapechar,
"""comment""": self.comment,
"""encoding""": self.encoding,
"""dialect""": self.dialect,
"""error_bad_lines""": self.error_bad_lines,
"""warn_bad_lines""": self.warn_bad_lines,
"""skipfooter""": self.skipfooter,
"""doublequote""": self.doublequote,
"""memory_map""": self.memory_map,
"""float_precision""": self.float_precision,
"""chunksize""": self.chunksize,
"""encoding_errors""": self.encoding_errors,
"""on_bad_lines""": self.on_bad_lines,
"""date_format""": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , __UpperCAmelCase ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class lowercase ( datasets.ArrowBasedBuilder):
"""simple docstring"""
a__ : int = CsvConfig
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
return datasets.DatasetInfo(features=self.config.features )
def _SCREAMING_SNAKE_CASE ( self : Any , __UpperCAmelCase : Dict ) -> Optional[int]:
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
UpperCAmelCase_= dl_manager.download_and_extract(self.config.data_files )
if isinstance(__UpperCAmelCase , (str, list, tuple) ):
UpperCAmelCase_= data_files
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCAmelCase_= [files]
UpperCAmelCase_= [dl_manager.iter_files(__UpperCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
UpperCAmelCase_= []
for split_name, files in data_files.items():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCAmelCase_= [files]
UpperCAmelCase_= [dl_manager.iter_files(__UpperCAmelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=__UpperCAmelCase , gen_kwargs={"""files""": files} ) )
return splits
def _SCREAMING_SNAKE_CASE ( self : Tuple , __UpperCAmelCase : pa.Table ) -> pa.Table:
if self.config.features is not None:
UpperCAmelCase_= self.config.features.arrow_schema
if all(not require_storage_cast(__UpperCAmelCase ) for feature in self.config.features.values() ):
# cheaper cast
UpperCAmelCase_= pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=__UpperCAmelCase )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
UpperCAmelCase_= table_cast(__UpperCAmelCase , __UpperCAmelCase )
return pa_table
def _SCREAMING_SNAKE_CASE ( self : Any , __UpperCAmelCase : List[Any] ) -> List[str]:
UpperCAmelCase_= self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
UpperCAmelCase_= (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(__UpperCAmelCase ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase ) ):
UpperCAmelCase_= pd.read_csv(__UpperCAmelCase , iterator=__UpperCAmelCase , dtype=__UpperCAmelCase , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(__UpperCAmelCase ):
UpperCAmelCase_= pa.Table.from_pandas(__UpperCAmelCase )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(__UpperCAmelCase )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase )}: {e}""" )
raise
| 277 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def __a ( ) -> str:
'''simple docstring'''
UpperCAmelCase_= torch.nn.Linear(2 ,4 )
UpperCAmelCase_= torch.optim.AdamW(model.parameters() ,lr=1.0 )
UpperCAmelCase_= torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase_ ,max_lr=0.01 ,steps_per_epoch=2 ,epochs=1 )
UpperCAmelCase_= DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
UpperCAmelCase_= DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def __a ( lowerCAmelCase_ : Any ) -> Union[str, Any]:
'''simple docstring'''
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def __a ( lowerCAmelCase_ : Tuple ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_= torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(lowerCAmelCase_ )
class lowercase ( snake_case__):
"""simple docstring"""
@require_cuda
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
UpperCAmelCase_= Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(__UpperCAmelCase ):
UpperCAmelCase_= Accelerator(cpu=__UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_= GradientState()
assert state.num_steps == 1
UpperCAmelCase_= 4
assert state.num_steps == 4
assert state.sync_gradients is True
UpperCAmelCase_= False
assert state.sync_gradients is False
GradientState._reset_state()
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
(
(
UpperCAmelCase_
), (
UpperCAmelCase_
), (
UpperCAmelCase_
), (
UpperCAmelCase_
), (
UpperCAmelCase_
),
)= accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
self.assertTrue(prepared_model in accelerator._models )
self.assertTrue(prepared_optimizer in accelerator._optimizers )
self.assertTrue(prepared_scheduler in accelerator._schedulers )
self.assertTrue(prepared_train_dl in accelerator._dataloaders )
self.assertTrue(prepared_valid_dl in accelerator._dataloaders )
def _SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
accelerator.free_memory()
self.assertTrue(len(accelerator._models ) == 0 )
self.assertTrue(len(accelerator._optimizers ) == 0 )
self.assertTrue(len(accelerator._schedulers ) == 0 )
self.assertTrue(len(accelerator._dataloaders ) == 0 )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[Any]:
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*__UpperCAmelCase : Dict , **__UpperCAmelCase : Tuple ):
pass
with patch("""torch.cuda.set_device""" , __UpperCAmelCase ), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64""" ):
UpperCAmelCase_= Accelerator()
self.assertEqual(str(accelerator.state.device ) , """cuda:64""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase_= get_signature(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase )
# make sure random weights don't match
load_random_weights(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) > 1E-3 )
# make sure loaded weights match
accelerator.load_state(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) < 1E-3 )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase_= get_signature(__UpperCAmelCase )
# saving hook
def save_config(__UpperCAmelCase : Tuple , __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple ):
UpperCAmelCase_= {"""class_name""": models[0].__class__.__name__}
with open(os.path.join(__UpperCAmelCase , """data.json""" ) , """w""" ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
# loading hook
def load_config(__UpperCAmelCase : Tuple , __UpperCAmelCase : Union[str, Any] ):
with open(os.path.join(__UpperCAmelCase , """data.json""" ) , """r""" ) as f:
UpperCAmelCase_= json.load(__UpperCAmelCase )
UpperCAmelCase_= config["""class_name"""]
UpperCAmelCase_= accelerator.register_save_state_pre_hook(__UpperCAmelCase )
UpperCAmelCase_= accelerator.register_load_state_pre_hook(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase )
# make sure random weights don't match with hooks
load_random_weights(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) > 1E-3 )
# random class name to verify correct one is loaded
UpperCAmelCase_= """random"""
# make sure loaded weights match with hooks
accelerator.load_state(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) < 1E-3 )
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__ )
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase )
# make sure random weights don't match with hooks removed
load_random_weights(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) > 1E-3 )
# random class name to verify correct one is loaded
UpperCAmelCase_= """random"""
# make sure loaded weights match with hooks removed
accelerator.load_state(__UpperCAmelCase )
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase ) ) < 1E-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
UpperCAmelCase_= None
# This should work
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= accelerator.prepare(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
self.assertTrue(dummy_obj is None )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
UpperCAmelCase_= Accelerator()
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= create_components()
UpperCAmelCase_= [1, 2, 3]
# This should work
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= accelerator.prepare(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Dummy object should have `_is_accelerate_prepared` set to `True`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Model is missing `_is_accelerator_prepared` or is set to `False`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Optimizer is missing `_is_accelerator_prepared` or is set to `False`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Scheduler is missing `_is_accelerator_prepared` or is set to `False`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`""" , )
self.assertEqual(
getattr(__UpperCAmelCase , """_is_accelerate_prepared""" , __UpperCAmelCase ) , __UpperCAmelCase , """Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`""" , )
@slow
@require_bnb
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
from transformers import AutoModelForCausalLM
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , load_in_abit=__UpperCAmelCase , device_map={"""""": 0} , )
UpperCAmelCase_= Accelerator()
# This should work
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
@slow
@require_bnb
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple:
from transformers import AutoModelForCausalLM
UpperCAmelCase_= Accelerator()
with init_empty_weights():
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , )
model.tie_weights()
UpperCAmelCase_= infer_auto_device_map(__UpperCAmelCase )
UpperCAmelCase_= """cpu"""
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , device_map=__UpperCAmelCase , load_in_abit=__UpperCAmelCase , llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase )
# This should not work and get value error
with self.assertRaises(__UpperCAmelCase ):
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
@slow
@require_bnb
@require_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : int ) -> Tuple:
from transformers import AutoModelForCausalLM
UpperCAmelCase_= {"""distributed_type""": DistributedType.MULTI_GPU}
with init_empty_weights():
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , )
model.tie_weights()
UpperCAmelCase_= infer_auto_device_map(__UpperCAmelCase )
UpperCAmelCase_= 1
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , load_in_abit=__UpperCAmelCase , device_map=__UpperCAmelCase , )
UpperCAmelCase_= Accelerator()
# This should not work and get value error
with self.assertRaises(__UpperCAmelCase ):
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Dict:
from transformers import AutoModelForCausalLM
with init_empty_weights():
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , )
UpperCAmelCase_= infer_auto_device_map(__UpperCAmelCase )
UpperCAmelCase_= 1
UpperCAmelCase_= AutoModelForCausalLM.from_pretrained(
"""EleutherAI/gpt-neo-125m""" , load_in_abit=__UpperCAmelCase , device_map=__UpperCAmelCase , )
UpperCAmelCase_= Accelerator()
# This should work
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
@require_cuda
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[int]:
UpperCAmelCase_= torch.nn.Linear(10 , 10 )
UpperCAmelCase_= torch.optim.SGD(model.parameters() , lr=0.01 )
UpperCAmelCase_= Accelerator(cpu=__UpperCAmelCase )
UpperCAmelCase_= accelerator.prepare(__UpperCAmelCase )
| 277 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Any = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-encoder': (
'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-scorer': (
'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-openqa': (
'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
),
'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
# See all REALM models at https://huggingface.co/models?filter=realm
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''realm'''
def __init__(self : Any , _UpperCAmelCase : Tuple=3_0522 , _UpperCAmelCase : Optional[Any]=768 , _UpperCAmelCase : Optional[Any]=128 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : Any=12 , _UpperCAmelCase : List[Any]=8 , _UpperCAmelCase : List[str]=3072 , _UpperCAmelCase : Any="gelu_new" , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : Optional[Any]=512 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Optional[Any]=0.02 , _UpperCAmelCase : Dict=1E-1_2 , _UpperCAmelCase : List[Any]=256 , _UpperCAmelCase : Optional[int]=10 , _UpperCAmelCase : Union[str, Any]=1E-3 , _UpperCAmelCase : int=5 , _UpperCAmelCase : str=320 , _UpperCAmelCase : str=1335_3718 , _UpperCAmelCase : List[Any]=5000 , _UpperCAmelCase : Optional[Any]=1 , _UpperCAmelCase : str=0 , _UpperCAmelCase : Tuple=2 , **_UpperCAmelCase : List[Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
# Common config
lowercase__ = vocab_size
lowercase__ = max_position_embeddings
lowercase__ = hidden_size
lowercase__ = retriever_proj_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = num_candidates
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = initializer_range
lowercase__ = type_vocab_size
lowercase__ = layer_norm_eps
# Reader config
lowercase__ = span_hidden_size
lowercase__ = max_span_width
lowercase__ = reader_layer_norm_eps
lowercase__ = reader_beam_size
lowercase__ = reader_seq_len
# Retrieval config
lowercase__ = num_block_records
lowercase__ = searcher_beam_size
| 305 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def UpperCamelCase ( __magic_name__ : int ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowercase__ = set()
lowercase__ = 42
lowercase__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def UpperCamelCase ( __magic_name__ : int = 5000 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1 , __magic_name__ ):
if len(partition(__magic_name__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'{solution() = }')
| 305 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class lowerCAmelCase__ ( A_ ):
__a = 42
__a = 42
__a = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
| 40 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 40 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCamelCase ( unittest.TestCase ):
def __init__(self , __a , __a=7 , __a=3 , __a=18 , __a=30 , __a=4_00 , __a=True , __a=None , __a=True , ) -> Union[str, Any]:
UpperCamelCase = size if size is not None else {"height": 18, "width": 18}
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = image_size
UpperCamelCase = min_resolution
UpperCamelCase = max_resolution
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = apply_ocr
def snake_case_ (self ) -> Any:
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _lowerCamelCase ( _lowercase , unittest.TestCase ):
UpperCAmelCase_ = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def snake_case_ (self ) -> Dict:
UpperCamelCase = LayoutLMvaImageProcessingTester(self )
@property
def snake_case_ (self ) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ (self ) -> Optional[Any]:
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , "do_resize" ) )
self.assertTrue(hasattr(__a , "size" ) )
self.assertTrue(hasattr(__a , "apply_ocr" ) )
def snake_case_ (self ) -> Optional[Any]:
UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 18, "width": 18} )
UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
def snake_case_ (self ) -> Optional[Any]:
pass
def snake_case_ (self ) -> Dict:
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors="pt" )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
self.assertIsInstance(encoding.words , __a )
self.assertIsInstance(encoding.boxes , __a )
# Test batched
UpperCamelCase = image_processing(__a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def snake_case_ (self ) -> Tuple:
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
UpperCamelCase = image_processing(__a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def snake_case_ (self ) -> Tuple:
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
UpperCamelCase = image_processing(__a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def snake_case_ (self ) -> List[Any]:
# with apply_OCR = True
UpperCamelCase = LayoutLMvaImageProcessor()
from datasets import load_dataset
UpperCamelCase = load_dataset("hf-internal-testing/fixtures_docvqa" , split="test" )
UpperCamelCase = Image.open(ds[0]["file"] ).convert("RGB" )
UpperCamelCase = image_processing(__a , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
UpperCamelCase = [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]] # noqa: E231
UpperCamelCase = [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __a )
self.assertListEqual(encoding.boxes , __a )
# with apply_OCR = False
UpperCamelCase = LayoutLMvaImageProcessor(apply_ocr=__a )
UpperCamelCase = image_processing(__a , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
| 153 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
def snake_case_ (self ) -> Tuple:
UpperCamelCase = torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
UpperCamelCase = get_activation("gelu" )
self.assertTrue(torch.allclose(gelu_python(__a ) , torch_builtin(__a ) ) )
self.assertFalse(torch.allclose(gelu_python(__a ) , gelu_new(__a ) ) )
def snake_case_ (self ) -> Union[str, Any]:
UpperCamelCase = torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
UpperCamelCase = get_activation("gelu" )
UpperCamelCase = get_activation("gelu_10" )
UpperCamelCase = torch_builtin(__a )
UpperCamelCase = geluaa(__a )
UpperCamelCase = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(__a ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def snake_case_ (self ) -> Any:
get_activation("gelu" )
get_activation("gelu_10" )
get_activation("gelu_fast" )
get_activation("gelu_new" )
get_activation("gelu_python" )
get_activation("gelu_pytorch_tanh" )
get_activation("linear" )
get_activation("mish" )
get_activation("quick_gelu" )
get_activation("relu" )
get_activation("sigmoid" )
get_activation("silu" )
get_activation("swish" )
get_activation("tanh" )
with self.assertRaises(__a ):
get_activation("bogus" )
with self.assertRaises(__a ):
get_activation(__a )
def snake_case_ (self ) -> Optional[Any]:
UpperCamelCase = get_activation("gelu" )
UpperCamelCase = 1
UpperCamelCase = get_activation("gelu" )
self.assertEqual(acta.a , 1 )
with self.assertRaises(__a ):
UpperCamelCase = acta.a
| 153 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase ) -> int:
A , A: Any = len(__lowercase ), len(grid[0] )
if (
min(__lowercase , __lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
A: List[Any] = 0
count += depth_first_search(__lowercase , row + 1 , __lowercase , __lowercase )
count += depth_first_search(__lowercase , row - 1 , __lowercase , __lowercase )
count += depth_first_search(__lowercase , __lowercase , col + 1 , __lowercase )
count += depth_first_search(__lowercase , __lowercase , col - 1 , __lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 334 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
UpperCamelCase = None
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
UpperCamelCase = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
},
}
UpperCamelCase = {
'''camembert-base''': 512,
}
UpperCamelCase = '''▁'''
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES
UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : str = ["""input_ids""", """attention_mask"""]
UpperCamelCase_ : int = CamembertTokenizer
def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : int=None , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : str="<s>" , SCREAMING_SNAKE_CASE_ : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE_ : List[Any]="</s>" , SCREAMING_SNAKE_CASE_ : int="<s>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="<unk>" , SCREAMING_SNAKE_CASE_ : str="<pad>" , SCREAMING_SNAKE_CASE_ : List[str]="<mask>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , **SCREAMING_SNAKE_CASE_ : Any , ) -> Any:
'''simple docstring'''
A: Tuple = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
A: Any = vocab_file
A: Any = False if not self.vocab_file else True
def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A: List[str] = [self.cls_token_id]
A: List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A: List[str] = [self.sep_token_id]
A: Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
A: Dict = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
| 334 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Dict = {}
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = """llama"""
_lowerCAmelCase = ["""past_key_values"""]
def __init__( self , __magic_name__=3_20_00 , __magic_name__=40_96 , __magic_name__=1_10_08 , __magic_name__=32 , __magic_name__=32 , __magic_name__=None , __magic_name__="silu" , __magic_name__=20_48 , __magic_name__=0.0_2 , __magic_name__=1e-6 , __magic_name__=True , __magic_name__=0 , __magic_name__=1 , __magic_name__=2 , __magic_name__=1 , __magic_name__=False , __magic_name__=None , **__magic_name__ , ) -> str:
_a = vocab_size
_a = max_position_embeddings
_a = hidden_size
_a = intermediate_size
_a = num_hidden_layers
_a = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
_a = num_attention_heads
_a = num_key_value_heads
_a = hidden_act
_a = initializer_range
_a = rms_norm_eps
_a = pretraining_tp
_a = use_cache
_a = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , tie_word_embeddings=__magic_name__ , **__magic_name__ , )
def __UpperCAmelCase ( self ) -> List[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __magic_name__ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
f'got {self.rope_scaling}' )
_a = self.rope_scaling.get('type' , __magic_name__ )
_a = self.rope_scaling.get('factor' , __magic_name__ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(__magic_name__ , __magic_name__ ) or rope_scaling_factor <= 1.0:
raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 168 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
a_ : str = logging.get_logger(__name__)
a_ : Tuple = {
"microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = """layoutlmv3"""
def __init__( self , __magic_name__=5_02_65 , __magic_name__=7_68 , __magic_name__=12 , __magic_name__=12 , __magic_name__=30_72 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=5_12 , __magic_name__=2 , __magic_name__=0.0_2 , __magic_name__=1e-5 , __magic_name__=1 , __magic_name__=0 , __magic_name__=2 , __magic_name__=10_24 , __magic_name__=1_28 , __magic_name__=1_28 , __magic_name__=True , __magic_name__=32 , __magic_name__=1_28 , __magic_name__=64 , __magic_name__=2_56 , __magic_name__=True , __magic_name__=True , __magic_name__=True , __magic_name__=2_24 , __magic_name__=3 , __magic_name__=16 , __magic_name__=None , **__magic_name__ , ) -> Dict:
super().__init__(
vocab_size=__magic_name__ , hidden_size=__magic_name__ , num_hidden_layers=__magic_name__ , num_attention_heads=__magic_name__ , intermediate_size=__magic_name__ , hidden_act=__magic_name__ , hidden_dropout_prob=__magic_name__ , attention_probs_dropout_prob=__magic_name__ , max_position_embeddings=__magic_name__ , type_vocab_size=__magic_name__ , initializer_range=__magic_name__ , layer_norm_eps=__magic_name__ , pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ , )
_a = max_ad_position_embeddings
_a = coordinate_size
_a = shape_size
_a = has_relative_attention_bias
_a = rel_pos_bins
_a = max_rel_pos
_a = has_spatial_attention_bias
_a = rel_ad_pos_bins
_a = max_rel_ad_pos
_a = text_embed
_a = visual_embed
_a = input_size
_a = num_channels
_a = patch_size
_a = classifier_dropout
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = version.parse("""1.12""" )
@property
def __UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
# The order of inputs is different for question answering and sequence classification
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
('bbox', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
else:
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('bbox', {0: 'batch', 1: 'sequence'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels'}),
] )
@property
def __UpperCAmelCase ( self ) -> float:
return 1e-5
@property
def __UpperCAmelCase ( self ) -> int:
return 12
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = False , __magic_name__ = None , __magic_name__ = 3 , __magic_name__ = 40 , __magic_name__ = 40 , ) -> Mapping[str, Any]:
setattr(processor.image_processor , 'apply_ocr' , __magic_name__ )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_a = compute_effective_axis_dimension(
__magic_name__ , 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
_a = processor.tokenizer.num_special_tokens_to_add(__magic_name__ )
_a = compute_effective_axis_dimension(
__magic_name__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__magic_name__ )
# Generate dummy inputs according to compute batch and sequence
_a = [[' '.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
_a = [[[48, 84, 73, 1_28]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
_a = self._generate_dummy_images(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
_a = dict(
processor(
__magic_name__ , text=__magic_name__ , boxes=__magic_name__ , return_tensors=__magic_name__ , ) )
return inputs
| 168 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''LlamaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''LlamaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''LlamaForCausalLM''',
'''LlamaModel''',
'''LlamaPreTrainedModel''',
'''LlamaForSequenceClassification''',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 174 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
lowerCamelCase_ = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
lowerCamelCase_ = {
'''allenai/led-base-16384''': 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def __lowercase ( ) -> Dict:
'''simple docstring'''
_A = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
_A = bs[:]
_A = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__lowercase )
cs.append(2**8 + n )
n += 1
_A = [chr(__lowercase ) for n in cs]
return dict(zip(__lowercase , __lowercase ) )
def __lowercase ( __lowercase ) -> Dict:
'''simple docstring'''
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A = char
return pairs
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
snake_case = VOCAB_FILES_NAMES
snake_case = PRETRAINED_VOCAB_FILES_MAP
snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case = ['''input_ids''', '''attention_mask''']
def __init__( self : Dict , __UpperCAmelCase : Any , __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple="replace" , __UpperCAmelCase : Optional[int]="<s>" , __UpperCAmelCase : str="</s>" , __UpperCAmelCase : List[Any]="</s>" , __UpperCAmelCase : Any="<s>" , __UpperCAmelCase : int="<unk>" , __UpperCAmelCase : Optional[int]="<pad>" , __UpperCAmelCase : Optional[Any]="<mask>" , __UpperCAmelCase : Any=False , **__UpperCAmelCase : Any , ):
'''simple docstring'''
_A = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else bos_token
_A = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else eos_token
_A = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else sep_token
_A = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else cls_token
_A = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else unk_token
_A = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
super().__init__(
errors=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , **__UpperCAmelCase , )
with open(__UpperCAmelCase , encoding="utf-8" ) as vocab_handle:
_A = json.load(__UpperCAmelCase )
_A = {v: k for k, v in self.encoder.items()}
_A = errors # how to handle errors in decoding
_A = bytes_to_unicode()
_A = {v: k for k, v in self.byte_encoder.items()}
with open(__UpperCAmelCase , encoding="utf-8" ) as merges_handle:
_A = merges_handle.read().split("\n" )[1:-1]
_A = [tuple(merge.split() ) for merge in bpe_merges]
_A = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
_A = {}
_A = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_A = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def lowerCAmelCase ( self : str ):
'''simple docstring'''
return len(self.encoder )
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Optional[int] ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
_A = tuple(__UpperCAmelCase )
_A = get_pairs(__UpperCAmelCase )
if not pairs:
return token
while True:
_A = min(__UpperCAmelCase , key=lambda __UpperCAmelCase : self.bpe_ranks.get(__UpperCAmelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
_A , _A = bigram
_A = []
_A = 0
while i < len(__UpperCAmelCase ):
try:
_A = word.index(__UpperCAmelCase , __UpperCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_A = j
if word[i] == first and i < len(__UpperCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_A = tuple(__UpperCAmelCase )
_A = new_word
if len(__UpperCAmelCase ) == 1:
break
else:
_A = get_pairs(__UpperCAmelCase )
_A = " ".join(__UpperCAmelCase )
_A = word
return word
def lowerCAmelCase ( self : Any , __UpperCAmelCase : str ):
'''simple docstring'''
_A = []
for token in re.findall(self.pat , __UpperCAmelCase ):
_A = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCAmelCase ).split(" " ) )
return bpe_tokens
def lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Tuple ):
'''simple docstring'''
return self.encoder.get(__UpperCAmelCase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : Optional[int] ):
'''simple docstring'''
return self.decoder.get(__UpperCAmelCase )
def lowerCAmelCase ( self : str , __UpperCAmelCase : Dict ):
'''simple docstring'''
_A = "".join(__UpperCAmelCase )
_A = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(__UpperCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
_A = os.path.join(
__UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__UpperCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__UpperCAmelCase , ensure_ascii=__UpperCAmelCase ) + "\n" )
_A = 0
with open(__UpperCAmelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __UpperCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
" Please check that the tokenizer is not corrupted!" )
_A = token_index
writer.write(" ".join(__UpperCAmelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase ( self : int , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__UpperCAmelCase )) + [1]
return [1] + ([0] * len(__UpperCAmelCase )) + [1, 1] + ([0] * len(__UpperCAmelCase )) + [1]
def lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ):
'''simple docstring'''
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Dict=False , **__UpperCAmelCase : Any ):
'''simple docstring'''
_A = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__UpperCAmelCase ) > 0 and not text[0].isspace()):
_A = " " + text
return (text, kwargs)
def lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : Union[Dict[str, EncodedInput], BatchEncoding] , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : Optional[bool] = None , ):
'''simple docstring'''
_A = super()._pad(
encoded_inputs=__UpperCAmelCase , max_length=__UpperCAmelCase , padding_strategy=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , )
# Load from model defaults
if return_attention_mask is None:
_A = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_A = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_A = len(encoded_inputs["global_attention_mask"] ) != len(__UpperCAmelCase )
if needs_to_be_padded:
_A = len(__UpperCAmelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_A = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
_A = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 174 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Any , A : str , A : Dict=7 , A : Dict=3 , A : List[str]=18 , A : int=30 , A : Any=4_00 , A : Union[str, Any]=True , A : List[str]=None , A : Dict=True , A : str=None , A : Dict=True , A : Optional[Any]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , A : Optional[Any]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , A : str=True , ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = size if size is not None else {'height': 2_24, 'width': 2_24}
_UpperCAmelCase = crop_size if crop_size is not None else {'height': 18, 'width': 18}
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = min_resolution
_UpperCAmelCase = max_resolution
_UpperCAmelCase = do_resize
_UpperCAmelCase = size
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean
_UpperCAmelCase = image_std
_UpperCAmelCase = do_convert_rgb
def _lowerCamelCase ( self : Optional[int]) -> List[Any]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def _lowerCamelCase ( self : List[str] , A : List[Any]=False , A : Union[str, Any]=False , A : List[Any]=False) -> str:
"""simple docstring"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
_UpperCAmelCase = []
for i in range(self.batch_size):
image_inputs.append(
np.random.randint(
2_55 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta))
else:
_UpperCAmelCase = []
for i in range(self.batch_size):
_UpperCAmelCase , _UpperCAmelCase = np.random.choice(np.arange(self.min_resolution , self.max_resolution) , 2)
image_inputs.append(np.random.randint(2_55 , size=(self.num_channels, width, height) , dtype=np.uinta))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
_UpperCAmelCase = [Image.fromarray(np.moveaxis(A , 0 , -1)) for x in image_inputs]
if torchify:
_UpperCAmelCase = [torch.from_numpy(A) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = ChineseCLIPImageProcessor if is_vision_available() else None
def _lowerCamelCase ( self : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = ChineseCLIPImageProcessingTester(self , do_center_crop=A)
@property
def _lowerCamelCase ( self : List[str]) -> Any:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCamelCase ( self : Any) -> str:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(A , 'do_resize'))
self.assertTrue(hasattr(A , 'size'))
self.assertTrue(hasattr(A , 'do_center_crop'))
self.assertTrue(hasattr(A , 'center_crop'))
self.assertTrue(hasattr(A , 'do_normalize'))
self.assertTrue(hasattr(A , 'image_mean'))
self.assertTrue(hasattr(A , 'image_std'))
self.assertTrue(hasattr(A , 'do_convert_rgb'))
def _lowerCamelCase ( self : str) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {'height': 2_24, 'width': 2_24})
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18})
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84)
self.assertEqual(image_processor.size , {'shortest_edge': 42})
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84})
def _lowerCamelCase ( self : List[Any]) -> Any:
"""simple docstring"""
pass
def _lowerCamelCase ( self : Tuple) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
_UpperCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=A)
for image in image_inputs:
self.assertIsInstance(A , Image.Image)
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(A , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
_UpperCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=A , numpify=A)
for image in image_inputs:
self.assertIsInstance(A , np.ndarray)
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(A , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
_UpperCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=A , torchify=A)
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor)
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(A , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
@require_torch
@require_vision
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = ChineseCLIPImageProcessor if is_vision_available() else None
def _lowerCamelCase ( self : List[str]) -> str:
"""simple docstring"""
_UpperCAmelCase = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=A)
_UpperCAmelCase = 3
@property
def _lowerCamelCase ( self : Optional[Any]) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCamelCase ( self : Optional[int]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(A , 'do_resize'))
self.assertTrue(hasattr(A , 'size'))
self.assertTrue(hasattr(A , 'do_center_crop'))
self.assertTrue(hasattr(A , 'center_crop'))
self.assertTrue(hasattr(A , 'do_normalize'))
self.assertTrue(hasattr(A , 'image_mean'))
self.assertTrue(hasattr(A , 'image_std'))
self.assertTrue(hasattr(A , 'do_convert_rgb'))
def _lowerCamelCase ( self : Any) -> Union[str, Any]:
"""simple docstring"""
pass
def _lowerCamelCase ( self : Any) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
_UpperCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=A)
for image in image_inputs:
self.assertIsInstance(A , Image.Image)
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(A , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 339 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 1 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
a__ : Optional[int] = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]
@register_to_config
def __init__( self : Tuple , _lowercase : int , _lowercase : int , _lowercase : Optional[int] = None , _lowercase : int = 5_02_57 , _lowercase : int = 10_24 , _lowercase : int = 7_68 , _lowercase : int = 12 , _lowercase : int = 12 , _lowercase : Optional[int] = None , _lowercase : str = "gelu_new" , _lowercase : float = 0.1 , _lowercase : float = 0.1 , _lowercase : float = 0.1 , _lowercase : float = 1E-5 , _lowercase : float = 0.02 , _lowercase : bool = True , _lowercase : bool = True , _lowercase : bool = False , _lowercase : bool = False , ):
super().__init__()
__UpperCAmelCase = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
F''' `n_embd`: {n_embd} are not equal.''' )
__UpperCAmelCase = prefix_inner_dim
__UpperCAmelCase = prefix_hidden_dim
__UpperCAmelCase = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
__UpperCAmelCase = (
nn.Linear(self.prefix_hidden_dim , _lowercase ) if self.prefix_hidden_dim is not None else nn.Identity()
)
__UpperCAmelCase = GPTaConfig(
vocab_size=_lowercase , n_positions=_lowercase , n_embd=_lowercase , n_layer=_lowercase , n_head=_lowercase , n_inner=_lowercase , activation_function=_lowercase , resid_pdrop=_lowercase , embd_pdrop=_lowercase , attn_pdrop=_lowercase , layer_norm_epsilon=_lowercase , initializer_range=_lowercase , scale_attn_weights=_lowercase , use_cache=_lowercase , scale_attn_by_inverse_layer_idx=_lowercase , reorder_and_upcast_attn=_lowercase , )
__UpperCAmelCase = GPTaLMHeadModel(_lowercase )
def a ( self : List[str] , _lowercase : torch.Tensor , _lowercase : torch.Tensor , _lowercase : Optional[torch.Tensor] = None , _lowercase : Optional[torch.Tensor] = None , ):
__UpperCAmelCase = self.transformer.transformer.wte(_lowercase )
__UpperCAmelCase = self.encode_prefix(_lowercase )
__UpperCAmelCase = self.decode_prefix(_lowercase )
__UpperCAmelCase = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
__UpperCAmelCase = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
__UpperCAmelCase = torch.cat((dummy_token, input_ids) , dim=1 )
__UpperCAmelCase = self.transformer(inputs_embeds=_lowercase , labels=_lowercase , attention_mask=_lowercase )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def a ( self : List[Any] , _lowercase : int , _lowercase : torch.device ):
return torch.zeros(_lowercase , self.prefix_length , dtype=torch.intaa , device=_lowercase )
def a ( self : str , _lowercase : List[str] ):
return self.encode_prefix(_lowercase )
@torch.no_grad()
def a ( self : Optional[Any] , _lowercase : Any , _lowercase : Union[str, Any] , _lowercase : Optional[int] ):
__UpperCAmelCase = torch.split(_lowercase , 1 , dim=0 )
__UpperCAmelCase = []
__UpperCAmelCase = []
for feature in features:
__UpperCAmelCase = self.decode_prefix(feature.to(_lowercase ) ) # back to the clip feature
# Only support beam search for now
__UpperCAmelCase , __UpperCAmelCase = self.generate_beam(
input_embeds=_lowercase , device=_lowercase , eos_token_id=_lowercase )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
__UpperCAmelCase = torch.stack(_lowercase )
__UpperCAmelCase = torch.stack(_lowercase )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def a ( self : str , _lowercase : Any=None , _lowercase : Dict=None , _lowercase : Tuple=None , _lowercase : int = 5 , _lowercase : int = 67 , _lowercase : float = 1.0 , _lowercase : Optional[int] = None , ):
__UpperCAmelCase = eos_token_id
__UpperCAmelCase = None
__UpperCAmelCase = None
__UpperCAmelCase = torch.ones(_lowercase , device=_lowercase , dtype=torch.int )
__UpperCAmelCase = torch.zeros(_lowercase , device=_lowercase , dtype=torch.bool )
if input_embeds is not None:
__UpperCAmelCase = input_embeds
else:
__UpperCAmelCase = self.transformer.transformer.wte(_lowercase )
for i in range(_lowercase ):
__UpperCAmelCase = self.transformer(inputs_embeds=_lowercase )
__UpperCAmelCase = outputs.logits
__UpperCAmelCase = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
__UpperCAmelCase = logits.softmax(-1 ).log()
if scores is None:
__UpperCAmelCase , __UpperCAmelCase = logits.topk(_lowercase , -1 )
__UpperCAmelCase = generated.expand(_lowercase , *generated.shape[1:] )
__UpperCAmelCase , __UpperCAmelCase = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
__UpperCAmelCase = next_tokens
else:
__UpperCAmelCase = tokens.expand(_lowercase , *tokens.shape[1:] )
__UpperCAmelCase = torch.cat((tokens, next_tokens) , dim=1 )
else:
__UpperCAmelCase = -float(np.inf )
__UpperCAmelCase = 0
__UpperCAmelCase = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
__UpperCAmelCase = scores_sum / seq_lengths[:, None]
__UpperCAmelCase , __UpperCAmelCase = scores_sum_average.view(-1 ).topk(_lowercase , -1 )
__UpperCAmelCase = next_tokens // scores_sum.shape[1]
__UpperCAmelCase = seq_lengths[next_tokens_source]
__UpperCAmelCase = next_tokens % scores_sum.shape[1]
__UpperCAmelCase = next_tokens.unsqueeze(1 )
__UpperCAmelCase = tokens[next_tokens_source]
__UpperCAmelCase = torch.cat((tokens, next_tokens) , dim=1 )
__UpperCAmelCase = generated[next_tokens_source]
__UpperCAmelCase = scores_sum_average * seq_lengths
__UpperCAmelCase = is_stopped[next_tokens_source]
__UpperCAmelCase = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
__UpperCAmelCase = torch.cat((generated, next_token_embed) , dim=1 )
__UpperCAmelCase = is_stopped + next_tokens.eq(_lowercase ).squeeze()
if is_stopped.all():
break
__UpperCAmelCase = scores / seq_lengths
__UpperCAmelCase = scores.argsort(descending=_lowercase )
# tokens tensors are already padded to max_seq_length
__UpperCAmelCase = [tokens[i] for i in order]
__UpperCAmelCase = torch.stack(_lowercase , dim=0 )
__UpperCAmelCase = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 370 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ( enum.Enum ):
a__ : str = 0
a__ : List[Any] = 1
a__ : str = 2
@add_end_docstrings(_lowerCAmelCase )
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : Dict = "\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n "
def __init__( self : Optional[Any] , *_lowercase : Any , **_lowercase : Optional[int] ):
super().__init__(*_lowercase , **_lowercase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
__UpperCAmelCase = None
if self.model.config.prefix is not None:
__UpperCAmelCase = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
__UpperCAmelCase = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = self._sanitize_parameters(prefix=_lowercase , **self._forward_params )
__UpperCAmelCase = {**self._preprocess_params, **preprocess_params}
__UpperCAmelCase = {**self._forward_params, **forward_params}
def a ( self : Any , _lowercase : Optional[Any]=None , _lowercase : List[str]=None , _lowercase : int=None , _lowercase : Union[str, Any]=None , _lowercase : Union[str, Any]=None , _lowercase : Union[str, Any]=None , _lowercase : Union[str, Any]=None , _lowercase : List[Any]=None , **_lowercase : str , ):
__UpperCAmelCase = {}
if prefix is not None:
__UpperCAmelCase = prefix
if prefix:
__UpperCAmelCase = self.tokenizer(
_lowercase , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
__UpperCAmelCase = prefix_inputs['''input_ids'''].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
F'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
''' [None, \'hole\']''' )
__UpperCAmelCase = handle_long_generation
preprocess_params.update(_lowercase )
__UpperCAmelCase = generate_kwargs
__UpperCAmelCase = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_full_text`''' )
if return_tensors is not None:
raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`''' )
__UpperCAmelCase = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_tensors`''' )
__UpperCAmelCase = ReturnType.TENSORS
if return_type is not None:
__UpperCAmelCase = return_type
if clean_up_tokenization_spaces is not None:
__UpperCAmelCase = clean_up_tokenization_spaces
if stop_sequence is not None:
__UpperCAmelCase = self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
if len(_lowercase ) > 1:
warnings.warn(
'''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
''' the stop sequence will be used as the stop sequence string in the interim.''' )
__UpperCAmelCase = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def a ( self : Optional[int] , *_lowercase : Optional[int] , **_lowercase : Any ):
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({'''add_space_before_punct_symbol''': True} )
return super()._parse_and_tokenize(*_lowercase , **_lowercase )
def __call__( self : List[str] , _lowercase : str , **_lowercase : Optional[Any] ):
return super().__call__(_lowercase , **_lowercase )
def a ( self : Union[str, Any] , _lowercase : Any , _lowercase : Dict="" , _lowercase : Union[str, Any]=None , **_lowercase : Tuple ):
__UpperCAmelCase = self.tokenizer(
prefix + prompt_text , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
__UpperCAmelCase = prompt_text
if handle_long_generation == "hole":
__UpperCAmelCase = inputs['''input_ids'''].shape[-1]
if "max_new_tokens" in generate_kwargs:
__UpperCAmelCase = generate_kwargs['''max_new_tokens''']
else:
__UpperCAmelCase = generate_kwargs.get('''max_length''' , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError('''We cannot infer how many new tokens are expected''' )
if cur_len + new_tokens > self.tokenizer.model_max_length:
__UpperCAmelCase = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
'''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
''' models max length''' )
__UpperCAmelCase = inputs['''input_ids'''][:, -keep_length:]
if "attention_mask" in inputs:
__UpperCAmelCase = inputs['''attention_mask'''][:, -keep_length:]
return inputs
def a ( self : Union[str, Any] , _lowercase : List[str] , **_lowercase : Optional[int] ):
__UpperCAmelCase = model_inputs['''input_ids''']
__UpperCAmelCase = model_inputs.get('''attention_mask''' , _lowercase )
# Allow empty prompts
if input_ids.shape[1] == 0:
__UpperCAmelCase = None
__UpperCAmelCase = None
__UpperCAmelCase = 1
else:
__UpperCAmelCase = input_ids.shape[0]
__UpperCAmelCase = model_inputs.pop('''prompt_text''' )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
__UpperCAmelCase = generate_kwargs.pop('''prefix_length''' , 0 )
if prefix_length > 0:
__UpperCAmelCase = '''max_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].max_new_tokens is not None
)
if not has_max_new_tokens:
__UpperCAmelCase = generate_kwargs.get('''max_length''' ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
__UpperCAmelCase = '''min_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
__UpperCAmelCase = self.model.generate(input_ids=_lowercase , attention_mask=_lowercase , **_lowercase )
__UpperCAmelCase = generated_sequence.shape[0]
if self.framework == "pt":
__UpperCAmelCase = generated_sequence.reshape(_lowercase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
__UpperCAmelCase = tf.reshape(_lowercase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def a ( self : Optional[int] , _lowercase : Union[str, Any] , _lowercase : Optional[int]=ReturnType.FULL_TEXT , _lowercase : List[str]=True ):
__UpperCAmelCase = model_outputs['''generated_sequence'''][0]
__UpperCAmelCase = model_outputs['''input_ids''']
__UpperCAmelCase = model_outputs['''prompt_text''']
__UpperCAmelCase = generated_sequence.numpy().tolist()
__UpperCAmelCase = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
__UpperCAmelCase = {'''generated_token_ids''': sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
__UpperCAmelCase = self.tokenizer.decode(
_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
__UpperCAmelCase = 0
else:
__UpperCAmelCase = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , ) )
if return_type == ReturnType.FULL_TEXT:
__UpperCAmelCase = prompt_text + text[prompt_length:]
else:
__UpperCAmelCase = text[prompt_length:]
__UpperCAmelCase = {'''generated_text''': all_text}
records.append(_lowercase )
return records
| 86 | 0 |
def lowerCAmelCase_ ( A_):
if not isinstance(A_ ,A_):
UpperCamelCase__: Dict = F"Input value of [number={number}] must be an integer"
raise TypeError(A_)
if number < 0:
return False
UpperCamelCase__: Any = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 149 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _a ( UpperCamelCase__):
"""simple docstring"""
def UpperCAmelCase_ ( self: Dict ):
'''simple docstring'''
UpperCamelCase__: str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowerCamelCase , "embed_dim" ) )
self.parent.assertTrue(hasattr(__lowerCamelCase , "num_heads" ) )
class _a :
"""simple docstring"""
def __init__( self: Tuple , __lowerCamelCase: List[str] , __lowerCamelCase: str=13 , __lowerCamelCase: Tuple=64 , __lowerCamelCase: List[Any]=3 , __lowerCamelCase: List[Any]=[16, 48, 96] , __lowerCamelCase: Union[str, Any]=[1, 3, 6] , __lowerCamelCase: Tuple=[1, 2, 10] , __lowerCamelCase: int=[7, 3, 3] , __lowerCamelCase: Dict=[4, 2, 2] , __lowerCamelCase: int=[2, 1, 1] , __lowerCamelCase: Dict=[2, 2, 2] , __lowerCamelCase: List[str]=[False, False, True] , __lowerCamelCase: str=[0.0, 0.0, 0.0] , __lowerCamelCase: Union[str, Any]=0.02 , __lowerCamelCase: str=1e-12 , __lowerCamelCase: Optional[Any]=True , __lowerCamelCase: Tuple=True , __lowerCamelCase: Union[str, Any]=2 , ):
'''simple docstring'''
UpperCamelCase__: Dict = parent
UpperCamelCase__: Union[str, Any] = batch_size
UpperCamelCase__: int = image_size
UpperCamelCase__: Dict = patch_sizes
UpperCamelCase__: Any = patch_stride
UpperCamelCase__: Optional[int] = patch_padding
UpperCamelCase__: Any = is_training
UpperCamelCase__: Dict = use_labels
UpperCamelCase__: List[str] = num_labels
UpperCamelCase__: Tuple = num_channels
UpperCamelCase__: int = embed_dim
UpperCamelCase__: int = num_heads
UpperCamelCase__: Dict = stride_kv
UpperCamelCase__: Optional[int] = depth
UpperCamelCase__: int = cls_token
UpperCamelCase__: Optional[Any] = attention_drop_rate
UpperCamelCase__: Tuple = initializer_range
UpperCamelCase__: Dict = layer_norm_eps
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
UpperCamelCase__: Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__: Any = None
if self.use_labels:
# create a random int32 tensor of given shape
UpperCamelCase__: Tuple = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase__: List[Any] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self: Any ):
'''simple docstring'''
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def UpperCAmelCase_ ( self: int , __lowerCamelCase: Tuple , __lowerCamelCase: List[str] , __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
UpperCamelCase__: str = TFCvtModel(config=__lowerCamelCase )
UpperCamelCase__: str = model(__lowerCamelCase , training=__lowerCamelCase )
UpperCamelCase__: Optional[Any] = (self.image_size, self.image_size)
UpperCamelCase__ , UpperCamelCase__: Optional[Any] = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCamelCase__: Optional[Any] = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCamelCase__: List[Any] = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def UpperCAmelCase_ ( self: Dict , __lowerCamelCase: Optional[Any] , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: Dict ):
'''simple docstring'''
UpperCamelCase__: int = self.num_labels
UpperCamelCase__: Tuple = TFCvtForImageClassification(__lowerCamelCase )
UpperCamelCase__: Tuple = model(__lowerCamelCase , labels=__lowerCamelCase , training=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__: Dict = config_and_inputs
UpperCamelCase__: int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class _a ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
UpperCamelCase__ = (
{"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
if is_tf_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def UpperCAmelCase_ ( self: int ):
'''simple docstring'''
UpperCamelCase__: Optional[Any] = TFCvtModelTester(self )
UpperCamelCase__: int = TFCvtConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="Cvt does not output attentions" )
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason="Cvt does not use inputs_embeds" )
def UpperCAmelCase_ ( self: List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason="Cvt does not support input and output embeddings" )
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
def UpperCAmelCase_ ( self: int ):
'''simple docstring'''
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
@slow
def UpperCAmelCase_ ( self: List[str] ):
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8" )
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
UpperCamelCase__: int = tf.keras.mixed_precision.Policy("mixed_float16" )
tf.keras.mixed_precision.set_global_policy(__lowerCamelCase )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("float32" )
def UpperCAmelCase_ ( self: List[str] ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__: List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__: int = model_class(__lowerCamelCase )
UpperCamelCase__: Dict = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__: List[str] = [*signature.parameters.keys()]
UpperCamelCase__: Optional[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def UpperCAmelCase_ ( self: Dict ):
'''simple docstring'''
def check_hidden_states_output(__lowerCamelCase: Union[str, Any] , __lowerCamelCase: Optional[int] , __lowerCamelCase: str ):
UpperCamelCase__: Tuple = model_class(__lowerCamelCase )
UpperCamelCase__: Any = model(**self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
UpperCamelCase__: Optional[Any] = outputs.hidden_states
UpperCamelCase__: str = len(self.model_tester.depth )
self.assertEqual(len(__lowerCamelCase ) , __lowerCamelCase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCamelCase__ , UpperCamelCase__: List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__: int = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase__: Union[str, Any] = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
UpperCamelCase__: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
UpperCamelCase__: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
@slow
def UpperCAmelCase_ ( self: Optional[Any] ):
'''simple docstring'''
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__: List[Any] = TFCvtModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def lowerCAmelCase_ ( ):
UpperCamelCase__: Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class _a ( unittest.TestCase):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self: Union[str, Any] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCamelCase__: str = self.default_image_processor
UpperCamelCase__: List[str] = prepare_img()
UpperCamelCase__: Tuple = image_processor(images=__lowerCamelCase , return_tensors="tf" )
# forward pass
UpperCamelCase__: List[str] = model(**__lowerCamelCase )
# verify the logits
UpperCamelCase__: Optional[Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
UpperCamelCase__: Optional[Any] = tf.constant([0.9_285, 0.9_015, -0.3_150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , __lowerCamelCase , atol=1e-4 ) )
| 149 | 1 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
snake_case__ : Union[str, Any] = '''bert-base-cased'''
snake_case__ : List[str] = '''google/pegasus-xsum'''
snake_case__ : int = [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
snake_case__ : Tuple = ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
snake_case__ : List[str] = '''patrickvonplaten/t5-tiny-random'''
snake_case__ : Dict = '''sshleifer/bart-tiny-random'''
snake_case__ : List[Any] = '''sshleifer/tiny-mbart'''
snake_case__ : Dict = '''sshleifer/tiny-marian-en-de'''
def _snake_case ( _snake_case : Any , _snake_case : str ):
lowerCAmelCase : List[Any] = '\n'.join(__a )
Path(__a ).open('''w''' ).writelines(__a )
def _snake_case ( _snake_case : Union[str, Any] ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(__a , f'''{split}.source''' ) , __a )
_dump_articles(os.path.join(__a , f'''{split}.target''' ) , __a )
return tmp_dir
class snake_case_( snake_case_ ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def lowerCamelCase__ ( self : List[str] , UpperCamelCase_ : Any ):
lowerCAmelCase : int = AutoTokenizer.from_pretrained(_A )
lowerCAmelCase : Any = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
lowerCAmelCase : Dict = max(len(tokenizer.encode(_A ) ) for a in ARTICLES )
lowerCAmelCase : str = max(len(tokenizer.encode(_A ) ) for a in SUMMARIES )
lowerCAmelCase : str = 4
lowerCAmelCase : List[str] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
lowerCAmelCase : Optional[int] = 'ro_RO', 'de_DE' # ignored for all but mbart, but never causes error.
lowerCAmelCase : Union[str, Any] = SeqaSeqDataset(
_A , data_dir=_A , type_path='''train''' , max_source_length=_A , max_target_length=_A , src_lang=_A , tgt_lang=_A , )
lowerCAmelCase : Optional[Any] = DataLoader(_A , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(_A , _A )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
lowerCAmelCase : Union[str, Any] = shift_tokens_right(batch['''labels'''] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def lowerCamelCase__ ( self : Any , UpperCamelCase_ : List[str] ):
lowerCAmelCase : List[Any] = AutoTokenizer.from_pretrained(_A )
lowerCAmelCase : Tuple = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
lowerCAmelCase : List[str] = max(len(tokenizer.encode(_A ) ) for a in ARTICLES )
lowerCAmelCase : Dict = max(len(tokenizer.encode(_A ) ) for a in SUMMARIES )
lowerCAmelCase : str = 4
lowerCAmelCase : Tuple = LegacySeqaSeqDataset(
_A , data_dir=_A , type_path='''train''' , max_source_length=2_0 , max_target_length=_A , )
lowerCAmelCase : Union[str, Any] = DataLoader(_A , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 2_0 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def lowerCamelCase__ ( self : Dict ):
lowerCAmelCase : Dict = AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''' )
lowerCAmelCase : List[Any] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
lowerCAmelCase : Optional[Any] = tmp_dir.joinpath('''train.source''' ).open().readlines()
lowerCAmelCase : Optional[int] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(_A , _A , 1_2_8 , _A )
lowerCAmelCase : Optional[int] = {x.name for x in tmp_dir.iterdir()}
lowerCAmelCase : Optional[Any] = {x.name for x in save_dir.iterdir()}
lowerCAmelCase : List[str] = save_dir.joinpath('''train.source''' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(_A ) < len(_A )
assert len(_A ) == 1
assert len(packed_examples[0] ) == sum(len(_A ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='''This test requires fairseq''' )
def lowerCamelCase__ ( self : Union[str, Any] ):
if not FAIRSEQ_AVAILABLE:
return
lowerCAmelCase : str = self._get_dataset(max_len=6_4 )
lowerCAmelCase : Optional[int] = 6_4
lowerCAmelCase : List[Any] = ds.make_dynamic_sampler(_A , required_batch_size_multiple=_A )
lowerCAmelCase : Optional[int] = [len(_A ) for x in batch_sampler]
assert len(set(_A ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(_A ) == len(_A ) # no dropped or added examples
lowerCAmelCase : List[str] = DataLoader(_A , batch_sampler=_A , collate_fn=ds.collate_fn , num_workers=2 )
lowerCAmelCase : List[Any] = []
lowerCAmelCase : Optional[int] = []
for batch in data_loader:
lowerCAmelCase : str = batch['input_ids'].shape
lowerCAmelCase : Tuple = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
lowerCAmelCase : Optional[int] = np.product(batch['''input_ids'''].shape )
num_src_per_batch.append(_A )
if num_src_tokens > (max_tokens * 1.1):
failures.append(_A )
assert num_src_per_batch[0] == max(_A )
if failures:
raise AssertionError(F'''too many tokens in {len(_A )} batches''' )
def lowerCamelCase__ ( self : int ):
lowerCAmelCase : Optional[Any] = self._get_dataset(max_len=5_1_2 )
lowerCAmelCase : Dict = 2
lowerCAmelCase : Dict = ds.make_sortish_sampler(_A , shuffle=_A )
lowerCAmelCase : Dict = DataLoader(_A , batch_size=_A , collate_fn=ds.collate_fn , num_workers=2 )
lowerCAmelCase : Union[str, Any] = DataLoader(_A , batch_size=_A , collate_fn=ds.collate_fn , num_workers=2 , sampler=_A )
lowerCAmelCase : List[Any] = tokenizer.pad_token_id
def count_pad_tokens(UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int="input_ids" ):
return [batch[k].eq(_A ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(_A , k='''labels''' ) ) < sum(count_pad_tokens(_A , k='''labels''' ) )
assert sum(count_pad_tokens(_A ) ) < sum(count_pad_tokens(_A ) )
assert len(_A ) == len(_A )
def lowerCamelCase__ ( self : List[Any] , UpperCamelCase_ : Optional[int]=1_0_0_0 , UpperCamelCase_ : List[str]=1_2_8 ):
if os.getenv('''USE_REAL_DATA''' , _A ):
lowerCAmelCase : List[Any] = 'examples/seq2seq/wmt_en_ro'
lowerCAmelCase : Optional[Any] = max_len * 2 * 6_4
if not Path(_A ).joinpath('''train.len''' ).exists():
save_len_file(_A , _A )
else:
lowerCAmelCase : Union[str, Any] = 'examples/seq2seq/test_data/wmt_en_ro'
lowerCAmelCase : List[str] = max_len * 4
save_len_file(_A , _A )
lowerCAmelCase : List[str] = AutoTokenizer.from_pretrained(_A )
lowerCAmelCase : str = SeqaSeqDataset(
_A , data_dir=_A , type_path='''train''' , max_source_length=_A , max_target_length=_A , n_obs=_A , )
return ds, max_tokens, tokenizer
def lowerCamelCase__ ( self : int ):
lowerCAmelCase : Optional[int] = self._get_dataset()
lowerCAmelCase : List[str] = set(DistributedSortishSampler(_A , 2_5_6 , num_replicas=2 , rank=0 , add_extra_examples=_A ) )
lowerCAmelCase : List[str] = set(DistributedSortishSampler(_A , 2_5_6 , num_replicas=2 , rank=1 , add_extra_examples=_A ) )
assert idsa.intersection(_A ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def lowerCamelCase__ ( self : int , UpperCamelCase_ : int ):
lowerCAmelCase : List[Any] = AutoTokenizer.from_pretrained(_A , use_fast=_A )
if tok_name == MBART_TINY:
lowerCAmelCase : Optional[int] = SeqaSeqDataset(
_A , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , src_lang='''EN''' , tgt_lang='''FR''' , )
lowerCAmelCase : Dict = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
lowerCAmelCase : Optional[int] = SeqaSeqDataset(
_A , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , )
lowerCAmelCase : List[Any] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(_A ) == 1 if tok_name == BART_TINY else len(_A ) == 0
| 350 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : Any = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class snake_case_( a__ ):
__UpperCamelCase = '''vit_msn'''
def __init__( self : Dict , UpperCamelCase_ : str=7_6_8 , UpperCamelCase_ : List[Any]=1_2 , UpperCamelCase_ : Optional[Any]=1_2 , UpperCamelCase_ : str=3_0_7_2 , UpperCamelCase_ : List[Any]="gelu" , UpperCamelCase_ : List[Any]=0.0 , UpperCamelCase_ : Any=0.0 , UpperCamelCase_ : List[str]=0.02 , UpperCamelCase_ : List[Any]=1E-06 , UpperCamelCase_ : Tuple=2_2_4 , UpperCamelCase_ : Union[str, Any]=1_6 , UpperCamelCase_ : List[Any]=3 , UpperCamelCase_ : Any=True , **UpperCamelCase_ : Union[str, Any] , ):
super().__init__(**UpperCamelCase_ )
lowerCAmelCase : Any = hidden_size
lowerCAmelCase : Tuple = num_hidden_layers
lowerCAmelCase : List[Any] = num_attention_heads
lowerCAmelCase : Any = intermediate_size
lowerCAmelCase : Dict = hidden_act
lowerCAmelCase : int = hidden_dropout_prob
lowerCAmelCase : List[str] = attention_probs_dropout_prob
lowerCAmelCase : Tuple = initializer_range
lowerCAmelCase : Union[str, Any] = layer_norm_eps
lowerCAmelCase : Tuple = image_size
lowerCAmelCase : List[str] = patch_size
lowerCAmelCase : int = num_channels
lowerCAmelCase : Optional[int] = qkv_bias
| 314 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_UpperCAmelCase : Any = 300 # TEMPERATURE (unit = K)
def A ( lowercase , lowercase , lowercase , ) -> float:
'''simple docstring'''
if donor_conc <= 0:
raise ValueError('Donor concentration should be positive' )
elif acceptor_conc <= 0:
raise ValueError('Acceptor concentration should be positive' )
elif intrinsic_conc <= 0:
raise ValueError('Intrinsic concentration should be positive' )
elif donor_conc <= intrinsic_conc:
raise ValueError(
'Donor concentration should be greater than intrinsic concentration' )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
'Acceptor concentration should be greater than intrinsic concentration' )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 222 |
def lowerCAmelCase__( lowercase : List[Any] , lowercase : Optional[Any] , lowercase : Optional[int] , lowercase : str , lowercase : List[Any] , lowercase : List[str] ) -> int:
if index == r:
for j in range(lowercase ):
print(data[j] , end=" " )
print(" " )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
__snake_case : Union[str, Any] = arr[i]
combination_util(lowercase , lowercase , lowercase , index + 1 , lowercase , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(lowercase , lowercase , lowercase , lowercase , lowercase , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def lowerCAmelCase__( lowercase : Any , lowercase : Tuple , lowercase : Union[str, Any] ) -> Optional[Any]:
# A temporary array to store all combination one by one
__snake_case : Tuple = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(lowercase , lowercase , lowercase , 0 , lowercase , 0 )
if __name__ == "__main__":
# Driver code to check the function above
_UpperCamelCase = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 326 | 0 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
SCREAMING_SNAKE_CASE_: List[Any] =[
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0.bias', 'time_embedding.linear_1.bias'),
('time_embed.2.weight', 'time_embedding.linear_2.weight'),
('time_embed.2.bias', 'time_embedding.linear_2.bias'),
('input_blocks.0.0.weight', 'conv_in.weight'),
('input_blocks.0.0.bias', 'conv_in.bias'),
('out.0.weight', 'conv_norm_out.weight'),
('out.0.bias', 'conv_norm_out.bias'),
('out.2.weight', 'conv_out.weight'),
('out.2.bias', 'conv_out.bias'),
]
SCREAMING_SNAKE_CASE_: Optional[Any] =[
# (stable-diffusion, HF Diffusers)
('in_layers.0', 'norm1'),
('in_layers.2', 'conv1'),
('out_layers.0', 'norm2'),
('out_layers.3', 'conv2'),
('emb_layers.1', 'time_emb_proj'),
('skip_connection', 'conv_shortcut'),
]
SCREAMING_SNAKE_CASE_: Tuple =[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
SCREAMING_SNAKE_CASE_: List[str] =f"down_blocks.{i}.resnets.{j}."
SCREAMING_SNAKE_CASE_: Optional[Any] =f"input_blocks.{3*i + j + 1}.0."
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
SCREAMING_SNAKE_CASE_: List[str] =f"down_blocks.{i}.attentions.{j}."
SCREAMING_SNAKE_CASE_: List[Any] =f"input_blocks.{3*i + j + 1}.1."
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
SCREAMING_SNAKE_CASE_: Any =f"up_blocks.{i}.resnets.{j}."
SCREAMING_SNAKE_CASE_: Dict =f"output_blocks.{3*i + j}.0."
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
SCREAMING_SNAKE_CASE_: Optional[Any] =f"up_blocks.{i}.attentions.{j}."
SCREAMING_SNAKE_CASE_: Union[str, Any] =f"output_blocks.{3*i + j}.1."
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
SCREAMING_SNAKE_CASE_: Optional[Any] =f"down_blocks.{i}.downsamplers.0.conv."
SCREAMING_SNAKE_CASE_: Optional[Any] =f"input_blocks.{3*(i+1)}.0.op."
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
SCREAMING_SNAKE_CASE_: List[str] =f"up_blocks.{i}.upsamplers.0."
SCREAMING_SNAKE_CASE_: List[Any] =f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
SCREAMING_SNAKE_CASE_: List[Any] ='mid_block.attentions.0.'
SCREAMING_SNAKE_CASE_: int ='middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
SCREAMING_SNAKE_CASE_: Union[str, Any] =f"mid_block.resnets.{j}."
SCREAMING_SNAKE_CASE_: int =f"middle_block.{2*j}."
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def lowerCAmelCase_ ( snake_case_ : str ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
UpperCAmelCase_ = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
UpperCAmelCase_ = v.replace(snake_case_ , snake_case_ )
UpperCAmelCase_ = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
UpperCAmelCase_ = v.replace(snake_case_ , snake_case_ )
UpperCAmelCase_ = v
UpperCAmelCase_ = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
SCREAMING_SNAKE_CASE_: Any =[
# (stable-diffusion, HF Diffusers)
('nin_shortcut', 'conv_shortcut'),
('norm_out', 'conv_norm_out'),
('mid.attn_1.', 'mid_block.attentions.0.'),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
SCREAMING_SNAKE_CASE_: Any =f"encoder.down_blocks.{i}.resnets.{j}."
SCREAMING_SNAKE_CASE_: str =f"encoder.down.{i}.block.{j}."
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
SCREAMING_SNAKE_CASE_: Any =f"down_blocks.{i}.downsamplers.0."
SCREAMING_SNAKE_CASE_: List[Any] =f"down.{i}.downsample."
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
SCREAMING_SNAKE_CASE_: List[str] =f"up_blocks.{i}.upsamplers.0."
SCREAMING_SNAKE_CASE_: Dict =f"up.{3-i}.upsample."
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
SCREAMING_SNAKE_CASE_: Optional[int] =f"decoder.up_blocks.{i}.resnets.{j}."
SCREAMING_SNAKE_CASE_: Tuple =f"decoder.up.{3-i}.block.{j}."
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
SCREAMING_SNAKE_CASE_: str =f"mid_block.resnets.{i}."
SCREAMING_SNAKE_CASE_: Optional[Any] =f"mid.block_{i+1}."
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
SCREAMING_SNAKE_CASE_: Optional[int] =[
# (stable-diffusion, HF Diffusers)
('norm.', 'group_norm.'),
('q.', 'query.'),
('k.', 'key.'),
('v.', 'value.'),
('proj_out.', 'proj_attn.'),
]
def lowerCAmelCase_ ( snake_case_ : Tuple ) -> Optional[int]:
'''simple docstring'''
return w.reshape(*w.shape , 1 , 1 )
def lowerCAmelCase_ ( snake_case_ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
UpperCAmelCase_ = v.replace(snake_case_ , snake_case_ )
UpperCAmelCase_ = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
UpperCAmelCase_ = v.replace(snake_case_ , snake_case_ )
UpperCAmelCase_ = v
UpperCAmelCase_ = {v: vae_state_dict[k] for k, v in mapping.items()}
UpperCAmelCase_ = ["q", "k", "v", "proj_out"]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f"""mid.attn_1.{weight_name}.weight""" in k:
print(f"""Reshaping {k} for SD format""" )
UpperCAmelCase_ = reshape_weight_for_sd(snake_case_ )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
SCREAMING_SNAKE_CASE_: Tuple =[
# (stable-diffusion, HF Diffusers)
('resblocks.', 'text_model.encoder.layers.'),
('ln_1', 'layer_norm1'),
('ln_2', 'layer_norm2'),
('.c_fc.', '.fc1.'),
('.c_proj.', '.fc2.'),
('.attn', '.self_attn'),
('ln_final.', 'transformer.text_model.final_layer_norm.'),
('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
SCREAMING_SNAKE_CASE_: Optional[int] ={re.escape(x[1]): x[0] for x in textenc_conversion_lst}
SCREAMING_SNAKE_CASE_: List[str] =re.compile('|'.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
SCREAMING_SNAKE_CASE_: Union[str, Any] ={'q': 0, 'k': 1, 'v': 2}
def lowerCAmelCase_ ( snake_case_ : Any ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ = {}
UpperCAmelCase_ = {}
UpperCAmelCase_ = {}
for k, v in text_enc_dict.items():
if (
k.endswith(".self_attn.q_proj.weight" )
or k.endswith(".self_attn.k_proj.weight" )
or k.endswith(".self_attn.v_proj.weight" )
):
UpperCAmelCase_ = k[: -len(".q_proj.weight" )]
UpperCAmelCase_ = k[-len("q_proj.weight" )]
if k_pre not in capture_qkv_weight:
UpperCAmelCase_ = [None, None, None]
UpperCAmelCase_ = v
continue
if (
k.endswith(".self_attn.q_proj.bias" )
or k.endswith(".self_attn.k_proj.bias" )
or k.endswith(".self_attn.v_proj.bias" )
):
UpperCAmelCase_ = k[: -len(".q_proj.bias" )]
UpperCAmelCase_ = k[-len("q_proj.bias" )]
if k_pre not in capture_qkv_bias:
UpperCAmelCase_ = [None, None, None]
UpperCAmelCase_ = v
continue
UpperCAmelCase_ = textenc_pattern.sub(lambda snake_case_ : protected[re.escape(m.group(0 ) )] , snake_case_ )
UpperCAmelCase_ = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
UpperCAmelCase_ = textenc_pattern.sub(lambda snake_case_ : protected[re.escape(m.group(0 ) )] , snake_case_ )
UpperCAmelCase_ = torch.cat(snake_case_ )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
UpperCAmelCase_ = textenc_pattern.sub(lambda snake_case_ : protected[re.escape(m.group(0 ) )] , snake_case_ )
UpperCAmelCase_ = torch.cat(snake_case_ )
return new_state_dict
def lowerCAmelCase_ ( snake_case_ : Tuple ) -> Optional[int]:
'''simple docstring'''
return text_enc_dict
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_: Union[str, Any] =argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
)
SCREAMING_SNAKE_CASE_: Tuple =parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
SCREAMING_SNAKE_CASE_: Optional[Any] =osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
SCREAMING_SNAKE_CASE_: List[Any] =osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
SCREAMING_SNAKE_CASE_: Optional[int] =osp.join(args.model_path, 'text_encoder', 'model.safetensors')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
SCREAMING_SNAKE_CASE_: Optional[Any] =load_file(unet_path, device='cpu')
else:
SCREAMING_SNAKE_CASE_: Optional[int] =osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
SCREAMING_SNAKE_CASE_: Optional[int] =torch.load(unet_path, map_location='cpu')
if osp.exists(vae_path):
SCREAMING_SNAKE_CASE_: Optional[int] =load_file(vae_path, device='cpu')
else:
SCREAMING_SNAKE_CASE_: Union[str, Any] =osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
SCREAMING_SNAKE_CASE_: Union[str, Any] =torch.load(vae_path, map_location='cpu')
if osp.exists(text_enc_path):
SCREAMING_SNAKE_CASE_: List[str] =load_file(text_enc_path, device='cpu')
else:
SCREAMING_SNAKE_CASE_: Optional[Any] =osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
SCREAMING_SNAKE_CASE_: List[Any] =torch.load(text_enc_path, map_location='cpu')
# Convert the UNet model
SCREAMING_SNAKE_CASE_: Tuple =convert_unet_state_dict(unet_state_dict)
SCREAMING_SNAKE_CASE_: List[str] ={'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
SCREAMING_SNAKE_CASE_: Dict =convert_vae_state_dict(vae_state_dict)
SCREAMING_SNAKE_CASE_: Union[str, Any] ={'first_stage_model.' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
SCREAMING_SNAKE_CASE_: Optional[int] ='text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
SCREAMING_SNAKE_CASE_: Tuple ={'transformer.' + k: v for k, v in text_enc_dict.items()}
SCREAMING_SNAKE_CASE_: Any =convert_text_enc_state_dict_vaa(text_enc_dict)
SCREAMING_SNAKE_CASE_: Optional[Any] ={'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
else:
SCREAMING_SNAKE_CASE_: Dict =convert_text_enc_state_dict(text_enc_dict)
SCREAMING_SNAKE_CASE_: Any ={'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
SCREAMING_SNAKE_CASE_: Any ={**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
SCREAMING_SNAKE_CASE_: Any ={k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
SCREAMING_SNAKE_CASE_: Any ={'state_dict': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 106 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_: List[str] =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Tuple ={
'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class __A ( UpperCamelCase__ , UpperCamelCase__ ):
a__ : Optional[Any] = """resnet"""
a__ : Tuple = ["""basic""", """bottleneck"""]
def __init__(self : List[Any] , __a : Any=3 , __a : Dict=64 , __a : Union[str, Any]=[256, 512, 1024, 2048] , __a : str=[3, 4, 6, 3] , __a : Optional[Any]="bottleneck" , __a : Tuple="relu" , __a : int=False , __a : Optional[int]=None , __a : str=None , **__a : Dict , ):
super().__init__(**__a )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types )}""" )
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = embedding_size
UpperCAmelCase_ = hidden_sizes
UpperCAmelCase_ = depths
UpperCAmelCase_ = layer_type
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = downsample_in_first_stage
UpperCAmelCase_ = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(__a ) + 1 )]
UpperCAmelCase_ , UpperCAmelCase_ = get_aligned_output_features_output_indices(
out_features=__a , out_indices=__a , stage_names=self.stage_names )
class __A ( UpperCamelCase__ ):
a__ : int = version.parse("""1.11""" )
@property
def _lowercase (self : Optional[int] ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _lowercase (self : str ):
return 1E-3
| 106 | 1 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
lowerCamelCase :Tuple = '''path-to-your-trained-model'''
lowerCamelCase :Optional[int] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
lowerCamelCase :Optional[int] = '''A photo of sks dog in a bucket'''
lowerCamelCase :List[Any] = pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]
image.save('''dog-bucket.png''') | 206 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
lowerCamelCase :Any = logging.get_logger(__name__) # pylint: disable=invalid-name
class _lowerCAmelCase ( __UpperCAmelCase ):
def __init__(self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ):
super().__init__()
self.register_modules(
vae=lowercase , text_encoder=lowercase , tokenizer=lowercase , unet=lowercase , scheduler=lowercase , safety_checker=lowercase , feature_extractor=lowercase , )
def _a (self , lowercase = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
A_ : int = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowercase )
def _a (self ):
self.enable_attention_slicing(lowercase )
@torch.no_grad()
def __call__(self , lowercase , lowercase = 512 , lowercase = 512 , lowercase = 50 , lowercase = 7.5 , lowercase = None , lowercase = 1 , lowercase = 0.0 , lowercase = None , lowercase = None , lowercase = "pil" , lowercase = True , lowercase = None , lowercase = 1 , lowercase = None , **lowercase , ):
if isinstance(lowercase , lowercase ):
A_ : Union[str, Any] = 1
elif isinstance(lowercase , lowercase ):
A_ : Any = len(lowercase )
else:
raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase )}' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowercase , lowercase ) or callback_steps <= 0)
):
raise ValueError(
F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
F' {type(lowercase )}.' )
# get prompt text embeddings
A_ : Optional[Any] = self.tokenizer(
lowercase , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
A_ : Dict = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
A_ : Union[str, Any] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
F' {self.tokenizer.model_max_length} tokens: {removed_text}' )
A_ : Dict = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
A_ : Any = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
A_, A_, A_ : Tuple = text_embeddings.shape
A_ : Optional[Any] = text_embeddings.repeat(1 , lowercase , 1 )
A_ : Any = text_embeddings.view(bs_embed * num_images_per_prompt , lowercase , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
A_ : List[str] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
A_ : List[str]
if negative_prompt is None:
A_ : Optional[int] = [""""""]
elif type(lowercase ) is not type(lowercase ):
raise TypeError(
F'`negative_prompt` should be the same type to `prompt`, but got {type(lowercase )} !='
F' {type(lowercase )}.' )
elif isinstance(lowercase , lowercase ):
A_ : Dict = [negative_prompt]
elif batch_size != len(lowercase ):
raise ValueError(
F'`negative_prompt`: {negative_prompt} has batch size {len(lowercase )}, but `prompt`:'
F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
""" the batch size of `prompt`.""" )
else:
A_ : Dict = negative_prompt
A_ : int = text_input_ids.shape[-1]
A_ : List[Any] = self.tokenizer(
lowercase , padding="""max_length""" , max_length=lowercase , truncation=lowercase , return_tensors="""pt""" , )
A_ : str = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
A_ : Optional[Any] = uncond_embeddings.shape[1]
A_ : str = uncond_embeddings.repeat(lowercase , lowercase , 1 )
A_ : List[str] = uncond_embeddings.view(batch_size * num_images_per_prompt , lowercase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
A_ : Dict = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
A_ : int = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
A_ : Dict = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
A_ : Dict = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
A_ : Tuple = torch.randn(
lowercase , generator=lowercase , device="""cpu""" , dtype=lowercase ).to(self.device )
A_ : int = torch.randn(lowercase , generator=lowercase , device="""cpu""" , dtype=lowercase ).to(
self.device )
else:
A_ : int = torch.randn(
lowercase , generator=lowercase , device=self.device , dtype=lowercase )
A_ : str = torch.randn(lowercase , generator=lowercase , device=self.device , dtype=lowercase )
else:
if latents_reference.shape != latents_shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
A_ : str = latents_reference.to(self.device )
A_ : Tuple = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
A_ : Optional[int] = (latents_shape[3] - latents_shape_reference[3]) // 2
A_ : Optional[int] = (latents_shape[2] - latents_shape_reference[2]) // 2
A_ : Optional[int] = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
A_ : int = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
A_ : Optional[Any] = 0 if dx < 0 else dx
A_ : Optional[Any] = 0 if dy < 0 else dy
A_ : Optional[int] = max(-dx , 0 )
A_ : List[str] = max(-dy , 0 )
# import pdb
# pdb.set_trace()
A_ : str = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(lowercase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
A_ : Any = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
A_ : Tuple = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
A_ : Dict = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
A_ : Any = {}
if accepts_eta:
A_ : Optional[int] = eta
for i, t in enumerate(self.progress_bar(lowercase ) ):
# expand the latents if we are doing classifier free guidance
A_ : Tuple = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A_ : Tuple = self.scheduler.scale_model_input(lowercase , lowercase )
# predict the noise residual
A_ : List[str] = self.unet(lowercase , lowercase , encoder_hidden_states=lowercase ).sample
# perform guidance
if do_classifier_free_guidance:
A_, A_ : str = noise_pred.chunk(2 )
A_ : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
A_ : List[str] = self.scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowercase , lowercase , lowercase )
A_ : List[str] = 1 / 0.1_82_15 * latents
A_ : List[str] = self.vae.decode(lowercase ).sample
A_ : Optional[int] = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
A_ : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
A_ : Union[str, Any] = self.feature_extractor(self.numpy_to_pil(lowercase ) , return_tensors="""pt""" ).to(
self.device )
A_, A_ : Optional[int] = self.safety_checker(
images=lowercase , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
A_ : Tuple = None
if output_type == "pil":
A_ : Tuple = self.numpy_to_pil(lowercase )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=lowercase , nsfw_content_detected=lowercase ) | 206 | 1 |
__UpperCAmelCase : int = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 315 |
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 UpperCAmelCase_ ( _a):
'''simple docstring'''
def __init__( self , *__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
UpperCamelCase : Any = eval_examples
UpperCamelCase : Optional[Any] = post_process_function
def _lowercase ( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE = "eval" ):
"""simple docstring"""
UpperCamelCase : int = self.eval_dataset if eval_dataset is None else eval_dataset
UpperCamelCase : int = self.get_eval_dataloader(__SCREAMING_SNAKE_CASE )
UpperCamelCase : str = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
UpperCamelCase : Any = self.compute_metrics
UpperCamelCase : List[Any] = None
UpperCamelCase : str = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCamelCase : Dict = time.time()
try:
UpperCamelCase : str = eval_loop(
__SCREAMING_SNAKE_CASE , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__SCREAMING_SNAKE_CASE , metric_key_prefix=__SCREAMING_SNAKE_CASE , )
finally:
UpperCamelCase : Union[str, Any] = compute_metrics
UpperCamelCase : 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(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , 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
UpperCamelCase : Dict = self.post_process_function(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , output.predictions )
UpperCamelCase : Optional[Any] = self.compute_metrics(__SCREAMING_SNAKE_CASE )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
UpperCamelCase : Dict = metrics.pop(__SCREAMING_SNAKE_CASE )
metrics.update(output.metrics )
else:
UpperCamelCase : List[Any] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(__SCREAMING_SNAKE_CASE )
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() )
UpperCamelCase : Any = self.callback_handler.on_evaluate(self.args , self.state , self.control , __SCREAMING_SNAKE_CASE )
return metrics
def _lowercase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE = "test" ):
"""simple docstring"""
UpperCamelCase : Tuple = self.get_test_dataloader(__SCREAMING_SNAKE_CASE )
# Temporarily disable metric computation, we will do it in the loop here.
UpperCamelCase : Union[str, Any] = self.compute_metrics
UpperCamelCase : Tuple = None
UpperCamelCase : str = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCamelCase : Optional[int] = time.time()
try:
UpperCamelCase : int = eval_loop(
__SCREAMING_SNAKE_CASE , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__SCREAMING_SNAKE_CASE , metric_key_prefix=__SCREAMING_SNAKE_CASE , )
finally:
UpperCamelCase : int = compute_metrics
UpperCamelCase : Dict = 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(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , 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
UpperCamelCase : Dict = self.post_process_function(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , output.predictions , '''predict''' )
UpperCamelCase : Union[str, Any] = self.compute_metrics(__SCREAMING_SNAKE_CASE )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
UpperCamelCase : Any = metrics.pop(__SCREAMING_SNAKE_CASE )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__SCREAMING_SNAKE_CASE )
| 315 | 1 |
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Dict:
'''simple docstring'''
snake_case_ : Any = name
snake_case_ : int = value
snake_case_ : Optional[int] = weight
def __repr__(self ) -> Dict:
'''simple docstring'''
return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return self.value
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
return self.name
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
return self.weight
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
return self.value / self.weight
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Tuple = []
for i in range(len(_UpperCamelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = sorted(_UpperCamelCase , key=_UpperCamelCase , reverse=_UpperCamelCase )
snake_case_ : Any = []
snake_case_ , snake_case_ : List[Any] = 0.0, 0.0
for i in range(len(_UpperCamelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''megatron-bert'''
def __init__(self , __magic_name__=2_9056 , __magic_name__=1024 , __magic_name__=24 , __magic_name__=16 , __magic_name__=4096 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=512 , __magic_name__=2 , __magic_name__=0.02 , __magic_name__=1e-12 , __magic_name__=0 , __magic_name__="absolute" , __magic_name__=True , **__magic_name__ , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : Union[str, Any] = vocab_size
snake_case_ : Dict = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : Optional[int] = num_attention_heads
snake_case_ : int = hidden_act
snake_case_ : List[str] = intermediate_size
snake_case_ : Dict = hidden_dropout_prob
snake_case_ : str = attention_probs_dropout_prob
snake_case_ : str = max_position_embeddings
snake_case_ : Any = type_vocab_size
snake_case_ : int = initializer_range
snake_case_ : int = layer_norm_eps
snake_case_ : List[str] = position_embedding_type
snake_case_ : Dict = use_cache
| 279 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
SCREAMING_SNAKE_CASE__ = TypeVar("""T""")
SCREAMING_SNAKE_CASE__ = Union[List[T], Tuple[T, ...]]
SCREAMING_SNAKE_CASE__ = Union[T, List[T], Dict[str, T]]
SCREAMING_SNAKE_CASE__ = Union[str, bytes, os.PathLike]
| 356 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self , UpperCAmelCase ) -> Any:
'''simple docstring'''
lowercase_ = data
def __iter__( self ) -> List[str]:
'''simple docstring'''
for element in self.data:
yield element
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Optional[Any]=True ):
'''simple docstring'''
lowercase_ = Accelerator(even_batches=__lowerCamelCase )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Accelerator , __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: bool = False ):
'''simple docstring'''
if iterable:
lowercase_ = DummyIterableDataset(torch.as_tensor(range(__lowerCamelCase ) ) )
else:
lowercase_ = TensorDataset(torch.as_tensor(range(__lowerCamelCase ) ) )
lowercase_ = DataLoader(__lowerCamelCase , batch_size=__lowerCamelCase )
lowercase_ = accelerator.prepare(__lowerCamelCase )
return dl
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Accelerator , __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: List[int] , __lowerCamelCase: List[int] , ):
'''simple docstring'''
lowercase_ = create_dataloader(accelerator=__lowerCamelCase , dataset_size=__lowerCamelCase , batch_size=__lowerCamelCase )
lowercase_ = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
__lowerCamelCase , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
__lowerCamelCase , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = create_accelerator(even_batches=__lowerCamelCase )
verify_dataloader_batch_sizes(
__lowerCamelCase , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
__lowerCamelCase , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = create_accelerator(even_batches=__lowerCamelCase )
lowercase_ = torch.nn.Linear(1 , 1 )
lowercase_ = accelerator.prepare(__lowerCamelCase )
lowercase_ = create_dataloader(__lowerCamelCase , dataset_size=3 , batch_size=1 )
lowercase_ = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(__lowerCamelCase ):
lowercase_ = ddp_model(batch[0].float() )
lowercase_ = output.sum()
loss.backward()
batch_idxs.append(__lowerCamelCase )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
with warnings.catch_warnings(record=__lowerCamelCase ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , __lowerCamelCase )
assert "only supported for multi-GPU" in str(w[-1].message )
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = True
lowercase_ = False
lowercase_ = create_accelerator(even_batches=__lowerCamelCase )
lowercase_ = torch.nn.Linear(1 , 1 )
lowercase_ = accelerator.prepare(__lowerCamelCase )
lowercase_ = create_dataloader(__lowerCamelCase , dataset_size=3 , batch_size=1 )
lowercase_ = create_dataloader(__lowerCamelCase , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__lowerCamelCase ):
lowercase_ = train_dl.batch_sampler.even_batches
lowercase_ = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = True
lowercase_ = False
lowercase_ = create_accelerator(even_batches=__lowerCamelCase )
lowercase_ = torch.nn.Linear(1 , 1 )
lowercase_ = accelerator.prepare(__lowerCamelCase )
create_dataloader(__lowerCamelCase , dataset_size=3 , batch_size=1 , iterable=__lowerCamelCase )
lowercase_ = create_dataloader(__lowerCamelCase , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings("ignore" )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__lowerCamelCase ):
lowercase_ = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = create_accelerator()
lowercase_ = torch.nn.Linear(1 , 1 )
lowercase_ = accelerator.prepare(__lowerCamelCase )
create_dataloader(__lowerCamelCase , dataset_size=3 , batch_size=1 , iterable=__lowerCamelCase )
with warnings.catch_warnings(record=__lowerCamelCase ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=__lowerCamelCase ):
pass
assert issubclass(w[-1].category , __lowerCamelCase )
assert "only supported for map-style datasets" in str(w[-1].message )
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = create_accelerator()
accelerator.print("Test that even_batches variable ensures uniform batches across processes" )
test_default_ensures_even_batch_sizes()
accelerator.print("Run tests with even_batches disabled" )
test_can_disable_even_batches()
accelerator.print("Test joining uneven inputs" )
test_can_join_uneven_inputs()
accelerator.print("Test overriding even_batches when joining uneven inputs" )
test_join_can_override_even_batches()
accelerator.print("Test overriding even_batches for mixed dataloader types" )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders" )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print("Test join with non DDP distributed raises warning" )
lowercase_ = accelerator.state.distributed_type
lowercase_ = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(__lowerCamelCase )
lowercase_ = original_state
if __name__ == "__main__":
main()
| 297 | 0 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
__lowerCamelCase : Tuple = logging.getLogger(__name__)
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = "token-classification"
def __init__( self : Optional[int] , _lowercase : Union[str, Any] ):
"""simple docstring"""
if type(_lowercase ) == dict:
SCREAMING_SNAKE_CASE__ = Namespace(**_lowercase )
SCREAMING_SNAKE_CASE__ = import_module("""tasks""" )
try:
SCREAMING_SNAKE_CASE__ = getattr(_lowercase , hparams.task_type )
SCREAMING_SNAKE_CASE__ = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
SCREAMING_SNAKE_CASE__ = self.token_classification_task.get_labels(hparams.labels )
SCREAMING_SNAKE_CASE__ = CrossEntropyLoss().ignore_index
super().__init__(_lowercase , len(self.labels ) , self.mode )
def __a ( self : Optional[Any] , **_lowercase : str ):
"""simple docstring"""
return self.model(**_lowercase )
def __a ( self : Any , _lowercase : Dict , _lowercase : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
SCREAMING_SNAKE_CASE__ = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
SCREAMING_SNAKE_CASE__ = self(**_lowercase )
SCREAMING_SNAKE_CASE__ = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.hparams
for mode in ["train", "dev", "test"]:
SCREAMING_SNAKE_CASE__ = self._feature_file(_lowercase )
if os.path.exists(_lowercase ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , _lowercase )
SCREAMING_SNAKE_CASE__ = torch.load(_lowercase )
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir )
SCREAMING_SNAKE_CASE__ = self.token_classification_task.read_examples_from_file(args.data_dir , _lowercase )
SCREAMING_SNAKE_CASE__ = self.token_classification_task.convert_examples_to_features(
_lowercase , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=_lowercase , pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("""Saving features into cached file %s""" , _lowercase )
torch.save(_lowercase , _lowercase )
def __a ( self : str , _lowercase : int , _lowercase : int , _lowercase : bool = False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self._feature_file(_lowercase )
logger.info("""Loading features from cached file %s""" , _lowercase )
SCREAMING_SNAKE_CASE__ = torch.load(_lowercase )
SCREAMING_SNAKE_CASE__ = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
SCREAMING_SNAKE_CASE__ = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
SCREAMING_SNAKE_CASE__ = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
SCREAMING_SNAKE_CASE__ = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
SCREAMING_SNAKE_CASE__ = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(_lowercase , _lowercase , _lowercase , _lowercase ) , batch_size=_lowercase )
def __a ( self : Union[str, Any] , _lowercase : int , _lowercase : Tuple ):
"""simple docstring"""
"""Compute validation""" ""
SCREAMING_SNAKE_CASE__ = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
SCREAMING_SNAKE_CASE__ = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
SCREAMING_SNAKE_CASE__ = self(**_lowercase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = outputs[:2]
SCREAMING_SNAKE_CASE__ = logits.detach().cpu().numpy()
SCREAMING_SNAKE_CASE__ = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def __a ( self : Dict , _lowercase : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
SCREAMING_SNAKE_CASE__ = np.concatenate([x["""pred"""] for x in outputs] , axis=0 )
SCREAMING_SNAKE_CASE__ = np.argmax(_lowercase , axis=2 )
SCREAMING_SNAKE_CASE__ = np.concatenate([x["""target"""] for x in outputs] , axis=0 )
SCREAMING_SNAKE_CASE__ = dict(enumerate(self.labels ) )
SCREAMING_SNAKE_CASE__ = [[] for _ in range(out_label_ids.shape[0] )]
SCREAMING_SNAKE_CASE__ = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
SCREAMING_SNAKE_CASE__ = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(_lowercase , _lowercase ),
"""precision""": precision_score(_lowercase , _lowercase ),
"""recall""": recall_score(_lowercase , _lowercase ),
"""f1""": fa_score(_lowercase , _lowercase ),
}
SCREAMING_SNAKE_CASE__ = dict(results.items() )
SCREAMING_SNAKE_CASE__ = results
return ret, preds_list, out_label_list
def __a ( self : Union[str, Any] , _lowercase : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._eval_end(_lowercase )
SCREAMING_SNAKE_CASE__ = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def __a ( self : Optional[int] , _lowercase : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self._eval_end(_lowercase )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
SCREAMING_SNAKE_CASE__ = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def __a ( _lowercase : Optional[Any] , _lowercase : Optional[int] ):
"""simple docstring"""
BaseTransformer.add_model_specific_args(_lowercase , _lowercase )
parser.add_argument(
"""--task_type""" , default="""NER""" , type=_lowercase , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" , default=1_28 , type=_lowercase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--labels""" , default="""""" , type=_lowercase , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=_lowercase , help="""The number of GPUs allocated for this, it is by default 0 meaning none""" , )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
return parser
if __name__ == "__main__":
__lowerCamelCase : str = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
__lowerCamelCase : List[str] = NERTransformer.add_model_specific_args(parser, os.getcwd())
__lowerCamelCase : Any = parser.parse_args()
__lowerCamelCase : Dict = NERTransformer(args)
__lowerCamelCase : Optional[Any] = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
__lowerCamelCase : Tuple = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
__lowerCamelCase : Union[str, Any] = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 219 | import socket
def __SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE__ = socket.gethostname()
SCREAMING_SNAKE_CASE__ = 1_23_12
sock.connect((host, port) )
sock.send(B"""Hello server!""" )
with open("""Received_file""" , """wb""" ) as out_file:
print("""File opened""" )
print("""Receiving data...""" )
while True:
SCREAMING_SNAKE_CASE__ = sock.recv(10_24 )
if not data:
break
out_file.write(__UpperCamelCase )
print("""Successfully received the file""" )
sock.close()
print("""Connection closed""" )
if __name__ == "__main__":
main()
| 219 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowercase : List[Any] = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
_A = ['input_values', 'attention_mask']
def __init__( self :List[Any] , a :int = 1 , a :int = 1_6_0_0_0 , a :float = 0.0 , a :bool = False , a :int = 8_0 , a :int = 1_6 , a :int = 6_4 , a :str = "hann_window" , a :float = 1.0 , a :float = 8_0 , a :float = 7_6_0_0 , a :float = 1E-1_0 , a :int = 2 , a :bool = True , **a :int , ) -> List[Any]:
super().__init__(feature_size=a , sampling_rate=a , padding_value=a , **a )
__UpperCamelCase : List[str] = do_normalize
__UpperCamelCase : List[Any] = return_attention_mask
__UpperCamelCase : str = num_mel_bins
__UpperCamelCase : Optional[Any] = hop_length
__UpperCamelCase : Any = win_length
__UpperCamelCase : str = win_function
__UpperCamelCase : Tuple = frame_signal_scale
__UpperCamelCase : Tuple = fmin
__UpperCamelCase : Union[str, Any] = fmax
__UpperCamelCase : Optional[Any] = mel_floor
__UpperCamelCase : Dict = reduction_factor
__UpperCamelCase : List[Any] = win_length * sampling_rate // 1_0_0_0
__UpperCamelCase : List[Any] = hop_length * sampling_rate // 1_0_0_0
__UpperCamelCase : str = optimal_fft_length(self.sample_size )
__UpperCamelCase : Optional[int] = (self.n_fft // 2) + 1
__UpperCamelCase : str = window_function(window_length=self.sample_size , name=self.win_function , periodic=a )
__UpperCamelCase : int = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm="slaney" , mel_scale="slaney" , )
if frame_signal_scale != 1.0:
warnings.warn(
"The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers" , a , )
if reduction_factor != 2.0:
warnings.warn(
"The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers" , a , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def _lowerCamelCase ( a :List[np.ndarray] , a :List[np.ndarray] , a :float = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
__UpperCamelCase : Dict = np.array(a , np.intaa )
__UpperCamelCase : List[Any] = []
for vector, length in zip(a , attention_mask.sum(-1 ) ):
__UpperCamelCase : List[str] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
__UpperCamelCase : List[Any] = padding_value
normed_input_values.append(a )
else:
__UpperCamelCase : Optional[int] = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def _lowerCamelCase ( self :int , a :np.ndarray , ) -> np.ndarray:
__UpperCamelCase : Dict = spectrogram(
a , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel="log10" , )
return log_mel_spec.T
def __call__( self :Optional[Any] , a :Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , a :Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , a :Union[bool, str, PaddingStrategy] = False , a :Optional[int] = None , a :bool = False , a :Optional[int] = None , a :Optional[bool] = None , a :Optional[Union[str, TensorType]] = None , a :Optional[int] = None , **a :List[Any] , ) -> BatchFeature:
if audio is None and audio_target is None:
raise ValueError("You must provide either `audio` or `audio_target` values." )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
f' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
f' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
if audio is not None:
__UpperCamelCase : Union[str, Any] = self._process_audio(
a , a , a , a , a , a , a , a , **a , )
else:
__UpperCamelCase : List[Any] = None
if audio_target is not None:
__UpperCamelCase : Optional[Any] = self._process_audio(
a , a , a , a , a , a , a , a , **a , )
if inputs is None:
return inputs_target
else:
__UpperCamelCase : Dict = inputs_target["input_values"]
__UpperCamelCase : str = inputs_target.get("attention_mask" )
if decoder_attention_mask is not None:
__UpperCamelCase : List[str] = decoder_attention_mask
return inputs
def _lowerCamelCase ( self :Dict , a :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , a :bool = False , a :Union[bool, str, PaddingStrategy] = False , a :Optional[int] = None , a :bool = False , a :Optional[int] = None , a :Optional[bool] = None , a :Optional[Union[str, TensorType]] = None , **a :Any , ) -> BatchFeature:
__UpperCamelCase : Any = isinstance(a , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
__UpperCamelCase : str = is_batched_numpy or (
isinstance(a , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__UpperCamelCase : Dict = [np.asarray(a , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(a , np.ndarray ):
__UpperCamelCase : int = np.asarray(a , dtype=np.floataa )
elif isinstance(a , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
__UpperCamelCase : List[str] = speech.astype(np.floataa )
# always return batch
if not is_batched:
__UpperCamelCase : List[str] = [speech]
# needed to make pad() work on spectrogram inputs
__UpperCamelCase : Dict = self.feature_size
# convert into correct format for padding
if is_target:
__UpperCamelCase : Tuple = [self._extract_mel_features(a ) for waveform in speech]
__UpperCamelCase : Tuple = BatchFeature({"input_values": features} )
__UpperCamelCase : Optional[Any] = self.num_mel_bins
else:
__UpperCamelCase : Any = BatchFeature({"input_values": speech} )
__UpperCamelCase : List[Any] = self.pad(
a , padding=a , max_length=a , truncation=a , pad_to_multiple_of=a , return_attention_mask=a , **a , )
__UpperCamelCase : Any = feature_size_hack
# convert input values to correct format
__UpperCamelCase : Union[str, Any] = padded_inputs["input_values"]
if not isinstance(input_values[0] , np.ndarray ):
__UpperCamelCase : List[str] = [np.asarray(a , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(a , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
__UpperCamelCase : List[str] = [array.astype(np.floataa ) for array in input_values]
elif isinstance(a , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
__UpperCamelCase : Union[str, Any] = input_values.astype(np.floataa )
# convert attention_mask to correct format
__UpperCamelCase : str = padded_inputs.get("attention_mask" )
if attention_mask is not None:
__UpperCamelCase : List[str] = [np.asarray(a , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
__UpperCamelCase : Dict = (
attention_mask
if self._get_padding_strategies(a , max_length=a ) is not PaddingStrategy.DO_NOT_PAD
else None
)
__UpperCamelCase : Any = self.zero_mean_unit_var_norm(
padded_inputs["input_values"] , attention_mask=a , padding_value=self.padding_value )
if return_tensors is not None:
__UpperCamelCase : Any = padded_inputs.convert_to_tensors(a )
return padded_inputs
def _lowerCamelCase ( self :Any ) -> Dict[str, Any]:
__UpperCamelCase : Optional[Any] = super().to_dict()
# Don't serialize these as they are derived from the other properties.
__UpperCamelCase : Tuple = ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
for name in names:
if name in output:
del output[name]
return output | 151 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : List[Any] = [
'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'InformerForPrediction',
'InformerModel',
'InformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
lowercase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 151 | 1 |
a_ :Union[str, Any] = 8.3_14_45_98
def lowercase_ (A : float , A : float ):
if temperature < 0:
raise Exception('Temperature cannot be less than 0 K' )
if molar_mass <= 0:
raise Exception('Molar mass cannot be less than or equal to 0 kg/mol' )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
a_ :Optional[int] = 300
a_ :List[Any] = 28
a_ :Union[str, Any] = rms_speed_of_molecule(temperature, molar_mass)
print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")
| 277 |
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 PoolFormerImageProcessor
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[int], _snake_case : List[Any], _snake_case : str=7, _snake_case : Tuple=3, _snake_case : List[str]=3_0, _snake_case : Tuple=4_0_0, _snake_case : Any=True, _snake_case : List[Any]=None, _snake_case : int=0.9, _snake_case : Optional[Any]=None, _snake_case : str=True, _snake_case : Union[str, Any]=[0.5, 0.5, 0.5], _snake_case : Union[str, Any]=[0.5, 0.5, 0.5], ) ->List[Any]:
snake_case__ : int = size if size is not None else {'shortest_edge': 3_0}
snake_case__ : Tuple = crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
snake_case__ : Union[str, Any] = parent
snake_case__ : Dict = batch_size
snake_case__ : int = num_channels
snake_case__ : Tuple = min_resolution
snake_case__ : Any = max_resolution
snake_case__ : List[Any] = do_resize_and_center_crop
snake_case__ : str = size
snake_case__ : str = crop_pct
snake_case__ : List[str] = crop_size
snake_case__ : Optional[int] = do_normalize
snake_case__ : Tuple = image_mean
snake_case__ : Tuple = image_std
def lowercase_ ( self : Optional[int] ) ->int:
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class snake_case__ ( lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = PoolFormerImageProcessor if is_vision_available() else None
def lowercase_ ( self : Union[str, Any] ) ->Dict:
snake_case__ : Union[str, Any] = PoolFormerImageProcessingTester(self )
@property
def lowercase_ ( self : int ) ->Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self : Union[str, Any] ) ->Optional[int]:
snake_case__ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_snake_case, 'do_resize_and_center_crop' ) )
self.assertTrue(hasattr(_snake_case, 'size' ) )
self.assertTrue(hasattr(_snake_case, 'crop_pct' ) )
self.assertTrue(hasattr(_snake_case, 'do_normalize' ) )
self.assertTrue(hasattr(_snake_case, 'image_mean' ) )
self.assertTrue(hasattr(_snake_case, 'image_std' ) )
def lowercase_ ( self : List[str] ) ->List[str]:
snake_case__ : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size, {'shortest_edge': 3_0} )
self.assertEqual(image_processor.crop_size, {'height': 3_0, 'width': 3_0} )
snake_case__ : int = self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4 )
self.assertEqual(image_processor.size, {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size, {'height': 8_4, 'width': 8_4} )
def lowercase_ ( self : List[Any] ) ->List[Any]:
pass
def lowercase_ ( self : List[str] ) ->str:
# Initialize image_processing
snake_case__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case__ : List[str] = prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case, Image.Image )
# Test not batched input
snake_case__ : Optional[int] = image_processing(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
# Test batched
snake_case__ : str = image_processing(_snake_case, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
def lowercase_ ( self : int ) ->List[Any]:
# Initialize image_processing
snake_case__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case__ : Dict = prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, numpify=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case, np.ndarray )
# Test not batched input
snake_case__ : Dict = image_processing(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
# Test batched
snake_case__ : List[Any] = image_processing(_snake_case, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
def lowercase_ ( self : List[str] ) ->List[str]:
# Initialize image_processing
snake_case__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case__ : List[str] = prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, torchify=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case, torch.Tensor )
# Test not batched input
snake_case__ : Tuple = image_processing(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
# Test batched
snake_case__ : Optional[Any] = image_processing(_snake_case, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
| 277 | 1 |
from __future__ import annotations
from typing import Any
class __A :
def __init__( self : Optional[Any] , UpperCAmelCase_ : int ):
lowerCAmelCase : Tuple = num_of_nodes
lowerCAmelCase : list[list[int]] = []
lowerCAmelCase : dict[int, int] = {}
def lowercase__ ( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
self.m_edges.append([u_node, v_node, weight] )
def lowercase__ ( self : Dict , UpperCAmelCase_ : int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def lowercase__ ( self : Optional[int] , UpperCAmelCase_ : int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
lowerCAmelCase : Dict = self.find_component(UpperCAmelCase_ )
def lowercase__ ( self : List[str] , UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if component_size[u_node] <= component_size[v_node]:
lowerCAmelCase : Optional[int] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(UpperCAmelCase_ )
elif component_size[u_node] >= component_size[v_node]:
lowerCAmelCase : Union[str, Any] = self.find_component(UpperCAmelCase_ )
component_size[u_node] += component_size[v_node]
self.set_component(UpperCAmelCase_ )
def lowercase__ ( self : str ):
lowerCAmelCase : str = []
lowerCAmelCase : Tuple = 0
lowerCAmelCase : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
lowerCAmelCase : int = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Union[str, Any] = edge
lowerCAmelCase : Optional[int] = self.m_component[u]
lowerCAmelCase : str = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
lowerCAmelCase : str = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Any = edge
lowerCAmelCase : Optional[Any] = self.m_component[u]
lowerCAmelCase : Optional[Any] = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n" )
num_of_components -= 1
lowerCAmelCase : Optional[Any] = [-1] * self.m_num_of_nodes
print(f"The total weight of the minimal spanning tree is: {mst_weight}" )
def SCREAMING_SNAKE_CASE__ ( ) -> None:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 323 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
return x + 2
class __A ( unittest.TestCase ):
def lowercase__ ( self : int ):
lowerCAmelCase : List[str] = 'x = 3'
lowerCAmelCase : Optional[Any] = {}
lowerCAmelCase : Tuple = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
assert result == 3
self.assertDictEqual(UpperCAmelCase_ , {'x': 3} )
lowerCAmelCase : Dict = 'x = y'
lowerCAmelCase : List[Any] = {'y': 5}
lowerCAmelCase : Tuple = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase_ , {'x': 5, 'y': 5} )
def lowercase__ ( self : Optional[Any] ):
lowerCAmelCase : Any = 'y = add_two(x)'
lowerCAmelCase : int = {'x': 3}
lowerCAmelCase : Optional[int] = evaluate(UpperCAmelCase_ , {'add_two': add_two} , state=UpperCAmelCase_ )
assert result == 5
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'y': 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowerCAmelCase : Tuple = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
assert result is None
assert "tried to execute add_two" in out.out
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Tuple = 'x = 3'
lowerCAmelCase : List[Any] = {}
lowerCAmelCase : Dict = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
assert result == 3
self.assertDictEqual(UpperCAmelCase_ , {'x': 3} )
def lowercase__ ( self : Optional[Any] ):
lowerCAmelCase : List[Any] = 'test_dict = {\'x\': x, \'y\': add_two(x)}'
lowerCAmelCase : Dict = {'x': 3}
lowerCAmelCase : Tuple = evaluate(UpperCAmelCase_ , {'add_two': add_two} , state=UpperCAmelCase_ )
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'y': 5} )
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'test_dict': {'x': 3, 'y': 5}} )
def lowercase__ ( self : Any ):
lowerCAmelCase : Union[str, Any] = 'x = 3\ny = 5'
lowerCAmelCase : str = {}
lowerCAmelCase : Optional[int] = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'y': 5} )
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Union[str, Any] = 'text = f\'This is x: {x}.\''
lowerCAmelCase : str = {'x': 3}
lowerCAmelCase : int = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'text': 'This is x: 3.'} )
def lowercase__ ( self : Dict ):
lowerCAmelCase : Optional[Any] = 'if x <= 3:\n y = 2\nelse:\n y = 5'
lowerCAmelCase : Dict = {'x': 3}
lowerCAmelCase : int = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'y': 2} )
lowerCAmelCase : Any = {'x': 8}
lowerCAmelCase : Optional[int] = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase_ , {'x': 8, 'y': 5} )
def lowercase__ ( self : List[Any] ):
lowerCAmelCase : int = 'test_list = [x, add_two(x)]'
lowerCAmelCase : Optional[Any] = {'x': 3}
lowerCAmelCase : Tuple = evaluate(UpperCAmelCase_ , {'add_two': add_two} , state=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , [3, 5] )
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'test_list': [3, 5]} )
def lowercase__ ( self : Optional[Any] ):
lowerCAmelCase : int = 'y = x'
lowerCAmelCase : Optional[int] = {'x': 3}
lowerCAmelCase : Tuple = evaluate(UpperCAmelCase_ , {} , state=UpperCAmelCase_ )
assert result == 3
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'y': 3} )
def lowercase__ ( self : List[str] ):
lowerCAmelCase : Dict = 'test_list = [x, add_two(x)]\ntest_list[1]'
lowerCAmelCase : List[str] = {'x': 3}
lowerCAmelCase : List[str] = evaluate(UpperCAmelCase_ , {'add_two': add_two} , state=UpperCAmelCase_ )
assert result == 5
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'test_list': [3, 5]} )
lowerCAmelCase : Optional[Any] = 'test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'
lowerCAmelCase : List[Any] = {'x': 3}
lowerCAmelCase : Optional[Any] = evaluate(UpperCAmelCase_ , {'add_two': add_two} , state=UpperCAmelCase_ )
assert result == 5
self.assertDictEqual(UpperCAmelCase_ , {'x': 3, 'test_dict': {'x': 3, 'y': 5}} )
def lowercase__ ( self : int ):
lowerCAmelCase : Any = 'x = 0\nfor i in range(3):\n x = i'
lowerCAmelCase : str = {}
lowerCAmelCase : Dict = evaluate(UpperCAmelCase_ , {'range': range} , state=UpperCAmelCase_ )
assert result == 2
self.assertDictEqual(UpperCAmelCase_ , {'x': 2, 'i': 2} )
| 323 | 1 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__lowercase = """sshleifer/bart-tiny-random"""
__lowercase = """patrickvonplaten/t5-tiny-random"""
@require_torch
class _A ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __snake_case ( self : Any):
return AutoConfig.from_pretrained(__UpperCAmelCase)
def __snake_case ( self : str):
a , *a : Optional[int] = create_student_by_copying_alternating_layers(__UpperCAmelCase , tempfile.mkdtemp() , e=1 , d=1)
self.assertEqual(student.config.num_hidden_layers , 1)
def __snake_case ( self : List[str]):
a , *a : Union[str, Any] = create_student_by_copying_alternating_layers(__UpperCAmelCase , tempfile.mkdtemp() , e=1 , d=__UpperCAmelCase)
def __snake_case ( self : Union[str, Any]):
a , *a : Optional[int] = create_student_by_copying_alternating_layers(__UpperCAmelCase , tempfile.mkdtemp() , e=1 , d=__UpperCAmelCase)
self.assertEqual(student.config.encoder_layers , 1)
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers)
def __snake_case ( self : List[str]):
a , *a : Union[str, Any] = create_student_by_copying_alternating_layers(__UpperCAmelCase , tempfile.mkdtemp() , e=1 , d=1)
self.assertEqual(student.config.encoder_layers , 1)
self.assertEqual(student.config.decoder_layers , 1)
def __snake_case ( self : List[Any]):
with self.assertRaises(__UpperCAmelCase):
create_student_by_copying_alternating_layers(__UpperCAmelCase , tempfile.mkdtemp() , e=__UpperCAmelCase , d=__UpperCAmelCase)
| 40 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class _A ( unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : List[Any]):
a : str = 0
a : Optional[int] = [0]
a : Union[str, Any] = [0]
a : Any = len(__UpperCAmelCase)
self.assertEqual(k.knapsack(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) , 0)
a : List[str] = [60]
a : str = [10]
a : Optional[int] = len(__UpperCAmelCase)
self.assertEqual(k.knapsack(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) , 0)
def __snake_case ( self : Optional[int]):
a : Any = 3
a : str = [1, 2, 3]
a : Tuple = [3, 2, 1]
a : Any = len(__UpperCAmelCase)
self.assertEqual(k.knapsack(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) , 5)
def __snake_case ( self : Tuple):
a : int = 50
a : List[Any] = [60, 100, 120]
a : Optional[int] = [10, 20, 30]
a : str = len(__UpperCAmelCase)
self.assertEqual(k.knapsack(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) , 220)
if __name__ == "__main__":
unittest.main()
| 40 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__lowerCamelCase : Optional[int] = """\
"""
__lowerCamelCase : Union[str, Any] = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
__lowerCamelCase : Any = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
"""simple docstring"""
def _lowercase ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"input_texts": datasets.Value("string" ),
} ) , reference_urls=["https://huggingface.co/docs/transformers/perplexity"] , )
def _lowercase ( self : Dict , __A : Dict , __A : List[str] , __A : int = 1_6 , __A : bool = True , __A : Dict=None ):
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
snake_case__ : Tuple = "cuda"
else:
snake_case__ : Optional[Any] = "cuda" if torch.cuda.is_available() else "cpu"
snake_case__ : List[Any] = AutoModelForCausalLM.from_pretrained(__A )
snake_case__ : List[str] = model.to(__A )
snake_case__ : Optional[int] = AutoTokenizer.from_pretrained(__A )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
snake_case__ : Tuple = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__A ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
snake_case__ : Tuple = model.config.max_length - 1
else:
snake_case__ : Any = model.config.max_length
snake_case__ : Optional[int] = tokenizer(
__A , add_special_tokens=__A , padding=__A , truncation=__A , max_length=__A , return_tensors="pt" , return_attention_mask=__A , ).to(__A )
snake_case__ : Union[str, Any] = encodings["input_ids"]
snake_case__ : List[str] = encodings["attention_mask"]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
snake_case__ : Any = []
snake_case__ : Optional[Any] = CrossEntropyLoss(reduction="none" )
for start_index in logging.tqdm(range(0 , len(__A ) , __A ) ):
snake_case__ : Tuple = min(start_index + batch_size , len(__A ) )
snake_case__ : List[Any] = encoded_texts[start_index:end_index]
snake_case__ : str = attn_masks[start_index:end_index]
if add_start_token:
snake_case__ : str = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__A )
snake_case__ : int = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
snake_case__ : Dict = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__A ), attn_mask] , dim=1 )
snake_case__ : str = encoded_batch
with torch.no_grad():
snake_case__ : Optional[int] = model(__A , attention_mask=__A ).logits
snake_case__ : Optional[Any] = out_logits[..., :-1, :].contiguous()
snake_case__ : List[Any] = labels[..., 1:].contiguous()
snake_case__ : Any = attn_mask[..., 1:].contiguous()
snake_case__ : Dict = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __A ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__A )}
| 286 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( snake_case_ : Tuple , snake_case_ : str , snake_case_ : Union[str, Any] ):
# Initialise PyTorch model
snake_case__ : List[str] = MobileBertConfig.from_json_file(snake_case_ )
print(F'''Building PyTorch model from configuration: {config}''' )
snake_case__ : Dict = MobileBertForPreTraining(snake_case_ )
# Load weights from tf checkpoint
snake_case__ : Any = load_tf_weights_in_mobilebert(snake_case_ , snake_case_ , snake_case_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , snake_case_ )
if __name__ == "__main__":
__lowerCamelCase : List[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(
"""--mobilebert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained MobileBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__lowerCamelCase : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 286 | 1 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =len(lowerCAmelCase_ ), len(grid[0] )
if (
min(lowerCAmelCase_, lowerCAmelCase_ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
SCREAMING_SNAKE_CASE =0
count += depth_first_search(lowerCAmelCase_, row + 1, lowerCAmelCase_, lowerCAmelCase_ )
count += depth_first_search(lowerCAmelCase_, row - 1, lowerCAmelCase_, lowerCAmelCase_ )
count += depth_first_search(lowerCAmelCase_, lowerCAmelCase_, col + 1, lowerCAmelCase_ )
count += depth_first_search(lowerCAmelCase_, lowerCAmelCase_, col - 1, lowerCAmelCase_ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 334 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Dict ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : List[str] ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Optional[Any] ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 1
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = True
def __call__( self : str ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ) and self.raise_if_mismatch:
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
class a_ ( nn.Module ):
"""simple docstring"""
def __init__( self : Any ,snake_case : nn.Module ):
super().__init__()
SCREAMING_SNAKE_CASE =[]
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), f'Unexpected layer name {k}'
SCREAMING_SNAKE_CASE =len(snake_case ) + 1
feature_blocks.append((f'res{block_index}', v) )
SCREAMING_SNAKE_CASE =nn.ModuleDict(snake_case )
def _lowerCAmelCase ( self : Dict ,snake_case : Tensor ):
return get_trunk_forward_outputs(
snake_case ,out_feat_keys=snake_case ,feature_blocks=self._feature_blocks ,)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str ):
SCREAMING_SNAKE_CASE =x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Optional[Any] ,snake_case : str ):
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE =self.convert_name_to_timm(snake_case )
SCREAMING_SNAKE_CASE =partial(lambda: (timm.create_model(snake_case ,pretrained=snake_case ).eval(), None) )
else:
SCREAMING_SNAKE_CASE =super().__getitem__(snake_case )
return val
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __getitem__( self : int ,snake_case : str ):
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE =RegNetModel
else:
SCREAMING_SNAKE_CASE =RegNetForImageClassification
return val
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE =from_state_dict[from_key].clone()
print(F'Copied key={from_key} to={to_key}' )
return to_state_dict
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True, ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =from_model_func()
SCREAMING_SNAKE_CASE =our_model_func(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_, raise_if_mismatch=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE =[]
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE =manually_copy_vissl_head(lowerCAmelCase_, our_model.state_dict(), lowerCAmelCase_ )
our_model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =our_model(lowerCAmelCase_, output_hidden_states=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =(
our_outputs.logits if isinstance(lowerCAmelCase_, lowerCAmelCase_ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE =from_model(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =from_output[-1] if type(lowerCAmelCase_ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =our_outputs.hidden_states[-1]
assert torch.allclose(lowerCAmelCase_, lowerCAmelCase_ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =224 if 'seer' not in name else 384
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k', size=lowerCAmelCase_ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(cached_download(hf_hub_url(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ) ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1008], groups_width=48, layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1360], groups_width=40, layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1624], groups_width=56, layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1920], groups_width=120, layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112, layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2048], groups_width=128, layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1344, 2520], groups_width=168, layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1512], groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1088], groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1296], groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2016], groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1232, 3024], groups_width=112 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
}
SCREAMING_SNAKE_CASE =NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE =NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowerCAmelCase_, lowerCAmelCase_ ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(lowerCAmelCase_, model_dir=str(lowerCAmelCase_ ), map_location='cpu' )
SCREAMING_SNAKE_CASE =model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE =files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE =model_state_dict['trunk']
model.load_state_dict(lowerCAmelCase_ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
# IN1K finetuned
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
if model_name:
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_, )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
snake_case__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase_ (datasets.BuilderConfig ):
"""simple docstring"""
_lowerCAmelCase = 1_0_0_0_0
_lowerCAmelCase = None
_lowerCAmelCase = None
class UpperCamelCase_ (datasets.ArrowBasedBuilder ):
"""simple docstring"""
_lowerCAmelCase = ParquetConfig
def _a ( self : Any ):
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def _a ( self : str , _lowerCamelCase : Dict ):
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
A_ : List[Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCamelCase , (str, list, tuple) ):
A_ : Union[str, Any] = data_files
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
A_ : Optional[int] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
A_ : str = []
for split_name, files in data_files.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
A_ : Optional[int] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(_lowerCamelCase ):
with open(_lowerCamelCase , '''rb''' ) as f:
A_ : Dict = datasets.Features.from_arrow_schema(pq.read_schema(_lowerCamelCase ) )
break
splits.append(datasets.SplitGenerator(name=_lowerCamelCase , gen_kwargs={'''files''': files} ) )
return splits
def _a ( self : Tuple , _lowerCamelCase : pa.Table ):
"""simple docstring"""
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
A_ : Optional[Any] = table_cast(_lowerCamelCase , self.info.features.arrow_schema )
return pa_table
def _a ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : List[str] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'' )
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase ) ):
with open(_lowerCamelCase , '''rb''' ) as f:
A_ : List[str] = pq.ParquetFile(_lowerCamelCase )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
A_ : Any = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f'{file_idx}_{batch_idx}', self._cast_table(_lowerCamelCase )
except ValueError as e:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise
| 368 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Union[str, Any] = val
A_ : Tuple = None
A_ : Any = None
def _a ( self : Tuple , _lowerCamelCase : List[Any] ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
A_ : int = Node(_lowerCamelCase )
else:
self.left.insert(_lowerCamelCase )
elif val > self.val:
if self.right is None:
A_ : List[str] = Node(_lowerCamelCase )
else:
self.right.insert(_lowerCamelCase )
else:
A_ : Any = val
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ) -> str:
# Recursive traversal
if root:
inorder(root.left , lowerCamelCase__ )
res.append(root.val )
inorder(root.right , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> Tuple:
# Build BST
if len(lowerCamelCase__ ) == 0:
return arr
A_ : Dict = Node(arr[0] )
for i in range(1 , len(lowerCamelCase__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
A_ : Tuple = []
inorder(lowerCamelCase__ , lowerCamelCase__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 4 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def __magic_name__( lowerCamelCase, lowerCamelCase=1_0):
__lowerCAmelCase = []
for _ in range(lowerCamelCase):
lrs.append(scheduler.get_lr()[0])
scheduler.step()
return lrs
def __magic_name__( lowerCamelCase, lowerCamelCase=1_0):
__lowerCAmelCase = []
for step in range(lowerCamelCase):
lrs.append(scheduler.get_lr()[0])
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase = os.path.join(lowerCamelCase, '''schedule.bin''')
torch.save(scheduler.state_dict(), lowerCamelCase)
__lowerCAmelCase = torch.load(lowerCamelCase)
scheduler.load_state_dict(lowerCamelCase)
return lrs
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
def _snake_case (self , __lowercase , __lowercase , __lowercase ):
self.assertEqual(len(__lowercase ) , len(__lowercase ) )
for a, b in zip(__lowercase , __lowercase ):
self.assertAlmostEqual(__lowercase , __lowercase , delta=__lowercase )
def _snake_case (self ):
__lowerCAmelCase = torch.tensor([0.1, -0.2, -0.1] , requires_grad=__lowercase )
__lowerCAmelCase = torch.tensor([0.4, 0.2, -0.5] )
__lowerCAmelCase = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
__lowerCAmelCase = AdamW(params=[w] , lr=2e-1 , weight_decay=0.0 )
for _ in range(1_00 ):
__lowerCAmelCase = criterion(__lowercase , __lowercase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
def _snake_case (self ):
__lowerCAmelCase = torch.tensor([0.1, -0.2, -0.1] , requires_grad=__lowercase )
__lowerCAmelCase = torch.tensor([0.4, 0.2, -0.5] )
__lowerCAmelCase = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
__lowerCAmelCase = Adafactor(
params=[w] , lr=1e-2 , eps=(1e-30, 1e-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=__lowercase , weight_decay=0.0 , relative_step=__lowercase , scale_parameter=__lowercase , warmup_init=__lowercase , )
for _ in range(10_00 ):
__lowerCAmelCase = criterion(__lowercase , __lowercase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Tuple = nn.Linear(50 , 50 ) if is_torch_available() else None
__UpperCamelCase : Union[str, Any] = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
__UpperCamelCase : int = 10
def _snake_case (self , __lowercase , __lowercase , __lowercase , __lowercase=None ):
self.assertEqual(len(__lowercase ) , len(__lowercase ) )
for a, b in zip(__lowercase , __lowercase ):
self.assertAlmostEqual(__lowercase , __lowercase , delta=__lowercase , msg=__lowercase )
def _snake_case (self ):
__lowerCAmelCase = {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
__lowerCAmelCase = {
get_constant_schedule: ({}, [1_0.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'''num_warmup_steps''': 4},
[0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, '''num_cycles''': 2},
[0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
[0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
),
get_inverse_sqrt_schedule: (
{'''num_warmup_steps''': 2},
[0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
),
}
for scheduler_func, data in scheds.items():
__lowerCAmelCase , __lowerCAmelCase = data
__lowerCAmelCase = scheduler_func(self.optimizer , **__lowercase )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
__lowerCAmelCase = unwrap_schedule(__lowercase , self.num_steps )
self.assertListAlmostEqual(
__lowercase , __lowercase , tol=1e-2 , msg=F"""failed for {scheduler_func} in normal scheduler""" , )
__lowerCAmelCase = scheduler_func(self.optimizer , **__lowercase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(__lowercase ) # wrap to test picklability of the schedule
__lowerCAmelCase = unwrap_and_save_reload_schedule(__lowercase , self.num_steps )
self.assertListEqual(__lowercase , __lowercase , msg=F"""failed for {scheduler_func} in save and reload""" )
class a__ :
"""simple docstring"""
def __init__(self , __lowercase ):
__lowerCAmelCase = fn
def __call__(self , *__lowercase , **__lowercase ):
return self.fn(*__lowercase , **__lowercase )
@classmethod
def _snake_case (self , __lowercase ):
__lowerCAmelCase = list(map(self , scheduler.lr_lambdas ) )
| 174 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = """▁"""
_UpperCAmelCase : str = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}
_UpperCAmelCase : Dict = {
"""vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
},
"""monolingual_vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
},
}
_UpperCAmelCase : List[Any] = {"""vinai/bartpho-syllable""": 1_0_2_4}
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__UpperCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : List[Any] = ['input_ids', 'attention_mask']
def __init__(self , __lowercase , __lowercase , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , __lowercase = None , **__lowercase , ):
# Mask token behave like a normal word, i.e. include the space before it
__lowerCAmelCase = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
__lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , sp_model_kwargs=self.sp_model_kwargs , **__lowercase , )
__lowerCAmelCase = vocab_file
__lowerCAmelCase = monolingual_vocab_file
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowercase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
__lowerCAmelCase = {}
__lowerCAmelCase = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__lowercase ) not in self.fairseq_tokens_to_ids:
__lowerCAmelCase = cnt
cnt += 1
with open(__lowercase , '''r''' , encoding='''utf-8''' ) as f:
for line in f.readlines():
__lowerCAmelCase = line.strip().split()[0]
__lowerCAmelCase = len(self.fairseq_tokens_to_ids )
if str(__lowercase ) not in self.fairseq_tokens_to_ids:
__lowerCAmelCase = len(self.fairseq_tokens_to_ids )
__lowerCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__(self ):
__lowerCAmelCase = self.__dict__.copy()
__lowerCAmelCase = None
__lowerCAmelCase = self.sp_model.serialized_model_proto()
return state
def __setstate__(self , __lowercase ):
__lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__lowerCAmelCase = {}
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _snake_case (self , __lowercase , __lowercase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowerCAmelCase = [self.cls_token_id]
__lowerCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case (self , __lowercase , __lowercase = None , __lowercase = 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 )) + [1]
return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]
def _snake_case (self , __lowercase , __lowercase = None ):
__lowerCAmelCase = [self.sep_token_id]
__lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _snake_case (self ):
return len(self.fairseq_ids_to_tokens )
def _snake_case (self ):
__lowerCAmelCase = {self.convert_ids_to_tokens(__lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _snake_case (self , __lowercase ):
return self.sp_model.encode(__lowercase , out_type=__lowercase )
def _snake_case (self , __lowercase ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def _snake_case (self , __lowercase ):
return self.fairseq_ids_to_tokens[index]
def _snake_case (self , __lowercase ):
__lowerCAmelCase = ''''''.join(__lowercase ).replace(__lowercase , ''' ''' ).strip()
return out_string
def _snake_case (self , __lowercase , __lowercase = None ):
if not os.path.isdir(__lowercase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCAmelCase = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__lowerCAmelCase = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_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:
__lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(__lowercase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__lowercase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __lowercase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__lowercase , '''w''' , encoding='''utf-8''' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(F"""{str(__lowercase )} \n""" )
return out_vocab_file, out_monolingual_vocab_file
| 174 | 1 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( lowerCamelCase_ : Union[str, Any] ,lowerCamelCase_ : List[Any] ,lowerCamelCase_ : Dict ,lowerCamelCase_ : Optional[Any]):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = multiprocessing.Manager()
lowerCAmelCase__ : Dict = manager.list()
lowerCAmelCase__ : Optional[Any] = multiprocessing.Process(target=lowerCamelCase_ ,args=(check_program, result, timeout))
p.start()
p.join(timeout=timeout + 1)
if p.is_alive():
p.kill()
if not result:
result.append('''timed out''')
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def lowerCAmelCase__ ( lowerCamelCase_ : Tuple ,lowerCamelCase_ : Union[str, Any] ,lowerCamelCase_ : Any):
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
lowerCAmelCase__ : List[Any] = shutil.rmtree
lowerCAmelCase__ : Dict = os.rmdir
lowerCAmelCase__ : Optional[Any] = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
lowerCAmelCase__ : Any = {}
with swallow_io():
with time_limit(lowerCamelCase_):
exec(lowerCamelCase_ ,lowerCamelCase_)
result.append('''passed''')
except TimeoutException:
result.append('''timed out''')
except BaseException as e:
result.append(f"""failed: {e}""")
# Needed for cleaning up.
lowerCAmelCase__ : List[Any] = rmtree
lowerCAmelCase__ : List[Any] = rmdir
lowerCAmelCase__ : Optional[int] = chdir
@contextlib.contextmanager
def lowerCAmelCase__ ( lowerCamelCase_ : List[str]):
'''simple docstring'''
def signal_handler(lowerCamelCase_ : Union[str, Any] ,lowerCamelCase_ : Dict):
raise TimeoutException('''Timed out!''')
signal.setitimer(signal.ITIMER_REAL ,lowerCamelCase_)
signal.signal(signal.SIGALRM ,lowerCamelCase_)
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL ,0)
@contextlib.contextmanager
def lowerCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase__ : int = WriteOnlyStringIO()
with contextlib.redirect_stdout(lowerCamelCase_):
with contextlib.redirect_stderr(lowerCamelCase_):
with redirect_stdin(lowerCamelCase_):
yield
@contextlib.contextmanager
def lowerCAmelCase__ ( ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(lowerCamelCase_):
yield dirname
class lowerCamelCase__ ( _a):
'''simple docstring'''
pass
class lowerCamelCase__ ( io.StringIO):
'''simple docstring'''
def lowerCAmelCase__ (self ,*__lowerCamelCase ,**__lowerCamelCase ) -> Tuple:
"""simple docstring"""
raise OSError
def lowerCAmelCase__ (self ,*__lowerCamelCase ,**__lowerCamelCase ) -> str:
"""simple docstring"""
raise OSError
def lowerCAmelCase__ (self ,*__lowerCamelCase ,**__lowerCamelCase ) -> str:
"""simple docstring"""
raise OSError
def lowerCAmelCase__ (self ,*__lowerCamelCase ,**__lowerCamelCase ) -> Optional[int]:
"""simple docstring"""
return False
class lowerCamelCase__ ( contextlib._RedirectStream): # type: ignore
'''simple docstring'''
snake_case_ ="""stdin"""
@contextlib.contextmanager
def lowerCAmelCase__ ( lowerCamelCase_ : Union[str, Any]):
'''simple docstring'''
if root == ".":
yield
return
lowerCAmelCase__ : List[str] = os.getcwd()
os.chdir(lowerCamelCase_)
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(lowerCamelCase_)
def lowerCAmelCase__ ( lowerCamelCase_ : Optional[int]=None):
'''simple docstring'''
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS ,(maximum_memory_bytes, maximum_memory_bytes))
resource.setrlimit(resource.RLIMIT_DATA ,(maximum_memory_bytes, maximum_memory_bytes))
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK ,(maximum_memory_bytes, maximum_memory_bytes))
faulthandler.disable()
import builtins
lowerCAmelCase__ : int = None
lowerCAmelCase__ : Optional[Any] = None
import os
lowerCAmelCase__ : Optional[int] = '1'
lowerCAmelCase__ : Tuple = None
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : Optional[int] = None
lowerCAmelCase__ : str = None
lowerCAmelCase__ : List[str] = None
lowerCAmelCase__ : Tuple = None
lowerCAmelCase__ : Optional[Any] = None
lowerCAmelCase__ : Any = None
lowerCAmelCase__ : Optional[int] = None
lowerCAmelCase__ : Optional[Any] = None
lowerCAmelCase__ : str = None
lowerCAmelCase__ : str = None
lowerCAmelCase__ : str = None
lowerCAmelCase__ : Optional[int] = None
lowerCAmelCase__ : int = None
lowerCAmelCase__ : Any = None
lowerCAmelCase__ : str = None
lowerCAmelCase__ : Dict = None
lowerCAmelCase__ : Dict = None
lowerCAmelCase__ : Tuple = None
lowerCAmelCase__ : List[Any] = None
lowerCAmelCase__ : int = None
lowerCAmelCase__ : Optional[int] = None
lowerCAmelCase__ : str = None
lowerCAmelCase__ : Optional[Any] = None
lowerCAmelCase__ : Tuple = None
lowerCAmelCase__ : Any = None
import shutil
lowerCAmelCase__ : int = None
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : Any = None
import subprocess
lowerCAmelCase__ : Any = None # type: ignore
lowerCAmelCase__ : str = None
import sys
lowerCAmelCase__ : Dict = None
lowerCAmelCase__ : Tuple = None
lowerCAmelCase__ : List[Any] = None
lowerCAmelCase__ : Optional[Any] = None
lowerCAmelCase__ : Optional[Any] = None
| 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Any ={
'configuration_blenderbot_small': [
'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotSmallConfig',
'BlenderbotSmallOnnxConfig',
],
'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Union[str, Any] =['BlenderbotSmallTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[str] =[
'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotSmallForCausalLM',
'BlenderbotSmallForConditionalGeneration',
'BlenderbotSmallModel',
'BlenderbotSmallPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str =[
'TFBlenderbotSmallForConditionalGeneration',
'TFBlenderbotSmallModel',
'TFBlenderbotSmallPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Dict =[
'FlaxBlenderbotSmallForConditionalGeneration',
'FlaxBlenderbotSmallModel',
'FlaxBlenderbotSmallPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
__snake_case : Optional[int] =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 94 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( _lowerCamelCase ):
'''simple docstring'''
snake_case_ : Optional[int] = (UnCLIPScheduler,)
def UpperCamelCase_ ( self : List[Any] , **lowerCAmelCase : Dict) -> Any:
"""simple docstring"""
_snake_case : Dict = {
'num_train_timesteps': 1000,
'variance_type': 'fixed_small_log',
'clip_sample': True,
'clip_sample_range': 1.0,
'prediction_type': 'epsilon',
}
config.update(**_SCREAMING_SNAKE_CASE)
return config
def UpperCamelCase_ ( self : str) -> Tuple:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self : Optional[int]) -> Optional[int]:
"""simple docstring"""
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self : Union[str, Any]) -> int:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=_SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self : Dict) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self : Optional[int]) -> List[str]:
"""simple docstring"""
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE , prev_timestep=_SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self : Optional[Any]) -> List[str]:
"""simple docstring"""
_snake_case : Optional[Any] = self.scheduler_classes[0]
_snake_case : Union[str, Any] = self.get_scheduler_config(variance_type="""fixed_small_log""")
_snake_case : Tuple = scheduler_class(**_SCREAMING_SNAKE_CASE)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000E-10)) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_549_625)) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9_994_987)) < 1E-5
def UpperCamelCase_ ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
_snake_case : Union[str, Any] = self.scheduler_classes[0]
_snake_case : Dict = self.get_scheduler_config(variance_type="""learned_range""")
_snake_case : Union[str, Any] = scheduler_class(**_SCREAMING_SNAKE_CASE)
_snake_case : str = 0.5
assert scheduler._get_variance(1 , predicted_variance=_SCREAMING_SNAKE_CASE) - -10.1_712_790 < 1E-5
assert scheduler._get_variance(487 , predicted_variance=_SCREAMING_SNAKE_CASE) - -5.7_998_052 < 1E-5
assert scheduler._get_variance(999 , predicted_variance=_SCREAMING_SNAKE_CASE) - -0.0_010_011 < 1E-5
def UpperCamelCase_ ( self : Union[str, Any]) -> Optional[Any]:
"""simple docstring"""
_snake_case : Optional[int] = self.scheduler_classes[0]
_snake_case : Optional[int] = self.get_scheduler_config()
_snake_case : Union[str, Any] = scheduler_class(**_SCREAMING_SNAKE_CASE)
_snake_case : Tuple = scheduler.timesteps
_snake_case : Optional[int] = self.dummy_model()
_snake_case : int = self.dummy_sample_deter
_snake_case : str = torch.manual_seed(0)
for i, t in enumerate(_SCREAMING_SNAKE_CASE):
# 1. predict noise residual
_snake_case : Dict = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
# 2. predict previous mean of sample x_t-1
_snake_case : int = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE).prev_sample
_snake_case : str = pred_prev_sample
_snake_case : List[str] = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
_snake_case : List[Any] = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))
assert abs(result_sum.item() - 252.2_682_495) < 1E-2
assert abs(result_mean.item() - 0.3_284_743) < 1E-3
def UpperCamelCase_ ( self : List[Any]) -> Dict:
"""simple docstring"""
_snake_case : Tuple = self.scheduler_classes[0]
_snake_case : Any = self.get_scheduler_config()
_snake_case : Tuple = scheduler_class(**_SCREAMING_SNAKE_CASE)
scheduler.set_timesteps(25)
_snake_case : int = scheduler.timesteps
_snake_case : List[Any] = self.dummy_model()
_snake_case : List[Any] = self.dummy_sample_deter
_snake_case : Any = torch.manual_seed(0)
for i, t in enumerate(_SCREAMING_SNAKE_CASE):
# 1. predict noise residual
_snake_case : Optional[int] = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
if i + 1 == timesteps.shape[0]:
_snake_case : Any = None
else:
_snake_case : Optional[Any] = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
_snake_case : Union[str, Any] = scheduler.step(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , prev_timestep=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE).prev_sample
_snake_case : List[Any] = pred_prev_sample
_snake_case : str = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
_snake_case : Union[str, Any] = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))
assert abs(result_sum.item() - 258.2_044_983) < 1E-2
assert abs(result_mean.item() - 0.3_362_038) < 1E-3
def UpperCamelCase_ ( self : str) -> Optional[Any]:
"""simple docstring"""
pass
def UpperCamelCase_ ( self : Optional[Any]) -> Optional[int]:
"""simple docstring"""
pass
| 317 |
"""simple docstring"""
import math
import sys
def __lowerCAmelCase (_UpperCamelCase ):
if number != int(_UpperCamelCase ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the value of input must not be a negative number' )
if number == 0:
return 1
__lowerCAmelCase : Any = [-1] * (number + 1)
__lowerCAmelCase : List[Any] = 0
for i in range(1 , number + 1 ):
__lowerCAmelCase : List[Any] = sys.maxsize
__lowerCAmelCase : Optional[int] = int(math.sqrt(_UpperCamelCase ) )
for j in range(1 , root + 1 ):
__lowerCAmelCase : Optional[Any] = 1 + answers[i - (j**2)]
__lowerCAmelCase : Any = min(_UpperCamelCase , _UpperCamelCase )
__lowerCAmelCase : List[str] = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod() | 86 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
class _snake_case (__SCREAMING_SNAKE_CASE):
def __init__( self ,_snake_case ):
super().__init__()
UpperCAmelCase_ : Tuple = nn.ModuleList(_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = False ,_snake_case = True ,):
for i, (image, scale, controlnet) in enumerate(zip(_snake_case ,_snake_case ,self.nets ) ):
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = controlnet(
_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,)
# merge samples
if i == 0:
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = down_samples, mid_sample
else:
UpperCAmelCase_ : List[Any] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(_snake_case ,_snake_case )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def UpperCamelCase__ ( self ,_snake_case ,_snake_case = True ,_snake_case = None ,_snake_case = False ,_snake_case = None ,):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : Optional[int] = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
_snake_case ,is_main_process=_snake_case ,save_function=_snake_case ,safe_serialization=_snake_case ,variant=_snake_case ,)
idx += 1
UpperCAmelCase_ : Union[str, Any] = model_path_to_save + f'''_{idx}'''
@classmethod
def UpperCamelCase__ ( cls ,_snake_case ,**_snake_case ):
UpperCAmelCase_ : List[str] = 0
UpperCAmelCase_ : Dict = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
UpperCAmelCase_ : int = pretrained_model_path
while os.path.isdir(_snake_case ):
UpperCAmelCase_ : Union[str, Any] = ControlNetModel.from_pretrained(_snake_case ,**_snake_case )
controlnets.append(_snake_case )
idx += 1
UpperCAmelCase_ : Union[str, Any] = pretrained_model_path + f'''_{idx}'''
logger.info(f'''{len(_snake_case )} controlnets loaded from {pretrained_model_path}.''' )
if len(_snake_case ) == 0:
raise ValueError(
f'''No ControlNets found under {os.path.dirname(_snake_case )}. Expected at least {pretrained_model_path + "_0"}.''' )
return cls(_snake_case )
| 67 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase = logging.get_logger(__name__)
class _snake_case (__SCREAMING_SNAKE_CASE):
def __init__( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
UpperCAmelCase_ : List[Any] = feature_size
UpperCAmelCase_ : Any = sampling_rate
UpperCAmelCase_ : Any = padding_value
UpperCAmelCase_ : Any = kwargs.pop("padding_side" ,"right" )
UpperCAmelCase_ : int = kwargs.pop("return_attention_mask" ,_snake_case )
super().__init__(**_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case = True ,_snake_case = None ,_snake_case = False ,_snake_case = None ,_snake_case = None ,_snake_case = None ,):
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(_snake_case ,(list, tuple) ) and isinstance(processed_features[0] ,(dict, BatchFeature) ):
UpperCAmelCase_ : Dict = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
f''' to this method that includes {self.model_input_names[0]}, but you provided'''
f''' {list(processed_features.keys() )}''' )
UpperCAmelCase_ : Optional[int] = processed_features[self.model_input_names[0]]
UpperCAmelCase_ : Dict = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(_snake_case ) == 0:
if return_attention_mask:
UpperCAmelCase_ : List[str] = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
UpperCAmelCase_ : Tuple = required_input[0]
if isinstance(_snake_case ,(list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
UpperCAmelCase_ : int = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(_snake_case ):
UpperCAmelCase_ : str = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(_snake_case ):
UpperCAmelCase_ : Any = "tf"
elif is_torch_tensor(_snake_case ):
UpperCAmelCase_ : Optional[int] = "pt"
elif isinstance(_snake_case ,(int, float, list, tuple, np.ndarray) ):
UpperCAmelCase_ : Any = "np"
else:
raise ValueError(
f'''type of {first_element} unknown: {type(_snake_case )}. '''
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] ,(int, float) ):
UpperCAmelCase_ : Optional[Any] = to_numpy(_snake_case )
else:
UpperCAmelCase_ : Any = [to_numpy(_snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
UpperCAmelCase_ : List[Any] = self._get_padding_strategies(padding=_snake_case ,max_length=_snake_case )
UpperCAmelCase_ : Dict = processed_features[self.model_input_names[0]]
UpperCAmelCase_ : str = len(_snake_case )
if not all(len(_snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
UpperCAmelCase_ : Dict = []
for i in range(_snake_case ):
UpperCAmelCase_ : List[Any] = {k: v[i] for k, v in processed_features.items()}
# truncation
UpperCAmelCase_ : Dict = self._truncate(
_snake_case ,max_length=_snake_case ,pad_to_multiple_of=_snake_case ,truncation=_snake_case ,)
truncated_inputs.append(_snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
UpperCAmelCase_ : List[Any] = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
UpperCAmelCase_ : str = PaddingStrategy.MAX_LENGTH
UpperCAmelCase_ : Dict = {}
for i in range(_snake_case ):
# padding
UpperCAmelCase_ : Dict = self._pad(
truncated_inputs[i] ,max_length=_snake_case ,padding_strategy=_snake_case ,pad_to_multiple_of=_snake_case ,return_attention_mask=_snake_case ,)
for key, value in outputs.items():
if key not in batch_outputs:
UpperCAmelCase_ : Optional[Any] = []
if value.dtype is np.dtype(np.floataa ):
UpperCAmelCase_ : str = value.astype(np.floataa )
batch_outputs[key].append(_snake_case )
return BatchFeature(_snake_case ,tensor_type=_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case = None ,_snake_case = PaddingStrategy.DO_NOT_PAD ,_snake_case = None ,_snake_case = None ,):
UpperCAmelCase_ : Any = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
UpperCAmelCase_ : Any = len(_snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
UpperCAmelCase_ : Optional[Any] = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
UpperCAmelCase_ : str = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
UpperCAmelCase_ : List[str] = np.ones(len(_snake_case ) ,dtype=np.intaa )
if needs_to_be_padded:
UpperCAmelCase_ : Union[str, Any] = max_length - len(_snake_case )
if self.padding_side == "right":
if return_attention_mask:
UpperCAmelCase_ : str = np.pad(
processed_features["attention_mask"] ,(0, difference) )
UpperCAmelCase_ : str = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
UpperCAmelCase_ : int = np.pad(
_snake_case ,_snake_case ,"constant" ,constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
UpperCAmelCase_ : List[Any] = np.pad(
processed_features["attention_mask"] ,(difference, 0) )
UpperCAmelCase_ : List[Any] = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
UpperCAmelCase_ : Union[str, Any] = np.pad(
_snake_case ,_snake_case ,"constant" ,constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def UpperCamelCase__ ( self ,_snake_case ,_snake_case = None ,_snake_case = None ,_snake_case = None ,):
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
UpperCAmelCase_ : List[Any] = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
UpperCAmelCase_ : int = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
UpperCAmelCase_ : Dict = len(_snake_case ) > max_length
if needs_to_be_truncated:
UpperCAmelCase_ : Any = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
UpperCAmelCase_ : str = processed_features["attention_mask"][:max_length]
return processed_features
def UpperCamelCase__ ( self ,_snake_case=False ,_snake_case=None ):
# Get padding strategy
if padding is not False:
if padding is True:
UpperCAmelCase_ : Optional[Any] = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(_snake_case ,_snake_case ):
UpperCAmelCase_ : str = PaddingStrategy(_snake_case )
elif isinstance(_snake_case ,_snake_case ):
UpperCAmelCase_ : List[Any] = padding
else:
UpperCAmelCase_ : List[str] = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 67 | 1 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[Any]=3 , UpperCamelCase__ : Any=32 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : Union[str, Any]=10 , UpperCamelCase__ : List[str]=[10, 20, 30, 40] , UpperCamelCase__ : Optional[int]=[1, 1, 2, 1] , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Dict=True , UpperCamelCase__ : str="relu" , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : Tuple=None , ) -> List[str]:
"""simple docstring"""
__magic_name__ = parent
__magic_name__ = batch_size
__magic_name__ = image_size
__magic_name__ = num_channels
__magic_name__ = embeddings_size
__magic_name__ = hidden_sizes
__magic_name__ = depths
__magic_name__ = is_training
__magic_name__ = use_labels
__magic_name__ = hidden_act
__magic_name__ = num_labels
__magic_name__ = scope
__magic_name__ = len(UpperCamelCase__ )
def _lowercase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__magic_name__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ = self.get_config()
return config, pixel_values
def _lowercase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Dict ) -> List[str]:
"""simple docstring"""
__magic_name__ = FlaxRegNetModel(config=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _lowercase ( self : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = self.num_labels
__magic_name__ = FlaxRegNetForImageClassification(config=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self : Tuple ) -> str:
"""simple docstring"""
__magic_name__ = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ = config_and_inputs
__magic_name__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
a__ = False
a__ = False
a__ = False
def _lowercase ( self : int ) -> None:
"""simple docstring"""
__magic_name__ = FlaxRegNetModelTester(self )
__magic_name__ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def _lowercase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _lowercase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return
def _lowercase ( self : List[str] ) -> Any:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowercase ( self : Any ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def _lowercase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def _lowercase ( self : Dict ) -> Dict:
"""simple docstring"""
pass
def _lowercase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ = model_class(UpperCamelCase__ )
__magic_name__ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ = [*signature.parameters.keys()]
__magic_name__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def _lowercase ( self : List[Any] ) -> Any:
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] ):
__magic_name__ = model_class(UpperCamelCase__ )
__magic_name__ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
__magic_name__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__magic_name__ = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase__ ) , expected_num_stages + 1 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__magic_name__ = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def _lowercase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__magic_name__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = model_class(UpperCamelCase__ )
@jax.jit
def model_jitted(UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : Any ):
return model(pixel_values=UpperCamelCase__ , **UpperCamelCase__ )
with self.subTest("""JIT Enabled""" ):
__magic_name__ = model_jitted(**UpperCamelCase__ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
__magic_name__ = model_jitted(**UpperCamelCase__ ).to_tuple()
self.assertEqual(len(UpperCamelCase__ ) , len(UpperCamelCase__ ) )
for jitted_output, output in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
def a__ ( ):
'''simple docstring'''
__magic_name__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_flax
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _lowercase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040""" ) if is_vision_available() else None
@slow
def _lowercase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__magic_name__ = FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040""" )
__magic_name__ = self.default_image_processor
__magic_name__ = prepare_img()
__magic_name__ = image_processor(images=UpperCamelCase__ , return_tensors="""np""" )
__magic_name__ = model(**UpperCamelCase__ )
# verify the logits
__magic_name__ = (1, 1000)
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
__magic_name__ = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1E-4 ) )
| 88 |
from functools import reduce
_SCREAMING_SNAKE_CASE : Any = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCAmelCase_ ( _A = N ):
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _A , _A : str(int(_A ) * int(_A ) ) , n[i : i + 13] ) )
for i in range(len(_A ) - 12 ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 314 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format='''%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s''',
datefmt='''%Y-%m-%d %H:%M:%S''',
level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
stream=sys.stdout,
)
lowerCAmelCase__ = logging.getLogger(__name__)
lowerCAmelCase__ = {'''facebook/bart-base''': BartForConditionalGeneration}
lowerCAmelCase__ = {'''facebook/bart-base''': BartTokenizer}
def a__ ( ):
"""simple docstring"""
UpperCamelCase = argparse.ArgumentParser(description="Export Bart model + Beam Search to ONNX graph." )
parser.add_argument(
"--validation_file" , type=__snake_case , default=__snake_case , help="A csv or a json file containing the validation data." )
parser.add_argument(
"--max_length" , type=__snake_case , default=5 , help="The maximum total input sequence length after tokenization." , )
parser.add_argument(
"--num_beams" , type=__snake_case , default=__snake_case , help=(
"Number of beams to use for evaluation. This argument will be "
"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."
) , )
parser.add_argument(
"--model_name_or_path" , type=__snake_case , help="Path to pretrained model or model identifier from huggingface.co/models." , required=__snake_case , )
parser.add_argument(
"--config_name" , type=__snake_case , default=__snake_case , help="Pretrained config name or path if not the same as model_name" , )
parser.add_argument(
"--device" , type=__snake_case , default="cpu" , help="Device where the model will be run" , )
parser.add_argument("--output_file_path" , type=__snake_case , default=__snake_case , help="Where to store the final ONNX file." )
UpperCamelCase = parser.parse_args()
return args
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="cpu" ):
"""simple docstring"""
UpperCamelCase = model_dict[model_name].from_pretrained(__snake_case ).to(__snake_case )
UpperCamelCase = tokenizer_dict[model_name].from_pretrained(__snake_case )
if model_name in ["facebook/bart-base"]:
UpperCamelCase = 0
UpperCamelCase = None
UpperCamelCase = 0
return huggingface_model, tokenizer
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
model.eval()
UpperCamelCase = None
UpperCamelCase = torch.jit.script(BARTBeamSearchGenerator(__snake_case ) )
with torch.no_grad():
UpperCamelCase = "My friends are cool but they eat too many carbs."
UpperCamelCase = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1_024 , return_tensors="pt" ).to(model.device )
UpperCamelCase = model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , num_beams=__snake_case , max_length=__snake_case , early_stopping=__snake_case , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
__snake_case , (
inputs["input_ids"],
inputs["attention_mask"],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , __snake_case , opset_version=14 , input_names=["input_ids", "attention_mask", "num_beams", "max_length", "decoder_start_token_id"] , output_names=["output_ids"] , dynamic_axes={
"input_ids": {0: "batch", 1: "seq"},
"output_ids": {0: "batch", 1: "seq_out"},
} , example_outputs=__snake_case , )
logger.info("Model exported to {}".format(__snake_case ) )
UpperCamelCase = remove_dup_initializers(os.path.abspath(__snake_case ) )
logger.info("Deduplicated and optimized model written to {}".format(__snake_case ) )
UpperCamelCase = onnxruntime.InferenceSession(__snake_case )
UpperCamelCase = ort_sess.run(
__snake_case , {
"input_ids": inputs["input_ids"].cpu().numpy(),
"attention_mask": inputs["attention_mask"].cpu().numpy(),
"num_beams": np.array(__snake_case ),
"max_length": np.array(__snake_case ),
"decoder_start_token_id": np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1e-3 , atol=1e-3 )
logger.info("Model outputs from torch and ONNX Runtime are similar." )
logger.info("Success." )
def a__ ( ):
"""simple docstring"""
UpperCamelCase = parse_args()
UpperCamelCase = 5
UpperCamelCase = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
UpperCamelCase = torch.device(args.device )
UpperCamelCase , UpperCamelCase = load_model_tokenizer(args.model_name_or_path , __snake_case )
if model.config.decoder_start_token_id is None:
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined" )
model.to(__snake_case )
if args.max_length:
UpperCamelCase = args.max_length
if args.num_beams:
UpperCamelCase = args.num_beams
if args.output_file_path:
UpperCamelCase = args.output_file_path
else:
UpperCamelCase = "BART.onnx"
logger.info("Exporting model to ONNX" )
export_and_validate_model(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
if __name__ == "__main__":
main()
| 362 |
"""simple docstring"""
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 3 ):
"""simple docstring"""
UpperCamelCase = min(_SCREAMING_SNAKE_CASE )
UpperCamelCase = max(_SCREAMING_SNAKE_CASE )
# normalize data
return [round((x - x_min) / (x_max - x_min) , _SCREAMING_SNAKE_CASE ) for x in data]
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 3 ):
"""simple docstring"""
UpperCamelCase = mean(_SCREAMING_SNAKE_CASE )
UpperCamelCase = stdev(_SCREAMING_SNAKE_CASE )
# standardize data
return [round((x - mu) / (sigma) , _SCREAMING_SNAKE_CASE ) for x in data]
| 244 | 0 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __SCREAMING_SNAKE_CASE ( A_ ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f) #
or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f) #
or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f) #
or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f) #
or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f) #
): #
return True
return False
def __SCREAMING_SNAKE_CASE ( A_ ):
# word like '180' or '身高' or '神'
for char in word:
lowerCAmelCase__ : Tuple = ord(A_ )
if not _is_chinese_char(A_ ):
return 0
return 1
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : str = set()
for token in tokens:
lowerCAmelCase__ : str = len(A_ ) > 1 and is_chinese(A_ )
if chinese_word:
word_set.add(A_ )
lowerCAmelCase__ : str = list(A_ )
return word_list
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
if not chinese_word_set:
return bert_tokens
lowerCAmelCase__ : int = max([len(A_ ) for w in chinese_word_set] )
lowerCAmelCase__ : Tuple = bert_tokens
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = 0, len(A_ )
while start < end:
lowerCAmelCase__ : int = True
if is_chinese(bert_word[start] ):
lowerCAmelCase__ : str = min(end - start , A_ )
for i in range(A_ , 1 , -1 ):
lowerCAmelCase__ : Dict = ''''''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowerCAmelCase__ : Any = '''##''' + bert_word[j]
lowerCAmelCase__ : str = start + i
lowerCAmelCase__ : List[Any] = False
break
if single_word:
start += 1
return bert_word
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ : Dict = []
for i in range(0 , len(A_ ) , 1_00 ):
lowerCAmelCase__ : List[str] = ltp_tokenizer.seg(lines[i : i + 1_00] )[0]
lowerCAmelCase__ : Union[str, Any] = [get_chinese_word(A_ ) for r in res]
ltp_res.extend(A_ )
assert len(A_ ) == len(A_ )
lowerCAmelCase__ : Union[str, Any] = []
for i in range(0 , len(A_ ) , 1_00 ):
lowerCAmelCase__ : Tuple = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=A_ , truncation=A_ , max_length=5_12 )
bert_res.extend(res['''input_ids'''] )
assert len(A_ ) == len(A_ )
lowerCAmelCase__ : List[str] = []
for input_ids, chinese_word in zip(A_ , A_ ):
lowerCAmelCase__ : List[Any] = []
for id in input_ids:
lowerCAmelCase__ : Optional[Any] = bert_tokenizer._convert_id_to_token(A_ )
input_tokens.append(A_ )
lowerCAmelCase__ : Any = add_sub_symbol(A_ , A_ )
lowerCAmelCase__ : Dict = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(A_ ):
if token[:2] == "##":
lowerCAmelCase__ : List[str] = token[2:]
# save chinese tokens' pos
if len(A_ ) == 1 and _is_chinese_char(ord(A_ ) ):
ref_id.append(A_ )
ref_ids.append(A_ )
assert len(A_ ) == len(A_ )
return ref_ids
def __SCREAMING_SNAKE_CASE ( A_ ):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , '''r''' , encoding='''utf-8''' ) as f:
lowerCAmelCase__ : List[str] = f.readlines()
lowerCAmelCase__ : Union[str, Any] = [line.strip() for line in data if len(A_ ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowerCAmelCase__ : int = LTP(args.ltp ) # faster in GPU device
lowerCAmelCase__ : str = BertTokenizer.from_pretrained(args.bert )
lowerCAmelCase__ : List[Any] = prepare_ref(A_ , A_ , A_ )
with open(args.save_path , '''w''' , encoding='''utf-8''' ) as f:
lowerCAmelCase__ : str = [json.dumps(A_ ) + '''\n''' for ref in ref_ids]
f.writelines(A_ )
if __name__ == "__main__":
__UpperCamelCase : List[str] = argparse.ArgumentParser(description='''prepare_chinese_ref''')
parser.add_argument(
'''--file_name''',
type=str,
default='''./resources/chinese-demo.txt''',
help='''file need process, same as training data in lm''',
)
parser.add_argument(
'''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
)
parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')
__UpperCamelCase : Optional[Any] = parser.parse_args()
main(args)
| 106 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __init__( self : List[Any] ,lowercase_ : Tuple ,lowercase_ : Dict ,lowercase_ : str ):
lowerCAmelCase__ : int = dataset
lowerCAmelCase__ : List[str] = process
lowerCAmelCase__ : Dict = params
def __len__( self : Any ):
return len(self.dataset )
def __getitem__( self : Union[str, Any] ,lowercase_ : List[Any] ):
lowerCAmelCase__ : Union[str, Any] = self.dataset[i]
lowerCAmelCase__ : Optional[Any] = self.process(lowercase_ ,**self.params )
return processed
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __init__( self : Optional[int] ,lowercase_ : Optional[Any] ,lowercase_ : List[Any] ,lowercase_ : Optional[Any] ,lowercase_ : Tuple=None ):
lowerCAmelCase__ : List[Any] = loader
lowerCAmelCase__ : int = infer
lowerCAmelCase__ : List[str] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
lowerCAmelCase__ : int = None
lowerCAmelCase__ : Dict = loader_batch_size
# Internal bookkeeping
lowerCAmelCase__ : Optional[Any] = None
lowerCAmelCase__ : Optional[int] = None
def __len__( self : Union[str, Any] ):
return len(self.loader )
def __iter__( self : List[Any] ):
lowerCAmelCase__ : List[Any] = iter(self.loader )
return self
def __lowerCAmelCase ( self : Tuple ):
if isinstance(self._loader_batch_data ,torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
lowerCAmelCase__ : Tuple = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
lowerCAmelCase__ : int = {}
for k, element in self._loader_batch_data.items():
if isinstance(lowercase_ ,lowercase_ ):
# Convert ModelOutput to tuple first
lowerCAmelCase__ : List[Any] = element.to_tuple()
if isinstance(element[0] ,torch.Tensor ):
lowerCAmelCase__ : List[Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] ,np.ndarray ):
lowerCAmelCase__ : str = tuple(np.expand_dims(el[self._loader_batch_index] ,0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowercase_ ,lowercase_ ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] ,torch.Tensor ):
lowerCAmelCase__ : Dict = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] ,np.ndarray ):
lowerCAmelCase__ : Optional[int] = tuple(np.expand_dims(el[self._loader_batch_index] ,0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
lowerCAmelCase__ : Dict = None
elif isinstance(element[self._loader_batch_index] ,torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
lowerCAmelCase__ : str = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] ,np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
lowerCAmelCase__ : Tuple = np.expand_dims(element[self._loader_batch_index] ,0 )
else:
# This is typically a list, so no need to `unsqueeze`.
lowerCAmelCase__ : int = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
lowerCAmelCase__ : int = self._loader_batch_data.__class__(lowercase_ )
self._loader_batch_index += 1
return result
def __lowerCAmelCase ( self : Optional[int] ):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
lowerCAmelCase__ : Dict = next(self.iterator )
lowerCAmelCase__ : List[Any] = self.infer(lowercase_ ,**self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(lowercase_ ,torch.Tensor ):
lowerCAmelCase__ : int = processed
else:
lowerCAmelCase__ : Union[str, Any] = list(processed.keys() )[0]
lowerCAmelCase__ : Union[str, Any] = processed[key]
if isinstance(lowercase_ ,lowercase_ ):
lowerCAmelCase__ : List[Any] = len(lowercase_ )
else:
lowerCAmelCase__ : List[str] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
lowerCAmelCase__ : Optional[Any] = observed_batch_size
# Setting internal index to unwrap the batch
lowerCAmelCase__ : str = processed
lowerCAmelCase__ : Any = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __init__( self : int ,lowercase_ : str ,lowercase_ : str ,lowercase_ : Union[str, Any] ,lowercase_ : int=None ):
super().__init__(lowercase_ ,lowercase_ ,lowercase_ )
def __iter__( self : List[Any] ):
lowerCAmelCase__ : Dict = iter(self.loader )
lowerCAmelCase__ : Tuple = None
return self
def __lowerCAmelCase ( self : Optional[int] ):
if self.subiterator is None:
lowerCAmelCase__ : List[Any] = self.infer(next(self.iterator ) ,**self.params )
try:
# Try to return next item
lowerCAmelCase__ : Optional[int] = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
lowerCAmelCase__ : Any = self.infer(next(self.iterator ) ,**self.params )
lowerCAmelCase__ : int = next(self.subiterator )
return processed
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __iter__( self : Tuple ):
lowerCAmelCase__ : int = iter(self.loader )
return self
def __lowerCAmelCase ( self : List[Any] ):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
lowerCAmelCase__ : Tuple = False
lowerCAmelCase__ : str = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
lowerCAmelCase__ : Dict = self.loader_batch_item()
lowerCAmelCase__ : Optional[Any] = item.pop('''is_last''' )
accumulator.append(lowercase_ )
if is_last:
return accumulator
while not is_last:
lowerCAmelCase__ : Any = self.infer(next(self.iterator ) ,**self.params )
if self.loader_batch_size is not None:
if isinstance(lowercase_ ,torch.Tensor ):
lowerCAmelCase__ : Tuple = processed
else:
lowerCAmelCase__ : List[Any] = list(processed.keys() )[0]
lowerCAmelCase__ : Union[str, Any] = processed[key]
if isinstance(lowercase_ ,lowercase_ ):
lowerCAmelCase__ : Tuple = len(lowercase_ )
else:
lowerCAmelCase__ : str = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
lowerCAmelCase__ : Optional[int] = observed_batch_size
lowerCAmelCase__ : Optional[int] = processed
lowerCAmelCase__ : Optional[int] = 0
while self._loader_batch_index < self.loader_batch_size:
lowerCAmelCase__ : Any = self.loader_batch_item()
lowerCAmelCase__ : Optional[Any] = item.pop('''is_last''' )
accumulator.append(lowercase_ )
if is_last:
return accumulator
else:
lowerCAmelCase__ : Dict = processed
lowerCAmelCase__ : Tuple = item.pop('''is_last''' )
accumulator.append(lowercase_ )
return accumulator
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __init__( self : int ,lowercase_ : Dataset ,lowercase_ : str ):
lowerCAmelCase__ : List[Any] = dataset
lowerCAmelCase__ : List[Any] = key
def __len__( self : List[Any] ):
return len(self.dataset )
def __getitem__( self : str ,lowercase_ : Union[str, Any] ):
return self.dataset[i][self.key]
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __init__( self : Dict ,lowercase_ : Dataset ,lowercase_ : str ,lowercase_ : str ):
lowerCAmelCase__ : str = dataset
lowerCAmelCase__ : List[str] = keya
lowerCAmelCase__ : Optional[Any] = keya
def __len__( self : str ):
return len(self.dataset )
def __getitem__( self : Optional[int] ,lowercase_ : Union[str, Any] ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 106 | 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
__a = logging.get_logger(__name__)
__a = {'vocab_file': 'spm_char.model'}
__a = {
'vocab_file': {
'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
}
}
__a = {
'microsoft/speecht5_asr': 1_024,
'microsoft/speecht5_tts': 1_024,
'microsoft/speecht5_vc': 1_024,
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Any = VOCAB_FILES_NAMES
UpperCamelCase_ : Any = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[Any]="<s>" , lowerCAmelCase__ : int="</s>" , lowerCAmelCase__ : Any="<unk>" , lowerCAmelCase__ : int="<pad>" , lowerCAmelCase__ : Optional[Dict[str, Any]] = None , **lowerCAmelCase__ : List[Any] , ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
_UpperCAmelCase : Tuple = vocab_file
_UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCAmelCase )
@property
def _lowerCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.sp_model.get_piece_size()
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : int = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Dict = self.__dict__.copy()
_UpperCAmelCase : Optional[int] = None
return state
def __setstate__( self : Union[str, Any] , lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Any = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_UpperCAmelCase : List[str] = {}
_UpperCAmelCase : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : str ) -> Union[str, Any]:
"""simple docstring"""
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return self.sp_model.piece_to_id(_lowerCAmelCase )
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.sp_model.IdToPiece(_lowerCAmelCase )
return token
def _lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : Tuple = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCAmelCase ) + token
_UpperCAmelCase : str = []
else:
current_sub_tokens.append(_lowerCAmelCase )
out_string += self.sp_model.decode(_lowerCAmelCase )
return out_string.strip()
def _lowerCAmelCase ( self : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Any=None ) -> Union[str, Any]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : bool = False ) -> Union[str, Any]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
_UpperCAmelCase : str = [1]
if token_ids_a is None:
return ([0] * len(_lowerCAmelCase )) + suffix_ones
return ([0] * len(_lowerCAmelCase )) + ([0] * len(_lowerCAmelCase )) + suffix_ones
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> List[Any]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[Any] = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase , "wb" ) as fi:
_UpperCAmelCase : int = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,) | 353 | '''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (EulerDiscreteScheduler,)
UpperCamelCase_ : Tuple = 10
def _lowerCAmelCase ( self : Dict , **lowerCAmelCase__ : Tuple ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = {
"num_train_timesteps": 1_1_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def _lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowerCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : int = torch.manual_seed(0 )
_UpperCAmelCase : Any = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : List[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = output.prev_sample
_UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Any = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : str = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = self.dummy_model()
_UpperCAmelCase : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : Tuple = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : Union[str, Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = output.prev_sample
_UpperCAmelCase : Tuple = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Any = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3
def _lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : str = self.dummy_model()
_UpperCAmelCase : Any = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : str = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Any = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Tuple = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : int = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : str = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : int = self.get_scheduler_config()
_UpperCAmelCase : Union[str, Any] = scheduler_class(**lowerCAmelCase__ , use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = self.dummy_model()
_UpperCAmelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_UpperCAmelCase : Optional[int] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
_UpperCAmelCase : List[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : str = model(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = output.prev_sample
_UpperCAmelCase : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
_UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1e-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1e-3 | 17 | 0 |
"""simple docstring"""
a = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmelCase ) , '''Tatoeba directory does not exist.''' )
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Optional[int] ):
self.resolver.convert_models(['heb-eng'] )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A , _A = self.resolver.write_model_card('opus-mt-he-en' , dry_run=_UpperCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 315 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class UpperCamelCase ( _UpperCAmelCase ):
def __init__( self , **UpperCAmelCase__ ):
super().__init__(**UpperCAmelCase__ )
if self.framework == "tf":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )
requires_backends(self , "vision" )
self.check_model_type(UpperCAmelCase__ )
def __call__( self , UpperCAmelCase__ , UpperCAmelCase__ = None , **UpperCAmelCase__ , ):
if "text_queries" in kwargs:
A__ = kwargs.pop("text_queries" )
if isinstance(UpperCAmelCase__ , (str, Image.Image) ):
A__ = {"image": image, "candidate_labels": candidate_labels}
else:
A__ = image
A__ = super().__call__(UpperCAmelCase__ , **UpperCAmelCase__ )
return results
def __A ( self , **UpperCAmelCase__ ):
A__ = {}
if "threshold" in kwargs:
A__ = kwargs["threshold"]
if "top_k" in kwargs:
A__ = kwargs["top_k"]
return {}, {}, postprocess_params
def __A ( self , UpperCAmelCase__ ):
A__ = load_image(inputs["image"] )
A__ = inputs["candidate_labels"]
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
A__ = candidate_labels.split("," )
A__ = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(UpperCAmelCase__ ):
A__ = self.tokenizer(UpperCAmelCase__ , return_tensors=self.framework )
A__ = self.image_processor(UpperCAmelCase__ , return_tensors=self.framework )
yield {
"is_last": i == len(UpperCAmelCase__ ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def __A ( self , UpperCAmelCase__ ):
A__ = model_inputs.pop("target_size" )
A__ = model_inputs.pop("candidate_label" )
A__ = model_inputs.pop("is_last" )
A__ = self.model(**UpperCAmelCase__ )
A__ = {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def __A ( self , UpperCAmelCase__ , UpperCAmelCase__=0.1 , UpperCAmelCase__=None ):
A__ = []
for model_output in model_outputs:
A__ = model_output["candidate_label"]
A__ = BaseModelOutput(UpperCAmelCase__ )
A__ = self.image_processor.post_process_object_detection(
outputs=UpperCAmelCase__ , threshold=UpperCAmelCase__ , target_sizes=model_output["target_size"] )[0]
for index in outputs["scores"].nonzero():
A__ = outputs["scores"][index].item()
A__ = self._get_bounding_box(outputs["boxes"][index][0] )
A__ = {"score": score, "label": label, "box": box}
results.append(UpperCAmelCase__ )
A__ = sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : x["score"] , reverse=UpperCAmelCase__ )
if top_k:
A__ = results[:top_k]
return results
def __A ( self , UpperCAmelCase__ ):
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch." )
A__ , A__ , A__ , A__ = box.int().tolist()
A__ = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 198 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class UpperCamelCase ( _UpperCAmelCase ):
def __init__( self , UpperCAmelCase__ , UpperCAmelCase__ = None , UpperCAmelCase__ = None , UpperCAmelCase__ = None , UpperCAmelCase__ = False , UpperCAmelCase__ = False , UpperCAmelCase__ = None , UpperCAmelCase__ = None , **UpperCAmelCase__ , ):
super().__init__(
UpperCAmelCase__ , split=UpperCAmelCase__ , features=UpperCAmelCase__ , cache_dir=UpperCAmelCase__ , keep_in_memory=UpperCAmelCase__ , streaming=UpperCAmelCase__ , num_proc=UpperCAmelCase__ , **UpperCAmelCase__ , )
A__ = field
A__ = path_or_paths if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else {self.split: path_or_paths}
A__ = Json(
cache_dir=UpperCAmelCase__ , data_files=UpperCAmelCase__ , features=UpperCAmelCase__ , field=UpperCAmelCase__ , **UpperCAmelCase__ , )
def __A ( self ):
# Build iterable dataset
if self.streaming:
A__ = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
A__ = None
A__ = None
A__ = None
A__ = None
self.builder.download_and_prepare(
download_config=UpperCAmelCase__ , download_mode=UpperCAmelCase__ , verification_mode=UpperCAmelCase__ , base_path=UpperCAmelCase__ , num_proc=self.num_proc , )
A__ = self.builder.as_dataset(
split=self.split , verification_mode=UpperCAmelCase__ , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase :
def __init__( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = None , UpperCAmelCase__ = None , **UpperCAmelCase__ , ):
if num_proc is not None and num_proc <= 0:
raise ValueError(F"""num_proc {num_proc} must be an integer > 0.""" )
A__ = dataset
A__ = path_or_buf
A__ = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
A__ = num_proc
A__ = "utf-8"
A__ = to_json_kwargs
def __A ( self ):
A__ = self.to_json_kwargs.pop("path_or_buf" , UpperCAmelCase__ )
A__ = self.to_json_kwargs.pop("orient" , "records" )
A__ = self.to_json_kwargs.pop("lines" , True if orient == "records" else False )
A__ = self.to_json_kwargs.pop("index" , False if orient in ["split", "table"] else True )
A__ = self.to_json_kwargs.pop("compression" , UpperCAmelCase__ )
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(F"""`datasets` currently does not support {compression} compression""" )
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with fsspec.open(self.path_or_buf , "wb" , compression=UpperCAmelCase__ ) as buffer:
A__ = self._write(file_obj=UpperCAmelCase__ , orient=UpperCAmelCase__ , lines=UpperCAmelCase__ , index=UpperCAmelCase__ , **self.to_json_kwargs )
else:
if compression:
raise NotImplementedError(
F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
" was passed. Please provide a local path instead." )
A__ = self._write(
file_obj=self.path_or_buf , orient=UpperCAmelCase__ , lines=UpperCAmelCase__ , index=UpperCAmelCase__ , **self.to_json_kwargs )
return written
def __A ( self , UpperCAmelCase__ ):
A__ , A__ , A__ , A__ , A__ = args
A__ = query_table(
table=self.dataset.data , key=slice(UpperCAmelCase__ , offset + self.batch_size ) , indices=self.dataset._indices , )
A__ = batch.to_pandas().to_json(
path_or_buf=UpperCAmelCase__ , orient=UpperCAmelCase__ , lines=UpperCAmelCase__ , index=UpperCAmelCase__ , **UpperCAmelCase__ )
if not json_str.endswith("\n" ):
json_str += "\n"
return json_str.encode(self.encoding )
def __A ( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ , ):
A__ = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating json from Arrow format" , ):
A__ = self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(UpperCAmelCase__ )
else:
A__ , A__ = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , UpperCAmelCase__ , UpperCAmelCase__ )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating json from Arrow format" , ):
written += file_obj.write(UpperCAmelCase__ )
return written
| 198 | 1 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
lowercase__ : Optional[int] = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
lowercase__ : List[str] = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw ).convert('''RGB''' )
lowercase__ : Any = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3) , (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1) ),
] )
lowercase__ : Dict = transform(__lowerCamelCase ).unsqueeze(0 ).to(__lowerCamelCase )
return image
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
if "visual_encoder" in key:
lowercase__ : Any = re.sub('''visual_encoder*''' , '''vision_model.encoder''' , __lowerCamelCase )
if "blocks" in key:
lowercase__ : List[str] = re.sub(r'''blocks''' , '''layers''' , __lowerCamelCase )
if "attn" in key:
lowercase__ : List[str] = re.sub(r'''attn''' , '''self_attn''' , __lowerCamelCase )
if "norm1" in key:
lowercase__ : Dict = re.sub(r'''norm1''' , '''layer_norm1''' , __lowerCamelCase )
if "norm2" in key:
lowercase__ : Optional[Any] = re.sub(r'''norm2''' , '''layer_norm2''' , __lowerCamelCase )
if "encoder.norm" in key:
lowercase__ : Union[str, Any] = re.sub(r'''encoder.norm''' , '''post_layernorm''' , __lowerCamelCase )
if "encoder.patch_embed.proj" in key:
lowercase__ : int = re.sub(r'''encoder.patch_embed.proj''' , '''embeddings.patch_embedding''' , __lowerCamelCase )
if "encoder.pos_embed" in key:
lowercase__ : int = re.sub(r'''encoder.pos_embed''' , '''embeddings.position_embedding''' , __lowerCamelCase )
if "encoder.cls_token" in key:
lowercase__ : Dict = re.sub(r'''encoder.cls_token''' , '''embeddings.class_embedding''' , __lowerCamelCase )
if "self_attn" in key:
lowercase__ : Optional[int] = re.sub(r'''self_attn.proj''' , '''self_attn.projection''' , __lowerCamelCase )
return key
@torch.no_grad()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=None ) -> Optional[int]:
if config_path is not None:
lowercase__ : Union[str, Any] = BlipConfig.from_pretrained(__lowerCamelCase )
else:
lowercase__ : str = BlipConfig(projection_dim=5_12 , text_config={} , vision_config={} )
lowercase__ : Dict = BlipForConditionalGeneration(__lowerCamelCase ).eval()
lowercase__ : Any = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''
lowercase__ : Optional[int] = blip_decoder(pretrained=__lowerCamelCase , image_size=3_84 , vit='''base''' )
lowercase__ : Optional[int] = pt_model.eval()
lowercase__ : Union[str, Any] = pt_model.state_dict()
for key in modified_state_dict.copy():
lowercase__ : Any = modified_state_dict.pop(__lowerCamelCase )
lowercase__ : Union[str, Any] = rename_key(__lowerCamelCase )
lowercase__ : Union[str, Any] = value
hf_model.load_state_dict(__lowerCamelCase )
lowercase__ : int = 3_84
lowercase__ : Optional[int] = load_demo_image(image_size=__lowerCamelCase , device='''cpu''' )
lowercase__ : str = BertTokenizer.from_pretrained('''bert-base-uncased''' )
lowercase__ : Dict = tokenizer(['''a picture of'''] ).input_ids
lowercase__ : Tuple = hf_model.generate(__lowerCamelCase , __lowerCamelCase )
assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
lowercase__ : Tuple = hf_model.generate(__lowerCamelCase )
assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(__lowerCamelCase )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
lowercase__ : int = (
'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
)
lowercase__ : Optional[Any] = blip_vqa(pretrained=__lowerCamelCase , image_size=__lowerCamelCase , vit='''base''' )
vqa_model.eval()
lowercase__ : Optional[Any] = vqa_model.state_dict()
for key in modified_state_dict.copy():
lowercase__ : str = modified_state_dict.pop(__lowerCamelCase )
lowercase__ : Optional[Any] = rename_key(__lowerCamelCase )
lowercase__ : List[Any] = value
lowercase__ : Tuple = BlipForQuestionAnswering(__lowerCamelCase )
hf_vqa_model.load_state_dict(__lowerCamelCase )
lowercase__ : Union[str, Any] = ['''How many dogs are in this image?''']
lowercase__ : Tuple = tokenizer(__lowerCamelCase , return_tensors='''pt''' ).input_ids
lowercase__ : Optional[int] = hf_vqa_model.generate(__lowerCamelCase , __lowerCamelCase )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa''' )
lowercase__ : Any = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''
lowercase__ : str = blip_itm(pretrained=__lowerCamelCase , image_size=__lowerCamelCase , vit='''base''' )
itm_model.eval()
lowercase__ : str = itm_model.state_dict()
for key in modified_state_dict.copy():
lowercase__ : str = modified_state_dict.pop(__lowerCamelCase )
lowercase__ : List[Any] = rename_key(__lowerCamelCase )
lowercase__ : Union[str, Any] = value
lowercase__ : Union[str, Any] = BlipForImageTextRetrieval(__lowerCamelCase )
lowercase__ : Any = ['''A picture of a woman with a dog sitting in a beach''']
lowercase__ : Optional[Any] = tokenizer(
__lowerCamelCase , return_tensors='''pt''' , padding='''max_length''' , truncation=__lowerCamelCase , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(__lowerCamelCase )
hf_itm_model.eval()
lowercase__ : Optional[Any] = hf_itm_model(__lowerCamelCase , __lowerCamelCase , use_itm_head=__lowerCamelCase )
lowercase__ : Any = hf_itm_model(__lowerCamelCase , __lowerCamelCase , use_itm_head=__lowerCamelCase )
assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
lowerCAmelCase_ = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 16 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : torch.FloatTensor
UpperCamelCase_ : torch.FloatTensor
UpperCamelCase_ : Optional[torch.FloatTensor] = None
class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Tuple = 2
@register_to_config
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : float = 0.02 , SCREAMING_SNAKE_CASE_ : float = 1_00 , SCREAMING_SNAKE_CASE_ : float = 1.007 , SCREAMING_SNAKE_CASE_ : float = 80 , SCREAMING_SNAKE_CASE_ : float = 0.05 , SCREAMING_SNAKE_CASE_ : float = 50 , ) -> Optional[int]:
'''simple docstring'''
A: Union[str, Any] = sigma_max
# setable values
A: int = None
A: np.IntTensor = None
A: torch.FloatTensor = None # sigma(t_i)
def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Optional[int] = None ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, torch.device] = None ) -> Optional[Any]:
'''simple docstring'''
A: List[Any] = num_inference_steps
A: List[str] = np.arange(0 , self.num_inference_steps )[::-1].copy()
A: Any = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
A: str = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
A: Tuple = torch.tensor(SCREAMING_SNAKE_CASE_ , dtype=torch.floataa , device=SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : Optional[torch.Generator] = None ) -> Tuple[torch.FloatTensor, float]:
'''simple docstring'''
if self.config.s_min <= sigma <= self.config.s_max:
A: str = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
A: List[str] = 0
# sample eps ~ N(0, S_noise^2 * I)
A: Optional[Any] = self.config.s_noise * randn_tensor(sample.shape , generator=SCREAMING_SNAKE_CASE_ ).to(sample.device )
A: Optional[Any] = sigma + gamma * sigma
A: List[Any] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[KarrasVeOutput, Tuple]:
'''simple docstring'''
A: Union[str, Any] = sample_hat + sigma_hat * model_output
A: str = (sample_hat - pred_original_sample) / sigma_hat
A: Optional[int] = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=SCREAMING_SNAKE_CASE_ , derivative=SCREAMING_SNAKE_CASE_ , pred_original_sample=SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[KarrasVeOutput, Tuple]:
'''simple docstring'''
A: int = sample_prev + sigma_prev * model_output
A: List[Any] = (sample_prev - pred_original_sample) / sigma_prev
A: Dict = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=SCREAMING_SNAKE_CASE_ , derivative=SCREAMING_SNAKE_CASE_ , pred_original_sample=SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str ) -> Dict:
'''simple docstring'''
raise NotImplementedError()
| 319 | 0 |
"""simple docstring"""
def lowercase_ ( _lowerCamelCase: list ) -> list:
'''simple docstring'''
def merge(_lowerCamelCase: list , _lowerCamelCase: list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(_lowerCamelCase ) <= 1:
return collection
__lowerCamelCase : Union[str, Any] = len(_lowerCamelCase ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__A = input('''Enter numbers separated by a comma:\n''').strip()
__A = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''') | 64 | """simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowercase_ ( _lowerCamelCase: Tuple , _lowerCamelCase: Dict=False ) -> Any:
'''simple docstring'''
__lowerCamelCase : Any = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""module.blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""module.blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
("module.cls_token", "vit.embeddings.cls_token"),
("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("module.pos_embed", "vit.embeddings.position_embeddings"),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("module.norm.weight", "layernorm.weight"),
("module.norm.bias", "layernorm.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__lowerCamelCase : str = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def lowercase_ ( _lowerCamelCase: Optional[int] , _lowerCamelCase: int , _lowerCamelCase: List[str]=False ) -> Union[str, Any]:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
__lowerCamelCase : Any = ""
else:
__lowerCamelCase : Optional[int] = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowerCamelCase : Optional[int] = state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight""" )
__lowerCamelCase : List[str] = state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
__lowerCamelCase : str = in_proj_bias[: config.hidden_size]
__lowerCamelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowerCamelCase : Any = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
__lowerCamelCase : str = in_proj_bias[-config.hidden_size :]
def lowercase_ ( _lowerCamelCase: int ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase : Tuple = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCamelCase , _lowerCamelCase )
def lowercase_ ( _lowerCamelCase: Tuple ) -> List[str]:
'''simple docstring'''
__lowerCamelCase : List[Any] = [
"module.fc.fc1.weight",
"module.fc.fc1.bias",
"module.fc.bn1.weight",
"module.fc.bn1.bias",
"module.fc.bn1.running_mean",
"module.fc.bn1.running_var",
"module.fc.bn1.num_batches_tracked",
"module.fc.fc2.weight",
"module.fc.fc2.bias",
"module.fc.bn2.weight",
"module.fc.bn2.bias",
"module.fc.bn2.running_mean",
"module.fc.bn2.running_var",
"module.fc.bn2.num_batches_tracked",
"module.fc.fc3.weight",
"module.fc.fc3.bias",
]
for k in ignore_keys:
state_dict.pop(_lowerCamelCase , _lowerCamelCase )
def lowercase_ ( _lowerCamelCase: Optional[int] , _lowerCamelCase: List[str] , _lowerCamelCase: Optional[int] ) -> Any:
'''simple docstring'''
__lowerCamelCase : str = dct.pop(_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = val
def lowercase_ ( _lowerCamelCase: Union[str, Any] , _lowerCamelCase: Tuple ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase : int = ViTMSNConfig()
__lowerCamelCase : Dict = 1000
__lowerCamelCase : str = "datasets/huggingface/label-files"
__lowerCamelCase : Optional[int] = "imagenet-1k-id2label.json"
__lowerCamelCase : List[str] = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase ) , "r" ) )
__lowerCamelCase : str = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
__lowerCamelCase : int = idalabel
__lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
__lowerCamelCase : int = 384
__lowerCamelCase : Optional[int] = 1536
__lowerCamelCase : str = 6
elif "l16" in checkpoint_url:
__lowerCamelCase : Optional[Any] = 1024
__lowerCamelCase : str = 4096
__lowerCamelCase : Any = 24
__lowerCamelCase : Optional[int] = 16
__lowerCamelCase : Union[str, Any] = 0.1
elif "b4" in checkpoint_url:
__lowerCamelCase : Optional[Any] = 4
elif "l7" in checkpoint_url:
__lowerCamelCase : str = 7
__lowerCamelCase : int = 1024
__lowerCamelCase : int = 4096
__lowerCamelCase : Union[str, Any] = 24
__lowerCamelCase : Optional[int] = 16
__lowerCamelCase : List[Any] = 0.1
__lowerCamelCase : str = ViTMSNModel(_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = torch.hub.load_state_dict_from_url(_lowerCamelCase , map_location="cpu" )["target_encoder"]
__lowerCamelCase : Any = ViTImageProcessor(size=config.image_size )
remove_projection_head(_lowerCamelCase )
__lowerCamelCase : Tuple = create_rename_keys(_lowerCamelCase , base_model=_lowerCamelCase )
for src, dest in rename_keys:
rename_key(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
read_in_q_k_v(_lowerCamelCase , _lowerCamelCase , base_model=_lowerCamelCase )
model.load_state_dict(_lowerCamelCase )
model.eval()
__lowerCamelCase : Any = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowerCamelCase : Tuple = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
__lowerCamelCase : List[str] = ViTImageProcessor(
size=config.image_size , image_mean=_lowerCamelCase , image_std=_lowerCamelCase )
__lowerCamelCase : Tuple = image_processor(images=_lowerCamelCase , return_tensors="pt" )
# forward pass
torch.manual_seed(2 )
__lowerCamelCase : Optional[int] = model(**_lowerCamelCase )
__lowerCamelCase : List[str] = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
__lowerCamelCase : Any = torch.tensor([[-1.0915, -1.4876, -1.1809]] )
elif "b16" in checkpoint_url:
__lowerCamelCase : Optional[Any] = torch.tensor([[14.2889, -18.9045, 11.7281]] )
elif "l16" in checkpoint_url:
__lowerCamelCase : List[str] = torch.tensor([[41.5028, -22.8681, 45.6475]] )
elif "b4" in checkpoint_url:
__lowerCamelCase : str = torch.tensor([[-4.3868, 5.2932, -0.4137]] )
else:
__lowerCamelCase : Optional[int] = torch.tensor([[-0.1792, -0.6465, 2.4263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , _lowerCamelCase , atol=1E-4 )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(_lowerCamelCase )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_lowerCamelCase )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__A = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path) | 64 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.mark.parametrize('revision' , [None, 'v2'] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = hf_hub_url(repo_id=UpperCAmelCase_ , path=UpperCAmelCase_ , revision=UpperCAmelCase_ )
assert url == F"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(UpperCAmelCase_ )}"""
| 151 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) < k or k < 0:
raise ValueError('Invalid Input' )
UpperCAmelCase : Tuple = sum(array[:k] )
for i in range(len(UpperCAmelCase_ ) - k ):
UpperCAmelCase : Optional[Any] = current_sum - array[i] + array[i + k]
UpperCAmelCase : List[Any] = max(UpperCAmelCase_ , UpperCAmelCase_ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
from random import randint
testmod()
lowercase__ = [randint(-1000, 1000) for i in range(100)]
lowercase__ = randint(0, 110)
print(f'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')
| 151 | 1 |
'''simple docstring'''
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase):
'''simple docstring'''
__A : Optional[Any] = data
__A : Node | None = None
__A : Node | None = None
def _lowerCAmelCase ( __snake_case : Node | None ) -> None: # In Order traversal of the tree
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def _lowerCAmelCase ( __snake_case : Node | None ) -> int:
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def _lowerCAmelCase ( __snake_case : Node ) -> bool:
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def _lowerCAmelCase ( ) -> None: # Main function for testing.
__A : List[str] = Node(1 )
__A : Optional[Any] = Node(2 )
__A : Dict = Node(3 )
__A : List[str] = Node(4 )
__A : Optional[Any] = Node(5 )
__A : List[str] = Node(6 )
__A : Dict = Node(7 )
__A : Optional[Any] = Node(8 )
__A : List[str] = Node(9 )
print(is_full_binary_tree(__snake_case ) )
print(depth_of_tree(__snake_case ) )
print('Tree is: ' )
display(__snake_case )
if __name__ == "__main__":
main() | 190 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : 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(__snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowerCAmelCase ( __snake_case : float = 0.1 ) -> int:
__A : Dict = 3
__A : int = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(__snake_case )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod() | 190 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = num_of_nodes
SCREAMING_SNAKE_CASE : list[list[int]] = []
SCREAMING_SNAKE_CASE : dict[int, int] = {}
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
SCREAMING_SNAKE_CASE : str = self.find_component(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : list[int] , lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
SCREAMING_SNAKE_CASE : Any = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowerCamelCase_ )
elif component_size[u_node] >= component_size[v_node]:
SCREAMING_SNAKE_CASE : str = self.find_component(lowerCamelCase_ )
component_size[u_node] += component_size[v_node]
self.set_component(lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : int = edge
SCREAMING_SNAKE_CASE : Any = self.m_component[u]
SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[str] = edge
SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
SCREAMING_SNAKE_CASE : Dict = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
SCREAMING_SNAKE_CASE : List[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def __A ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 323 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class UpperCamelCase__ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : Dict[str, int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : int = None , lowerCamelCase_ : int = None ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : Any = pad_token_id
SCREAMING_SNAKE_CASE : List[Any] = max_length
SCREAMING_SNAKE_CASE : Optional[int] = vocab
SCREAMING_SNAKE_CASE : List[Any] = merges
SCREAMING_SNAKE_CASE : Tuple = BytePairTokenizer(lowerCamelCase_ , lowerCamelCase_ , sequence_length=lowerCamelCase_ )
@classmethod
def lowerCamelCase_ ( cls : Any , lowerCamelCase_ : GPTaTokenizer , *lowerCamelCase_ : str , **lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = [""" """.join(lowerCamelCase_ ) for m in tokenizer.bpe_ranks.keys()]
SCREAMING_SNAKE_CASE : List[str] = tokenizer.get_vocab()
return cls(lowerCamelCase_ , lowerCamelCase_ , *lowerCamelCase_ , **lowerCamelCase_ )
@classmethod
def lowerCamelCase_ ( cls : List[Any] , lowerCamelCase_ : Union[str, os.PathLike] , *lowerCamelCase_ : str , **lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = GPTaTokenizer.from_pretrained(lowerCamelCase_ , *lowerCamelCase_ , **lowerCamelCase_ )
return cls.from_tokenizer(lowerCamelCase_ , *lowerCamelCase_ , **lowerCamelCase_ )
@classmethod
def lowerCamelCase_ ( cls : List[str] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return cls(**lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : int = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.tf_tokenizer(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = tf.ones_like(lowerCamelCase_ )
if self.pad_token_id is not None:
# pad the tokens up to max length
SCREAMING_SNAKE_CASE : Optional[int] = max_length if max_length is not None else self.max_length
if max_length is not None:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = pad_model_inputs(
lowerCamelCase_ , max_seq_length=lowerCamelCase_ , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 323 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = KandinskyInpaintPipeline
A_ = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
A_ = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
A_ = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
A_ = False
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 32
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 32
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.time_input_dim
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 100
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
return tokenizer
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Union[str, Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
__a : List[Any] = MultilingualCLIP(__a )
__a : int = text_encoder.eval()
return text_encoder
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Optional[Any] = {
'in_channels': 9,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'text_image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'text_image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
__a : str = UNetaDConditionModel(**__a )
return model
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.dummy_text_encoder
__a : List[str] = self.dummy_tokenizer
__a : Optional[Any] = self.dummy_unet
__a : str = self.dummy_movq
__a : str = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=__a , set_alpha_to_one=__a , steps_offset=1 , prediction_type='epsilon' , thresholding=__a , )
__a : Optional[int] = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __UpperCAmelCase ( self , __a , __a=0 ):
'''simple docstring'''
__a : List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__a ) ).to(__a )
__a : Optional[int] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__a )
# create init_image
__a : List[str] = floats_tensor((1, 3, 64, 64) , rng=random.Random(__a ) ).to(__a )
__a : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__a : str = Image.fromarray(np.uinta(__a ) ).convert('RGB' ).resize((256, 256) )
# create mask
__a : Tuple = np.ones((64, 64) , dtype=np.floataa )
__a : Any = 0
if str(__a ).startswith('mps' ):
__a : str = torch.manual_seed(__a )
else:
__a : str = torch.Generator(device=__a ).manual_seed(__a )
__a : List[Any] = {
'prompt': 'horse',
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = 'cpu'
__a : List[Any] = self.get_dummy_components()
__a : str = self.pipeline_class(**__a )
__a : List[str] = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__a : Tuple = pipe(**self.get_dummy_inputs(__a ) )
__a : Any = output.images
__a : Optional[int] = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
__a : int = image[0, -3:, -3:, -1]
__a : Tuple = image_from_tuple[0, -3:, -3:, -1]
print(f"""image.shape {image.shape}""" )
assert image.shape == (1, 64, 64, 3)
__a : Union[str, Any] = np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy' )
__a : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
__a : Union[str, Any] = np.ones((768, 768) , dtype=np.floataa )
__a : List[str] = 0
__a : Dict = 'a hat'
__a : Any = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa )
pipe_prior.to(__a )
__a : List[str] = KandinskyInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-inpaint' , torch_dtype=torch.floataa )
__a : Optional[Any] = pipeline.to(__a )
pipeline.set_progress_bar_config(disable=__a )
__a : Union[str, Any] = torch.Generator(device='cpu' ).manual_seed(0 )
__a , __a : Optional[Any] = pipe_prior(
__a , generator=__a , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__a : Tuple = pipeline(
__a , image=__a , mask_image=__a , image_embeds=__a , negative_image_embeds=__a , generator=__a , num_inference_steps=100 , height=768 , width=768 , output_type='np' , )
__a : Dict = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__a , __a )
| 294 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase : Dict = abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
from transformers.testing_utils import pytest_terminal_summary_main
__a : Any = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE , id=_SCREAMING_SNAKE_CASE )
| 294 | 1 |
"""simple docstring"""
lowerCamelCase_ : List[str] = tuple[float, float, float]
lowerCamelCase_ : Optional[int] = tuple[float, float, float]
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = end_pointa[0] - end_pointa[0]
A_ : Dict = end_pointa[1] - end_pointa[1]
A_ : List[Any] = end_pointa[2] - end_pointa[2]
return (x, y, z)
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = ab[1] * ac[2] - ab[2] * ac[1] # *i
A_ : int = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
A_ : Optional[Any] = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
return tuple(round(_UpperCAmelCase , _UpperCAmelCase ) for x in vector ) == (0, 0, 0)
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 10 ):
"""simple docstring"""
A_ : Optional[int] = create_vector(_UpperCAmelCase , _UpperCAmelCase )
A_ : Dict = create_vector(_UpperCAmelCase , _UpperCAmelCase )
return is_zero_vector(get_ad_vectors_cross(_UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase ) | 286 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
lowerCamelCase_ : str = logging.get_logger(__name__)
@add_end_docstrings(
UpperCAmelCase__ , r"""
top_k (`int`, defaults to 5):
The number of predictions to return.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).
""" , )
class _UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case_ ):
"""simple docstring"""
if self.framework == "tf":
A_ : str = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
A_ : List[str] = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=snake_case_ )
else:
raise ValueError('Unsupported framework' )
return masked_index
def lowerCamelCase_ ( self , snake_case_ ):
"""simple docstring"""
A_ : List[str] = self.get_masked_index(snake_case_ )
A_ : str = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
'fill-mask' , self.model.base_model_prefix , F"""No mask_token ({self.tokenizer.mask_token}) found on the input""" , )
def lowerCamelCase_ ( self , snake_case_ ):
"""simple docstring"""
if isinstance(snake_case_ , snake_case_ ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['input_ids'][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(snake_case_ )
def lowerCamelCase_ ( self , snake_case_ , snake_case_=None , **snake_case_ ):
"""simple docstring"""
if return_tensors is None:
A_ : Any = self.framework
A_ : Dict = self.tokenizer(snake_case_ , return_tensors=snake_case_ )
self.ensure_exactly_one_mask_token(snake_case_ )
return model_inputs
def lowerCamelCase_ ( self , snake_case_ ):
"""simple docstring"""
A_ : Dict = self.model(**snake_case_ )
A_ : Optional[int] = model_inputs['input_ids']
return model_outputs
def lowerCamelCase_ ( self , snake_case_ , snake_case_=5 , snake_case_=None ):
"""simple docstring"""
if target_ids is not None and target_ids.shape[0] < top_k:
A_ : str = target_ids.shape[0]
A_ : Optional[Any] = model_outputs['input_ids'][0]
A_ : List[Any] = model_outputs['logits']
if self.framework == "tf":
A_ : List[str] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
A_ : Union[str, Any] = outputs.numpy()
A_ : Optional[int] = outputs[0, masked_index, :]
A_ : Optional[Any] = stable_softmax(snake_case_ , axis=-1 )
if target_ids is not None:
A_ : Union[str, Any] = tf.gather_nd(tf.squeeze(snake_case_ , 0 ) , target_ids.reshape(-1 , 1 ) )
A_ : Optional[int] = tf.expand_dims(snake_case_ , 0 )
A_ : Any = tf.math.top_k(snake_case_ , k=snake_case_ )
A_ , A_ : str = topk.values.numpy(), topk.indices.numpy()
else:
A_ : int = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=snake_case_ ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
A_ : Tuple = outputs[0, masked_index, :]
A_ : List[str] = logits.softmax(dim=-1 )
if target_ids is not None:
A_ : str = probs[..., target_ids]
A_ , A_ : List[str] = probs.topk(snake_case_ )
A_ : List[Any] = []
A_ : int = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
A_ : str = []
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
A_ : Union[str, Any] = input_ids.numpy().copy()
if target_ids is not None:
A_ : str = target_ids[p].tolist()
A_ : Union[str, Any] = p
# Filter padding out:
A_ : Any = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
A_ : Any = self.tokenizer.decode(snake_case_ , skip_special_tokens=snake_case_ )
A_ : Any = {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p] ), 'sequence': sequence}
row.append(snake_case_ )
result.append(snake_case_ )
if single_mask:
return result[0]
return result
def lowerCamelCase_ ( self , snake_case_ , snake_case_=None ):
"""simple docstring"""
if isinstance(snake_case_ , snake_case_ ):
A_ : List[str] = [targets]
try:
A_ : Optional[int] = self.tokenizer.get_vocab()
except Exception:
A_ : int = {}
A_ : Tuple = []
for target in targets:
A_ : int = vocab.get(snake_case_ , snake_case_ )
if id_ is None:
A_ : Tuple = self.tokenizer(
snake_case_ , add_special_tokens=snake_case_ , return_attention_mask=snake_case_ , return_token_type_ids=snake_case_ , max_length=1 , truncation=snake_case_ , )['input_ids']
if len(snake_case_ ) == 0:
logger.warning(
F"""The specified target token `{target}` does not exist in the model vocabulary. """
'We cannot replace it with anything meaningful, ignoring it' )
continue
A_ : str = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
F"""The specified target token `{target}` does not exist in the model vocabulary. """
F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.""" )
target_ids.append(id_ )
A_ : Tuple = list(set(snake_case_ ) )
if len(snake_case_ ) == 0:
raise ValueError('At least one target must be provided when passed.' )
A_ : Optional[Any] = np.array(snake_case_ )
return target_ids
def lowerCamelCase_ ( self , snake_case_=None , snake_case_=None ):
"""simple docstring"""
A_ : List[str] = {}
if targets is not None:
A_ : Any = self.get_target_ids(snake_case_ , snake_case_ )
A_ : Optional[Any] = target_ids
if top_k is not None:
A_ : int = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'fill-mask' , self.model.base_model_prefix , 'The tokenizer does not define a `mask_token`.' )
return {}, {}, postprocess_params
def __call__( self , snake_case_ , *snake_case_ , **snake_case_ ):
"""simple docstring"""
A_ : List[str] = super().__call__(snake_case_ , **snake_case_ )
if isinstance(snake_case_ , snake_case_ ) and len(snake_case_ ) == 1:
return outputs[0]
return outputs | 286 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
class __lowercase ( lowerCAmelCase__ ):
'''simple docstring'''
a : Union[str, Any] = "summarization"
a : List[str] = ["loss"]
a : Union[str, Any] = ROUGE_KEYS
a : str = "rouge2"
def __init__(self ,_lowerCamelCase ,**_lowerCamelCase ) -> int:
'''simple docstring'''
if hparams.sortish_sampler and hparams.gpus > 1:
__lowercase = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training''' )
if hparams.sortish_sampler:
raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously''' )
super().__init__(_lowerCamelCase ,num_labels=_lowerCamelCase ,mode=self.mode ,**_lowerCamelCase )
use_task_specific_params(self.model ,'''summarization''' )
save_git_info(self.hparams.output_dir )
__lowercase = Path(self.output_dir ) / '''metrics.json'''
__lowercase = Path(self.output_dir ) / '''hparams.pkl'''
pickle_save(self.hparams ,self.hparams_save_path )
__lowercase = 0
__lowercase = defaultdict(_lowerCamelCase )
__lowercase = self.config.model_type
__lowercase = self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size
__lowercase = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
__lowercase = {
'''train''': self.hparams.n_train,
'''val''': self.hparams.n_val,
'''test''': self.hparams.n_test,
}
__lowercase = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
__lowercase = {
'''train''': self.hparams.max_target_length,
'''val''': self.hparams.val_max_target_length,
'''test''': self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}"
assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}"
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
__lowercase = get_git_info()['''repo_sha''']
__lowercase = hparams.num_workers
__lowercase = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,_lowerCamelCase ):
__lowercase = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
__lowercase = self.decoder_start_token_id
__lowercase = (
SeqaSeqDataset if hasattr(self.tokenizer ,'''prepare_seq2seq_batch''' ) else LegacySeqaSeqDataset
)
__lowercase = False
__lowercase = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
__lowercase = self.hparams.eval_max_gen_length
else:
__lowercase = self.model.config.max_length
__lowercase = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def _UpperCAmelCase (self ,_lowerCamelCase ) -> Dict[str, List[str]]:
'''simple docstring'''
__lowercase = {
k: self.tokenizer.batch_decode(v.tolist() ) if '''mask''' not in k else v.shape for k, v in batch.items()
}
save_json(_lowerCamelCase ,Path(self.output_dir ) / '''text_batch.json''' )
save_json({k: v.tolist() for k, v in batch.items()} ,Path(self.output_dir ) / '''tok_batch.json''' )
__lowercase = True
return readable_batch
def _UpperCAmelCase (self ,_lowerCamelCase ,**_lowerCamelCase ) -> List[str]:
'''simple docstring'''
return self.model(_lowerCamelCase ,**_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ) -> Dict:
'''simple docstring'''
__lowercase = self.tokenizer.batch_decode(
_lowerCamelCase ,skip_special_tokens=_lowerCamelCase ,clean_up_tokenization_spaces=_lowerCamelCase )
return lmap(str.strip ,_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ) -> Tuple:
'''simple docstring'''
__lowercase = self.tokenizer.pad_token_id
__lowercase , __lowercase = batch['''input_ids'''], batch['''attention_mask''']
__lowercase = batch['''labels''']
if isinstance(self.model ,_lowerCamelCase ):
__lowercase = self.model._shift_right(_lowerCamelCase )
else:
__lowercase = shift_tokens_right(_lowerCamelCase ,_lowerCamelCase )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
__lowercase = decoder_input_ids
self.save_readable_batch(_lowerCamelCase )
__lowercase = self(_lowerCamelCase ,attention_mask=_lowerCamelCase ,decoder_input_ids=_lowerCamelCase ,use_cache=_lowerCamelCase )
__lowercase = outputs['''logits''']
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
__lowercase = nn.CrossEntropyLoss(ignore_index=_lowerCamelCase )
assert lm_logits.shape[-1] == self.vocab_size
__lowercase = ce_loss_fct(lm_logits.view(-1 ,lm_logits.shape[-1] ) ,tgt_ids.view(-1 ) )
else:
__lowercase = nn.functional.log_softmax(_lowerCamelCase ,dim=-1 )
__lowercase , __lowercase = label_smoothed_nll_loss(
_lowerCamelCase ,_lowerCamelCase ,self.hparams.label_smoothing ,ignore_index=_lowerCamelCase )
return (loss,)
@property
def _UpperCAmelCase (self ) -> int:
'''simple docstring'''
return self.tokenizer.pad_token_id
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ) -> Dict:
'''simple docstring'''
__lowercase = self._step(_lowerCamelCase )
__lowercase = dict(zip(self.loss_names ,_lowerCamelCase ) )
# tokens per batch
__lowercase = batch['''input_ids'''].ne(self.pad ).sum() + batch['''labels'''].ne(self.pad ).sum()
__lowercase = batch['''input_ids'''].shape[0]
__lowercase = batch['''input_ids'''].eq(self.pad ).sum()
__lowercase = batch['''input_ids'''].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ) -> Dict:
'''simple docstring'''
return self._generative_step(_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase="val" ) -> Dict:
'''simple docstring'''
self.step_count += 1
__lowercase = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
__lowercase = losses['''loss''']
__lowercase = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
}
__lowercase = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
__lowercase = torch.tensor(_lowerCamelCase ).type_as(_lowerCamelCase )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(_lowerCamelCase )
__lowercase = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
__lowercase = self.step_count
self.metrics[prefix].append(_lowerCamelCase ) # callback writes this to self.metrics_save_path
__lowercase = flatten_list([x['''preds'''] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
f"{prefix}_loss": loss,
f"{prefix}_{self.val_metric}": metric_tensor,
}
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ) -> Dict:
'''simple docstring'''
return calculate_rouge(_lowerCamelCase ,_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ) -> dict:
'''simple docstring'''
__lowercase = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
__lowercase = self.model.generate(
batch['''input_ids'''] ,attention_mask=batch['''attention_mask'''] ,use_cache=_lowerCamelCase ,decoder_start_token_id=self.decoder_start_token_id ,num_beams=self.eval_beams ,max_length=self.eval_max_length ,)
__lowercase = (time.time() - ta) / batch['''input_ids'''].shape[0]
__lowercase = self.ids_to_clean_text(_lowerCamelCase )
__lowercase = self.ids_to_clean_text(batch['''labels'''] )
__lowercase = self._step(_lowerCamelCase )
__lowercase = dict(zip(self.loss_names ,_lowerCamelCase ) )
__lowercase = self.calc_generative_metrics(_lowerCamelCase ,_lowerCamelCase )
__lowercase = np.mean(lmap(_lowerCamelCase ,_lowerCamelCase ) )
base_metrics.update(gen_time=_lowerCamelCase ,gen_len=_lowerCamelCase ,preds=_lowerCamelCase ,target=_lowerCamelCase ,**_lowerCamelCase )
return base_metrics
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ) -> List[str]:
'''simple docstring'''
return self._generative_step(_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ) -> Union[str, Any]:
'''simple docstring'''
return self.validation_epoch_end(_lowerCamelCase ,prefix='''test''' )
def _UpperCAmelCase (self ,_lowerCamelCase ) -> SeqaSeqDataset:
'''simple docstring'''
__lowercase = self.n_obs[type_path]
__lowercase = self.target_lens[type_path]
__lowercase = self.dataset_class(
self.tokenizer ,type_path=_lowerCamelCase ,n_obs=_lowerCamelCase ,max_target_length=_lowerCamelCase ,**self.dataset_kwargs ,)
return dataset
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase = False ) -> DataLoader:
'''simple docstring'''
__lowercase = self.get_dataset(_lowerCamelCase )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
__lowercase = dataset.make_sortish_sampler(_lowerCamelCase ,distributed=self.hparams.gpus > 1 )
return DataLoader(
_lowerCamelCase ,batch_size=_lowerCamelCase ,collate_fn=dataset.collate_fn ,shuffle=_lowerCamelCase ,num_workers=self.num_workers ,sampler=_lowerCamelCase ,)
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
__lowercase = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch ,distributed=self.hparams.gpus > 1 )
return DataLoader(
_lowerCamelCase ,batch_sampler=_lowerCamelCase ,collate_fn=dataset.collate_fn ,num_workers=self.num_workers ,)
else:
return DataLoader(
_lowerCamelCase ,batch_size=_lowerCamelCase ,collate_fn=dataset.collate_fn ,shuffle=_lowerCamelCase ,num_workers=self.num_workers ,sampler=_lowerCamelCase ,)
def _UpperCAmelCase (self ) -> DataLoader:
'''simple docstring'''
__lowercase = self.get_dataloader('''train''' ,batch_size=self.hparams.train_batch_size ,shuffle=_lowerCamelCase )
return dataloader
def _UpperCAmelCase (self ) -> DataLoader:
'''simple docstring'''
return self.get_dataloader('''val''' ,batch_size=self.hparams.eval_batch_size )
def _UpperCAmelCase (self ) -> DataLoader:
'''simple docstring'''
return self.get_dataloader('''test''' ,batch_size=self.hparams.eval_batch_size )
@staticmethod
def _UpperCAmelCase (_lowerCamelCase ,_lowerCamelCase ) -> int:
'''simple docstring'''
BaseTransformer.add_model_specific_args(_lowerCamelCase ,_lowerCamelCase )
add_generic_args(_lowerCamelCase ,_lowerCamelCase )
parser.add_argument(
'''--max_source_length''' ,default=1024 ,type=_lowerCamelCase ,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) ,)
parser.add_argument(
'''--max_target_length''' ,default=56 ,type=_lowerCamelCase ,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) ,)
parser.add_argument(
'''--val_max_target_length''' ,default=142 ,type=_lowerCamelCase ,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) ,)
parser.add_argument(
'''--test_max_target_length''' ,default=142 ,type=_lowerCamelCase ,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) ,)
parser.add_argument('''--freeze_encoder''' ,action='''store_true''' )
parser.add_argument('''--freeze_embeds''' ,action='''store_true''' )
parser.add_argument('''--sortish_sampler''' ,action='''store_true''' ,default=_lowerCamelCase )
parser.add_argument('''--overwrite_output_dir''' ,action='''store_true''' ,default=_lowerCamelCase )
parser.add_argument('''--max_tokens_per_batch''' ,type=_lowerCamelCase ,default=_lowerCamelCase )
parser.add_argument('''--logger_name''' ,type=_lowerCamelCase ,choices=['''default''', '''wandb''', '''wandb_shared'''] ,default='''default''' )
parser.add_argument('''--n_train''' ,type=_lowerCamelCase ,default=-1 ,required=_lowerCamelCase ,help='''# examples. -1 means use all.''' )
parser.add_argument('''--n_val''' ,type=_lowerCamelCase ,default=500 ,required=_lowerCamelCase ,help='''# examples. -1 means use all.''' )
parser.add_argument('''--n_test''' ,type=_lowerCamelCase ,default=-1 ,required=_lowerCamelCase ,help='''# examples. -1 means use all.''' )
parser.add_argument(
'''--task''' ,type=_lowerCamelCase ,default='''summarization''' ,required=_lowerCamelCase ,help='''# examples. -1 means use all.''' )
parser.add_argument('''--label_smoothing''' ,type=_lowerCamelCase ,default=0.0 ,required=_lowerCamelCase )
parser.add_argument('''--src_lang''' ,type=_lowerCamelCase ,default='''''' ,required=_lowerCamelCase )
parser.add_argument('''--tgt_lang''' ,type=_lowerCamelCase ,default='''''' ,required=_lowerCamelCase )
parser.add_argument('''--eval_beams''' ,type=_lowerCamelCase ,default=_lowerCamelCase ,required=_lowerCamelCase )
parser.add_argument(
'''--val_metric''' ,type=_lowerCamelCase ,default=_lowerCamelCase ,required=_lowerCamelCase ,choices=['''bleu''', '''rouge2''', '''loss''', None] )
parser.add_argument('''--eval_max_gen_length''' ,type=_lowerCamelCase ,default=_lowerCamelCase ,help='''never generate more than n tokens''' )
parser.add_argument('''--save_top_k''' ,type=_lowerCamelCase ,default=1 ,required=_lowerCamelCase ,help='''How many checkpoints to save''' )
parser.add_argument(
'''--early_stopping_patience''' ,type=_lowerCamelCase ,default=-1 ,required=_lowerCamelCase ,help=(
'''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
''' val_check_interval will effect it.'''
) ,)
return parser
class __lowercase ( lowerCAmelCase__ ):
'''simple docstring'''
a : Any = "translation"
a : Union[str, Any] = ["loss"]
a : Optional[int] = ["bleu"]
a : Optional[Any] = "bleu"
def __init__(self ,_lowerCamelCase ,**_lowerCamelCase ) -> List[str]:
'''simple docstring'''
super().__init__(_lowerCamelCase ,**_lowerCamelCase )
__lowercase = hparams.src_lang
__lowercase = hparams.tgt_lang
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ) -> dict:
'''simple docstring'''
return calculate_bleu(_lowerCamelCase ,_lowerCamelCase )
def _lowerCAmelCase ( lowerCamelCase_ : Any , lowerCamelCase_ : List[str]=None ):
Path(args.output_dir ).mkdir(exist_ok=lowerCamelCase_ )
check_output_dir(lowerCamelCase_ , expected_items=3 )
if model is None:
if "summarization" in args.task:
__lowercase = SummarizationModule(lowerCamelCase_ )
else:
__lowercase = TranslationModule(lowerCamelCase_ )
__lowercase = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith('''/tmp''' )
or str(args.output_dir ).startswith('''/var''' )
):
__lowercase = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
__lowercase = os.environ.get('''WANDB_PROJECT''' , lowerCamelCase_ )
__lowercase = WandbLogger(name=model.output_dir.name , project=lowerCamelCase_ )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
__lowercase = WandbLogger(name=model.output_dir.name , project=f"hf_{dataset}" )
if args.early_stopping_patience >= 0:
__lowercase = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
__lowercase = False
__lowercase = args.val_metric == '''loss'''
__lowercase = generic_train(
lowerCamelCase_ , lowerCamelCase_ , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , lowerCamelCase_ ) , early_stopping_callback=lowerCamelCase_ , logger=lowerCamelCase_ , )
pickle_save(model.hparams , model.output_dir / '''hparams.pkl''' )
if not args.do_predict:
return model
__lowercase = ''''''
__lowercase = sorted(glob.glob(os.path.join(args.output_dir , '''*.ckpt''' ) , recursive=lowerCamelCase_ ) )
if checkpoints:
__lowercase = checkpoints[-1]
__lowercase = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
_SCREAMING_SNAKE_CASE = pl.Trainer.add_argparse_args(parser)
_SCREAMING_SNAKE_CASE = SummarizationModule.add_model_specific_args(parser, os.getcwd())
_SCREAMING_SNAKE_CASE = parser.parse_args()
main(args)
| 217 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotConfig''',
'''BlenderbotOnnxConfig''',
],
'''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ['''BlenderbotTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotForCausalLM''',
'''BlenderbotForConditionalGeneration''',
'''BlenderbotModel''',
'''BlenderbotPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'''TFBlenderbotForConditionalGeneration''',
'''TFBlenderbotModel''',
'''TFBlenderbotPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'''FlaxBlenderbotForConditionalGeneration''',
'''FlaxBlenderbotModel''',
'''FlaxBlenderbotPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 217 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
a =[
{"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
{"""dataset""": """snli""", """config_name""": """plain_text"""},
{"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
{"""dataset""": """wiki40b""", """config_name""": """en"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
{"""dataset""": """natural_questions""", """config_name""": """default"""},
]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__=True ) -> Dict:
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__lowercase ) )
class A_ ( __lowercase ):
_UpperCAmelCase : Union[str, Any] = None
_UpperCAmelCase : Dict = None
def lowerCAmelCase ( self : str ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : List[Any]):
with TemporaryDirectory() as tmp_dir:
__lowerCamelCase : int = dataset_module_factory(UpperCAmelCase__ ,cache_dir=UpperCAmelCase__)
__lowerCamelCase : Union[str, Any] = import_main_class(dataset_module.module_path ,dataset=UpperCAmelCase__)
__lowerCamelCase : Any = builder_cls(
cache_dir=UpperCAmelCase__ ,config_name=UpperCAmelCase__ ,hash=dataset_module.hash ,)
__lowerCamelCase : int = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=UpperCAmelCase__).replace(os.sep ,'/'),
config.DATASET_INFO_FILENAME,
])
__lowerCamelCase : Union[str, Any] = cached_path(UpperCAmelCase__ ,cache_dir=UpperCAmelCase__)
self.assertTrue(os.path.exists(UpperCAmelCase__))
@pytest.mark.integration
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Dict:
__lowerCamelCase : Dict = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
__lowerCamelCase : Any = dataset_module_factory('wikipedia' , cache_dir=lowerCamelCase__ )
__lowerCamelCase : List[Any] = import_main_class(dataset_module.module_path )
__lowerCamelCase : List[str] = builder_cls(
cache_dir=lowerCamelCase__ , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
__lowerCamelCase : str = None
builder_instance.download_and_prepare()
__lowerCamelCase : Optional[Any] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Any:
__lowerCamelCase : Optional[Any] = dataset_module_factory('wikipedia' , cache_dir=lowerCamelCase__ )
__lowerCamelCase : Optional[int] = import_main_class(dataset_module.module_path , dataset=lowerCamelCase__ )
__lowerCamelCase : str = builder_cls(
cache_dir=lowerCamelCase__ , config_name='20220301.frr' , hash=dataset_module.hash , )
__lowerCamelCase : Dict = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert "train" in ds
assert isinstance(ds['train'] , lowerCamelCase__ )
assert next(iter(ds['train'] ) )
| 73 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__snake_case =logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : bool = field(default=__lowercase , metadata={'''help''': '''Whether to use SortishSampler or not.'''} )
lowerCamelCase : bool = field(
default=__lowercase , metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''} )
lowerCamelCase : Optional[int] = field(
default=__lowercase , metadata={
'''help''': (
'''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
'''to the `max_length` value of the model configuration.'''
)
} , )
lowerCamelCase : Optional[int] = field(
default=__lowercase , metadata={
'''help''': (
'''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
'''to the `num_beams` value of the model configuration.'''
)
} , )
lowerCamelCase : Optional[Union[str, Path, GenerationConfig]] = field(
default=__lowercase , metadata={
'''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
} , )
def __UpperCAmelCase ( self : Dict ) -> List[str]:
lowerCAmelCase = super().to_dict()
for k, v in d.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowerCAmelCase = v.to_dict()
return d
| 4 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'''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 A ( A_ ):
UpperCamelCase_ : Dict ='''deformable_detr'''
UpperCamelCase_ : Optional[Any] ={
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__(self , lowerCAmelCase=True , lowerCAmelCase=None , lowerCAmelCase=3 , lowerCAmelCase=3_0_0 , lowerCAmelCase=1_0_2_4 , lowerCAmelCase=6 , lowerCAmelCase=1_0_2_4 , lowerCAmelCase=8 , lowerCAmelCase=6 , lowerCAmelCase=1_0_2_4 , lowerCAmelCase=8 , lowerCAmelCase=0.0 , lowerCAmelCase=True , lowerCAmelCase="relu" , lowerCAmelCase=2_5_6 , lowerCAmelCase=0.1 , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=0.02 , lowerCAmelCase=1.0 , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase="sine" , lowerCAmelCase="resnet50" , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase=4 , lowerCAmelCase=4 , lowerCAmelCase=4 , lowerCAmelCase=False , lowerCAmelCase=3_0_0 , lowerCAmelCase=False , lowerCAmelCase=1 , lowerCAmelCase=5 , lowerCAmelCase=2 , lowerCAmelCase=1 , lowerCAmelCase=1 , lowerCAmelCase=5 , lowerCAmelCase=2 , lowerCAmelCase=0.1 , lowerCAmelCase=0.25 , lowerCAmelCase=False , **lowerCAmelCase , ):
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= CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
__lowercase= backbone_config.get('model_type' )
__lowercase= CONFIG_MAPPING[backbone_model_type]
__lowercase= config_class.from_dict(lowerCAmelCase )
__lowercase= use_timm_backbone
__lowercase= backbone_config
__lowercase= num_channels
__lowercase= num_queries
__lowercase= max_position_embeddings
__lowercase= d_model
__lowercase= encoder_ffn_dim
__lowercase= encoder_layers
__lowercase= encoder_attention_heads
__lowercase= decoder_ffn_dim
__lowercase= decoder_layers
__lowercase= decoder_attention_heads
__lowercase= dropout
__lowercase= attention_dropout
__lowercase= activation_dropout
__lowercase= activation_function
__lowercase= init_std
__lowercase= init_xavier_std
__lowercase= encoder_layerdrop
__lowercase= auxiliary_loss
__lowercase= position_embedding_type
__lowercase= backbone
__lowercase= use_pretrained_backbone
__lowercase= dilation
# deformable attributes
__lowercase= num_feature_levels
__lowercase= encoder_n_points
__lowercase= decoder_n_points
__lowercase= two_stage
__lowercase= two_stage_num_proposals
__lowercase= 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= class_cost
__lowercase= bbox_cost
__lowercase= giou_cost
# Loss coefficients
__lowercase= mask_loss_coefficient
__lowercase= dice_loss_coefficient
__lowercase= bbox_loss_coefficient
__lowercase= giou_loss_coefficient
__lowercase= eos_coefficient
__lowercase= focal_alpha
__lowercase= disable_custom_kernels
super().__init__(is_encoder_decoder=lowerCAmelCase , **lowerCAmelCase )
@property
def _A (self ):
return self.encoder_attention_heads
@property
def _A (self ):
return self.d_model
def _A (self ):
__lowercase= copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
__lowercase= self.backbone_config.to_dict()
__lowercase= self.__class__.model_type
return output
| 369 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = None , ) -> int:
'''simple docstring'''
__lowercase= {}
if train_file is not None:
__lowercase= [train_file]
if eval_file is not None:
__lowercase= [eval_file]
if test_file is not None:
__lowercase= [test_file]
__lowercase= datasets.load_dataset('csv' , data_files=lowercase__ )
__lowercase= list(ds[list(files.keys() )[0]].features.keys() )
__lowercase= features_name.pop(lowercase__ )
__lowercase= list(set(ds[list(files.keys() )[0]][label_name] ) )
__lowercase= {label: i for i, label in enumerate(lowercase__ )}
__lowercase= tokenizer.model_input_names
__lowercase= {}
if len(lowercase__ ) == 1:
for k in files.keys():
__lowercase= ds[k].map(
lambda lowercase__ : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=lowercase__ , max_length=lowercase__ , padding='max_length' ) , batched=lowercase__ , )
elif len(lowercase__ ) == 2:
for k in files.keys():
__lowercase= ds[k].map(
lambda lowercase__ : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=lowercase__ , max_length=lowercase__ , padding='max_length' , ) , batched=lowercase__ , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
__lowercase= {k: v for k, v in ex.items() if k in input_names}
__lowercase= labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
__lowercase= {k: v for k, v in ex.items() if k in input_names}
__lowercase= labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
__lowercase= {k: v for k, v in ex.items() if k in input_names}
__lowercase= labelaid[ex[label_name]]
yield (d, label)
__lowercase= (
tf.data.Dataset.from_generator(
lowercase__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
__lowercase= train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
__lowercase= (
tf.data.Dataset.from_generator(
lowercase__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
__lowercase= val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
__lowercase= (
tf.data.Dataset.from_generator(
lowercase__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
__lowercase= test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
lowerCAmelCase = logging.getLogger(__name__)
@dataclass
class A :
UpperCamelCase_ : int =field(metadata={'''help''': '''Which column contains the label'''} )
UpperCamelCase_ : str =field(default=A_ , metadata={'''help''': '''The path of the training file'''} )
UpperCamelCase_ : Optional[str] =field(default=A_ , metadata={'''help''': '''The path of the development file'''} )
UpperCamelCase_ : Optional[str] =field(default=A_ , metadata={'''help''': '''The path of the test file'''} )
UpperCamelCase_ : int =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_ : bool =field(
default=A_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
@dataclass
class A :
UpperCamelCase_ : str =field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase_ : Optional[str] =field(
default=A_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] =field(
default=A_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase_ : bool =field(default=A_ , metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
UpperCamelCase_ : Optional[str] =field(
default=A_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
def _lowerCamelCase( ) -> Optional[Any]:
'''simple docstring'''
__lowercase= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
__lowercase, __lowercase, __lowercase= parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
' --overwrite_output_dir to overcome.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.info(
F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '
F'16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowercase= AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
__lowercase, __lowercase, __lowercase, __lowercase= get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=lowercase__ , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
__lowercase= AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(lowercase__ ) , labelaid=lowercase__ , idalabel={id: label for label, id in labelaid.items()} , finetuning_task='text-classification' , cache_dir=model_args.cache_dir , )
with training_args.strategy.scope():
__lowercase= TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool('.bin' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , )
def compute_metrics(lowercase__ ) -> Dict:
__lowercase= np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
__lowercase= TFTrainer(
model=lowercase__ , args=lowercase__ , train_dataset=lowercase__ , eval_dataset=lowercase__ , compute_metrics=lowercase__ , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase= {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__lowercase= trainer.evaluate()
__lowercase= os.path.join(training_args.output_dir , 'eval_results.txt' )
with open(lowercase__ , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in result.items():
logger.info(F' {key} = {value}' )
writer.write(F'{key} = {value}\n' )
results.update(lowercase__ )
return results
if __name__ == "__main__":
main()
| 304 | 0 |
import comet # From: unbabel-comet
import torch
import datasets
A__ = datasets.logging.get_logger(__name__)
A__ = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel's Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
author = \"Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon\",
booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
month = nov,
year = \"2020\",
address = \"Online\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
pages = \"2685--2702\",
}
"""
A__ = """\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""
A__ = """
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric('comet')
>>> # comet_metric = load_metric('comet', 'wmt20-comet-da') # you can also choose which model to use
>>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
>>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
>>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results[\"scores\"]])
[0.19, 0.92]
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def snake_case ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://unbabel.github.io/COMET/html/index.html""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""sources""": datasets.Value("""string""" , id="""sequence""" ),
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/Unbabel/COMET"""] , reference_urls=[
"""https://github.com/Unbabel/COMET""",
"""https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
"""http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
] , )
def snake_case ( self , _snake_case ):
"""simple docstring"""
if self.config_name == "default":
_lowerCAmelCase = comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da""" ) )
else:
_lowerCAmelCase = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case=None , _snake_case=False ):
"""simple docstring"""
if gpus is None:
_lowerCAmelCase = 1 if torch.cuda.is_available() else 0
_lowerCAmelCase = {"""src""": sources, """mt""": predictions, """ref""": references}
_lowerCAmelCase = [dict(zip(_snake_case , _snake_case ) ) for t in zip(*data.values() )]
_lowerCAmelCase , _lowerCAmelCase = self.scorer.predict(_snake_case , gpus=_snake_case , progress_bar=_snake_case )
return {"mean_score": mean_score, "scores": scores}
| 82 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Tuple = '''▁'''
snake_case : Any = {'''vocab_file''': '''sentencepiece.bpe.model'''}
snake_case : Tuple = {
'''vocab_file''': {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large-finetuned-conll02-dutch''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll02-spanish''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-english''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-german''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
),
}
}
snake_case : int = {
'''xlm-roberta-base''': 5_12,
'''xlm-roberta-large''': 5_12,
'''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
'''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
'''xlm-roberta-large-finetuned-conll03-english''': 5_12,
'''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['input_ids', 'attention_mask']
def __init__( self , _lowerCamelCase , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<mask>" , _lowerCamelCase = None , **_lowerCamelCase , ):
# Mask token behave like a normal word, i.e. include the space before it
a :Optional[int] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else mask_token
a :int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
a :Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowerCamelCase ) )
a :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'
# Mimic fairseq token-to-id alignment for the first 4 token
a :Tuple = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
a :List[str] = 1
a :Dict = len(self.sp_model ) + self.fairseq_offset
a :List[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
a :List[str] = self.__dict__.copy()
a :Optional[int] = None
a :int = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , _lowerCamelCase ):
a :Union[str, Any] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
a :Union[str, Any] = {}
a :Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
a :List[Any] = [self.cls_token_id]
a :Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(_lowerCamelCase )) + [1]
return [1] + ([0] * len(_lowerCamelCase )) + [1, 1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None ):
a :int = [self.sep_token_id]
a :int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def SCREAMING_SNAKE_CASE__ ( self ):
a :Any = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
a :Optional[Any] = self.sp_model.PieceToId(_lowerCamelCase )
# 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 , _lowerCamelCase ):
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 , _lowerCamelCase ):
a :Tuple = ''''''.join(_lowerCamelCase ).replace(_lowerCamelCase , ''' ''' ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a :int = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
a :List[Any] = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 94 | 0 |
from collections import defaultdict
def __UpperCamelCase ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str ):
__a : str = first_str.lower().strip()
__a : str = second_str.lower().strip()
# Remove whitespace
__a : List[Any] = first_str.replace(''' ''' , '''''' )
__a : Dict = second_str.replace(''' ''' , '''''' )
# Strings of different lengths are not anagrams
if len(lowerCamelCase_ ) != len(lowerCamelCase_ ):
return False
# Default values for count should be 0
__a : defaultdict[str, int] = defaultdict(lowerCamelCase_ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(lowerCamelCase_ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
lowercase__ =input('Enter the first string ').strip()
lowercase__ =input('Enter the second string ').strip()
lowercase__ =check_anagrams(input_a, input_b)
print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
| 368 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
lowercase__ ='bert-base-cased'
lowercase__ ='google/pegasus-xsum'
lowercase__ =[' Sam ate lunch today.', 'Sams lunch ingredients.']
lowercase__ =['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowercase__ ='patrickvonplaten/t5-tiny-random'
lowercase__ ='sshleifer/bart-tiny-random'
lowercase__ ='sshleifer/tiny-mbart'
lowercase__ ='sshleifer/tiny-marian-en-de'
def __UpperCamelCase ( lowerCAmelCase__ : Path , lowerCAmelCase__ : list ):
__a : List[Any] = '''\n'''.join(lowerCAmelCase__ )
Path(lowerCAmelCase__ ).open('''w''' ).writelines(lowerCAmelCase__ )
def __UpperCamelCase ( lowerCAmelCase__ : int ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(lowerCAmelCase__ , f"{split}.source" ) , lowerCAmelCase__ )
_dump_articles(os.path.join(lowerCAmelCase__ , f"{split}.target" ) , lowerCAmelCase__ )
return tmp_dir
class UpperCamelCase__ ( __lowercase ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def lowerCAmelCase (self : int , snake_case_ : int ):
__a : Optional[Any] = AutoTokenizer.from_pretrained(snake_case_ )
__a : Optional[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__a : Union[str, Any] = max(len(tokenizer.encode(snake_case_ ) ) for a in ARTICLES )
__a : str = max(len(tokenizer.encode(snake_case_ ) ) for a in SUMMARIES )
__a : str = 4
__a : Dict = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__a , __a : Any = '''ro_RO''', '''de_DE''' # ignored for all but mbart, but never causes error.
__a : List[Any] = SeqaSeqDataset(
snake_case_ , data_dir=snake_case_ , type_path='''train''' , max_source_length=snake_case_ , max_target_length=snake_case_ , src_lang=snake_case_ , tgt_lang=snake_case_ , )
__a : Dict = DataLoader(snake_case_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(snake_case_ , snake_case_ )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__a : Dict = shift_tokens_right(batch['''labels'''] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def lowerCAmelCase (self : Optional[Any] , snake_case_ : str ):
__a : Union[str, Any] = AutoTokenizer.from_pretrained(snake_case_ )
__a : str = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__a : Any = max(len(tokenizer.encode(snake_case_ ) ) for a in ARTICLES )
__a : Any = max(len(tokenizer.encode(snake_case_ ) ) for a in SUMMARIES )
__a : Dict = 4
__a : Optional[int] = LegacySeqaSeqDataset(
snake_case_ , data_dir=snake_case_ , type_path='''train''' , max_source_length=2_0 , max_target_length=snake_case_ , )
__a : Optional[Any] = DataLoader(snake_case_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 2_0 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def lowerCAmelCase (self : List[str] ):
__a : int = AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''' )
__a : Any = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
__a : Optional[int] = tmp_dir.joinpath('''train.source''' ).open().readlines()
__a : List[Any] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(snake_case_ , snake_case_ , 1_2_8 , snake_case_ )
__a : Optional[Any] = {x.name for x in tmp_dir.iterdir()}
__a : Union[str, Any] = {x.name for x in save_dir.iterdir()}
__a : str = save_dir.joinpath('''train.source''' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(snake_case_ ) < len(snake_case_ )
assert len(snake_case_ ) == 1
assert len(packed_examples[0] ) == sum(len(snake_case_ ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='''This test requires fairseq''' )
def lowerCAmelCase (self : Any ):
if not FAIRSEQ_AVAILABLE:
return
__a , __a , __a : Any = self._get_dataset(max_len=6_4 )
__a : int = 6_4
__a : List[str] = ds.make_dynamic_sampler(snake_case_ , required_batch_size_multiple=snake_case_ )
__a : List[str] = [len(snake_case_ ) for x in batch_sampler]
assert len(set(snake_case_ ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(snake_case_ ) == len(snake_case_ ) # no dropped or added examples
__a : Union[str, Any] = DataLoader(snake_case_ , batch_sampler=snake_case_ , collate_fn=ds.collate_fn , num_workers=2 )
__a : Tuple = []
__a : Union[str, Any] = []
for batch in data_loader:
__a : Any = batch['''input_ids'''].shape
__a : str = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__a : Optional[Any] = np.product(batch['''input_ids'''].shape )
num_src_per_batch.append(snake_case_ )
if num_src_tokens > (max_tokens * 1.1):
failures.append(snake_case_ )
assert num_src_per_batch[0] == max(snake_case_ )
if failures:
raise AssertionError(f"too many tokens in {len(snake_case_ )} batches" )
def lowerCAmelCase (self : int ):
__a , __a , __a : Optional[int] = self._get_dataset(max_len=5_1_2 )
__a : Union[str, Any] = 2
__a : str = ds.make_sortish_sampler(snake_case_ , shuffle=snake_case_ )
__a : Tuple = DataLoader(snake_case_ , batch_size=snake_case_ , collate_fn=ds.collate_fn , num_workers=2 )
__a : Tuple = DataLoader(snake_case_ , batch_size=snake_case_ , collate_fn=ds.collate_fn , num_workers=2 , sampler=snake_case_ )
__a : Optional[int] = tokenizer.pad_token_id
def count_pad_tokens(snake_case_ : Union[str, Any] , snake_case_ : List[str]="input_ids" ):
return [batch[k].eq(snake_case_ ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(snake_case_ , k='''labels''' ) ) < sum(count_pad_tokens(snake_case_ , k='''labels''' ) )
assert sum(count_pad_tokens(snake_case_ ) ) < sum(count_pad_tokens(snake_case_ ) )
assert len(snake_case_ ) == len(snake_case_ )
def lowerCAmelCase (self : int , snake_case_ : int=1_0_0_0 , snake_case_ : Optional[Any]=1_2_8 ):
if os.getenv('''USE_REAL_DATA''' , snake_case_ ):
__a : Optional[int] = '''examples/seq2seq/wmt_en_ro'''
__a : List[Any] = max_len * 2 * 6_4
if not Path(snake_case_ ).joinpath('''train.len''' ).exists():
save_len_file(snake_case_ , snake_case_ )
else:
__a : int = '''examples/seq2seq/test_data/wmt_en_ro'''
__a : List[str] = max_len * 4
save_len_file(snake_case_ , snake_case_ )
__a : str = AutoTokenizer.from_pretrained(snake_case_ )
__a : Optional[int] = SeqaSeqDataset(
snake_case_ , data_dir=snake_case_ , type_path='''train''' , max_source_length=snake_case_ , max_target_length=snake_case_ , n_obs=snake_case_ , )
return ds, max_tokens, tokenizer
def lowerCAmelCase (self : List[str] ):
__a , __a , __a : str = self._get_dataset()
__a : Optional[Any] = set(DistributedSortishSampler(snake_case_ , 2_5_6 , num_replicas=2 , rank=0 , add_extra_examples=snake_case_ ) )
__a : Tuple = set(DistributedSortishSampler(snake_case_ , 2_5_6 , num_replicas=2 , rank=1 , add_extra_examples=snake_case_ ) )
assert idsa.intersection(snake_case_ ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def lowerCAmelCase (self : str , snake_case_ : Union[str, Any] ):
__a : Union[str, Any] = AutoTokenizer.from_pretrained(snake_case_ , use_fast=snake_case_ )
if tok_name == MBART_TINY:
__a : Any = SeqaSeqDataset(
snake_case_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , src_lang='''EN''' , tgt_lang='''FR''' , )
__a : Tuple = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__a : Optional[Any] = SeqaSeqDataset(
snake_case_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , )
__a : List[Any] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(snake_case_ ) == 1 if tok_name == BART_TINY else len(snake_case_ ) == 0
| 90 | 0 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def __lowerCAmelCase ( UpperCamelCase__=None ) -> Tuple:
if subparsers is not None:
__lowerCamelCase = subparsers.add_parser('''env''' )
else:
__lowerCamelCase = argparse.ArgumentParser('''Accelerate env command''' )
parser.add_argument(
'''--config_file''' , default=UpperCamelCase__ , help='''The config file to use for the default values in the launching script.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCamelCase__ )
return parser
def __lowerCAmelCase ( UpperCamelCase__ ) -> Union[str, Any]:
__lowerCamelCase = torch.__version__
__lowerCamelCase = torch.cuda.is_available()
__lowerCamelCase = is_xpu_available()
__lowerCamelCase = is_npu_available()
__lowerCamelCase = '''Not found'''
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(UpperCamelCase__ ):
__lowerCamelCase = load_config_from_file(args.config_file ).to_dict()
__lowerCamelCase = {
'''`Accelerate` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Numpy version''': np.__version__,
'''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
'''PyTorch XPU available''': str(UpperCamelCase__ ),
'''PyTorch NPU available''': str(UpperCamelCase__ ),
'''System RAM''': f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
}
if pt_cuda_available:
__lowerCamelCase = torch.cuda.get_device_name()
print('''\nCopy-and-paste the text below in your GitHub issue\n''' )
print('''\n'''.join([f"""- {prop}: {val}""" for prop, val in info.items()] ) )
print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' )
__lowerCamelCase = (
'''\n'''.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
if isinstance(UpperCamelCase__ , UpperCamelCase__ )
else f"""\t{accelerate_config}"""
)
print(UpperCamelCase__ )
__lowerCamelCase = accelerate_config
return info
def __lowerCAmelCase ( ) -> int:
__lowerCamelCase = env_command_parser()
__lowerCamelCase = parser.parse_args()
env_command(UpperCamelCase__ )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 67 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Optional[int] ="gpt_neox_japanese"
def __init__( self : List[Any] , a : Tuple=3_20_00 , a : Dict=25_60 , a : Union[str, Any]=32 , a : Dict=32 , a : Dict=4 , a : Optional[Any]="gelu" , a : Any=1.00 , a : str=1_00_00 , a : List[str]=20_48 , a : str=0.02 , a : Union[str, Any]=1e-5 , a : Optional[Any]=True , a : str=3_19_96 , a : List[str]=3_19_99 , a : str=0.1 , a : Union[str, Any]=0.0 , **a : Optional[Any] , ):
"""simple docstring"""
super().__init__(bos_token_id=a , eos_token_id=a , **a )
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_multiple_size
__lowerCamelCase = hidden_act
__lowerCamelCase = rotary_pct
__lowerCamelCase = rotary_emb_base
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = use_cache
__lowerCamelCase = attention_dropout
__lowerCamelCase = hidden_dropout
| 67 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int = 1000 ):
"""simple docstring"""
return sum(e for e in range(3 , lowerCamelCase_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 362 | '''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('''TEST_SAGEMAKER''' , '''False''' ) ) is not True , reason='''Skipping test because should only be run when releasing minor transformers version''' , )
@pytest.mark.usefixtures('''sm_env''' )
@parameterized_class(
[
{
'''framework''': '''pytorch''',
'''script''': '''run_glue.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.g4dn.xlarge''',
'''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
},
{
'''framework''': '''tensorflow''',
'''script''': '''run_tf.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.g4dn.xlarge''',
'''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
},
] )
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split() , encoding='utf-8' , check=snake_case_ , )
assert hasattr(self , 'env' )
def _UpperCamelCase ( self , snake_case_=1 ):
'''simple docstring'''
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F'''{self.env.base_job_name}-single''' , instance_count=snake_case_ , instance_type=self.instance_type , debugger_hook_config=snake_case_ , hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version='py36' , )
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
TrainingJobAnalytics(snake_case_ ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = self.create_estimator()
# run training
estimator.fit()
# result dataframe
UpperCAmelCase_ : int = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
UpperCAmelCase_ : Dict = list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
UpperCAmelCase_ : List[str] = list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
UpperCAmelCase_ : int = (
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds' , 9_9_9_9_9_9 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F'''{estimator.latest_training_job.name}.json''' , 'w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} , snake_case_ )
| 274 | 0 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __lowerCamelCase ( UpperCAmelCase_ : List[str] ):
"""simple docstring"""
a :str = tmp_path / '''file.csv'''
a :List[str] = textwrap.dedent(
'''\
header1,header2
1,2
10,20
''' )
with open(UpperCAmelCase_ , '''w''' ) as f:
f.write(UpperCAmelCase_ )
return str(UpperCAmelCase_ )
@pytest.fixture
def __lowerCamelCase ( UpperCAmelCase_ : List[str] ):
"""simple docstring"""
a :Optional[Any] = tmp_path / '''malformed_file.csv'''
a :Optional[Any] = textwrap.dedent(
'''\
header1,header2
1,2
10,20,
''' )
with open(UpperCAmelCase_ , '''w''' ) as f:
f.write(UpperCAmelCase_ )
return str(UpperCAmelCase_ )
@pytest.fixture
def __lowerCamelCase ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : int ):
"""simple docstring"""
a :List[Any] = tmp_path / '''csv_with_image.csv'''
a :Tuple = textwrap.dedent(
F'''\
image
{image_file}
''' )
with open(UpperCAmelCase_ , '''w''' ) as f:
f.write(UpperCAmelCase_ )
return str(UpperCAmelCase_ )
@pytest.fixture
def __lowerCamelCase ( UpperCAmelCase_ : Optional[int] ):
"""simple docstring"""
a :List[Any] = tmp_path / '''csv_with_label.csv'''
a :Dict = textwrap.dedent(
'''\
label
good
bad
good
''' )
with open(UpperCAmelCase_ , '''w''' ) as f:
f.write(UpperCAmelCase_ )
return str(UpperCAmelCase_ )
@pytest.fixture
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
a :int = tmp_path / '''csv_with_int_list.csv'''
a :Tuple = textwrap.dedent(
'''\
int_list
1 2 3
4 5 6
7 8 9
''' )
with open(UpperCAmelCase_ , '''w''' ) as f:
f.write(UpperCAmelCase_ )
return str(UpperCAmelCase_ )
def __lowerCamelCase ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[Any] ):
"""simple docstring"""
a :str = Csv()
a :Optional[int] = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(UpperCAmelCase_ , match='''Error tokenizing data''' ):
for _ in generator:
pass
assert any(
record.levelname == '''ERROR'''
and '''Failed to read file''' in record.message
and os.path.basename(UpperCAmelCase_ ) in record.message
for record in caplog.records )
@require_pil
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
with open(UpperCAmelCase_ , encoding='''utf-8''' ) as f:
a :Any = f.read().splitlines()[1]
a :Union[str, Any] = Csv(encoding='''utf-8''' , features=Features({'''image''': Image()} ) )
a :Dict = csv._generate_tables([[csv_file_with_image]] )
a :Union[str, Any] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''image''' ).type == Image()()
a :Dict = pa_table.to_pydict()['''image''']
assert generated_content == [{"path": image_file, "bytes": None}]
def __lowerCamelCase ( UpperCAmelCase_ : Optional[Any] ):
"""simple docstring"""
with open(UpperCAmelCase_ , encoding='''utf-8''' ) as f:
a :List[str] = f.read().splitlines()[1:]
a :int = Csv(encoding='''utf-8''' , features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
a :int = csv._generate_tables([[csv_file_with_label]] )
a :str = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
a :Union[str, Any] = pa_table.to_pydict()['''label''']
assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(UpperCAmelCase_ ) for label in labels]
def __lowerCamelCase ( UpperCAmelCase_ : Tuple ):
"""simple docstring"""
a :str = Csv(encoding='''utf-8''' , sep=''',''' , converters={'''int_list''': lambda UpperCAmelCase_ : [int(UpperCAmelCase_ ) for i in x.split()]} )
a :Optional[Any] = csv._generate_tables([[csv_file_with_int_list]] )
a :Optional[Any] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
a :int = pa_table.to_pydict()['''int_list''']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 94 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __A:
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=30 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , ):
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = image_size
UpperCamelCase__ = patch_size
UpperCamelCase__ = num_channels
UpperCamelCase__ = is_training
UpperCamelCase__ = use_labels
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_act
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = type_sequence_label_size
UpperCamelCase__ = initializer_range
UpperCamelCase__ = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCamelCase__ = (image_size // patch_size) ** 2
UpperCamelCase__ = num_patches + 1
def UpperCAmelCase_ (self ):
UpperCamelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ = None
if self.use_labels:
UpperCamelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase__ = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ (self ):
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = TFViTModel(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
UpperCamelCase__ = self.image_size // 2
UpperCamelCase__ = pixel_values[:, :, :image_size, :image_size]
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , interpolate_pos_encoding=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self.type_sequence_label_size
UpperCamelCase__ = TFViTForImageClassification(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
UpperCamelCase__ = self.image_size // 2
UpperCamelCase__ = pixel_values[:, :, :image_size, :image_size]
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , interpolate_pos_encoding=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCamelCase__ = 1
UpperCamelCase__ = TFViTForImageClassification(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = config_and_inputs
UpperCamelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __A( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE__ = (
{"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
def UpperCAmelCase_ (self ):
UpperCamelCase__ = TFViTModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCAmelCase_ (self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def UpperCAmelCase_ (self ):
pass
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def UpperCAmelCase_ (self ):
pass
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ = model_class(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
UpperCamelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_ , tf.keras.layers.Layer ) )
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ = model_class(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ = [*signature.parameters.keys()]
UpperCamelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = TFViTModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __A( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ (self ):
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" )
UpperCamelCase__ = self.default_image_processor
UpperCamelCase__ = prepare_img()
UpperCamelCase__ = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="""tf""" )
# forward pass
UpperCamelCase__ = model(**SCREAMING_SNAKE_CASE_ )
# verify the logits
UpperCamelCase__ = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tf.constant([-0.2744, 0.8215, -0.0836] )
tf.debugging.assert_near(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 )
| 244 | 0 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
_SCREAMING_SNAKE_CASE = [
"""python""",
"""tqdm""",
"""regex""",
"""requests""",
"""packaging""",
"""filelock""",
"""numpy""",
"""tokenizers""",
"""huggingface-hub""",
"""safetensors""",
"""accelerate""",
"""pyyaml""",
]
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
elif pkg == "accelerate":
# must be loaded here, or else tqdm check may fail
from .utils import is_accelerate_available
# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
# Transformers with PyTorch
if not is_accelerate_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
require_version(deps[pkg] , __a )
| 88 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
Args:
predictions: List of predicted texts.
references: List of reference texts.
regexes_to_ignore: List, defaults to None. Regex expressions of characters to
ignore when calculating the exact matches. Note: these regexes are removed
from the input data before the changes based on the options below (e.g. ignore_case,
ignore_punctuation, ignore_numbers) are applied.
ignore_case: Boolean, defaults to False. If true, turns everything
to lowercase so that capitalization differences are ignored.
ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
Returns:
exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results[\"exact_match\"], 1))
25.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results[\"exact_match\"], 1))
50.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results[\"exact_match\"], 1))
75.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
>>> print(round(results[\"exact_match\"], 1))
100.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
>>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results[\"exact_match\"], 1))
33.3
"""
_SCREAMING_SNAKE_CASE = """
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE_ ( datasets.Metric ):
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , reference_urls=[] , )
def UpperCAmelCase_ ( self : List[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Optional[int]=None , _A : Dict=False , _A : Dict=False , _A : Optional[Any]=False , ) -> List[str]:
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
snake_case_ : List[str] = np.array([re.sub(_A , '' , _A ) for x in predictions] )
snake_case_ : int = np.array([re.sub(_A , '' , _A ) for x in references] )
else:
snake_case_ : Optional[Any] = np.asarray(_A )
snake_case_ : Optional[Any] = np.asarray(_A )
if ignore_case:
snake_case_ : int = np.char.lower(_A )
snake_case_ : List[str] = np.char.lower(_A )
if ignore_punctuation:
snake_case_ : str = string.punctuation.maketrans('' , '' , string.punctuation )
snake_case_ : str = np.char.translate(_A , table=_A )
snake_case_ : Any = np.char.translate(_A , table=_A )
if ignore_numbers:
snake_case_ : int = string.digits.maketrans('' , '' , string.digits )
snake_case_ : Tuple = np.char.translate(_A , table=_A )
snake_case_ : Optional[Any] = np.char.translate(_A , table=_A )
snake_case_ : Optional[Any] = predictions == references
return {"exact_match": np.mean(_A ) * 100}
| 88 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , **UpperCamelCase__ , ) -> List[Any]:
super().__init__(features=UpperCamelCase__ , cache_dir=UpperCamelCase__ , keep_in_memory=UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase : str = Sql(
cache_dir=UpperCamelCase__ , features=UpperCamelCase__ , sql=UpperCamelCase__ , con=UpperCamelCase__ , **UpperCamelCase__ , )
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : List[Any] = None
lowerCamelCase : List[str] = None
lowerCamelCase : int = None
lowerCamelCase : Optional[Any] = None
self.builder.download_and_prepare(
download_config=UpperCamelCase__ , download_mode=UpperCamelCase__ , verification_mode=UpperCamelCase__ , base_path=UpperCamelCase__ , )
# Build dataset for splits
lowerCamelCase : Any = self.builder.as_dataset(
split="train" , verification_mode=UpperCamelCase__ , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Any:
if num_proc is not None and num_proc <= 0:
raise ValueError(F'''num_proc {num_proc} must be an integer > 0.''' )
lowerCamelCase : int = dataset
lowerCamelCase : int = name
lowerCamelCase : Optional[int] = con
lowerCamelCase : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
lowerCamelCase : int = num_proc
lowerCamelCase : int = to_sql_kwargs
def _lowercase ( self ) -> int:
lowerCamelCase : Optional[Any] = self.to_sql_kwargs.pop("sql" , UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = self.to_sql_kwargs.pop("con" , UpperCamelCase__ )
lowerCamelCase : str = self.to_sql_kwargs.pop("index" , UpperCamelCase__ )
lowerCamelCase : str = self._write(index=UpperCamelCase__ , **self.to_sql_kwargs )
return written
def _lowercase ( self , UpperCamelCase__ ) -> str:
lowerCamelCase , lowerCamelCase , lowerCamelCase : Tuple = args
lowerCamelCase : Tuple = {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
lowerCamelCase : List[str] = query_table(
table=self.dataset.data , key=slice(UpperCamelCase__ , offset + self.batch_size ) , indices=self.dataset._indices , )
lowerCamelCase : List[Any] = batch.to_pandas()
lowerCamelCase : Any = df.to_sql(self.name , self.con , index=UpperCamelCase__ , **UpperCamelCase__ )
return num_rows or len(UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> int:
lowerCamelCase : Tuple = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
lowerCamelCase , lowerCamelCase : Tuple = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , UpperCamelCase__ , UpperCamelCase__ )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += num_rows
return written
| 48 |
"""simple docstring"""
import baseaa
def _A ( UpperCamelCase_ : str) -> bytes:
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8"))
def _A ( UpperCamelCase_ : bytes) -> str:
'''simple docstring'''
return baseaa.baadecode(UpperCamelCase_).decode("utf-8")
if __name__ == "__main__":
_a = 'Hello World!'
_a = baseaa_encode(test)
print(encoded)
_a = baseaa_decode(encoded)
print(decoded)
| 17 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase = 200 ):
__UpperCAmelCase : Optional[Any] = [1, 2, 5, 10, 20, 50, 100, 200]
__UpperCAmelCase : int = [0] * (pence + 1)
__UpperCAmelCase : List[Any] = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(__lowerCamelCase, pence + 1, 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(2_00) == 7_36_82
| 367 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
class SCREAMING_SNAKE_CASE__ ( snake_case__ ,snake_case__ ):
"""simple docstring"""
@register_to_config
def __init__( self : Union[str, Any] , UpperCAmelCase_ : int = 65_536 , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : int = 2 , UpperCAmelCase_ : int = 2 , UpperCAmelCase_ : int = 0 , UpperCAmelCase_ : str = "fourier" , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , UpperCAmelCase_ : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , UpperCAmelCase_ : Tuple[str] = "UNetMidBlock1D" , UpperCAmelCase_ : str = None , UpperCAmelCase_ : Tuple[int] = (32, 32, 64) , UpperCAmelCase_ : str = None , UpperCAmelCase_ : int = 8 , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : bool = False , ):
"""simple docstring"""
super().__init__()
__UpperCAmelCase : str = sample_size
# time
if time_embedding_type == "fourier":
__UpperCAmelCase : int = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=UpperCAmelCase_ , log=UpperCAmelCase_ , flip_sin_to_cos=UpperCAmelCase_ )
__UpperCAmelCase : str = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
__UpperCAmelCase : str = Timesteps(
block_out_channels[0] , flip_sin_to_cos=UpperCAmelCase_ , downscale_freq_shift=UpperCAmelCase_ )
__UpperCAmelCase : Dict = block_out_channels[0]
if use_timestep_embedding:
__UpperCAmelCase : Union[str, Any] = block_out_channels[0] * 4
__UpperCAmelCase : str = TimestepEmbedding(
in_channels=UpperCAmelCase_ , time_embed_dim=UpperCAmelCase_ , act_fn=UpperCAmelCase_ , out_dim=block_out_channels[0] , )
__UpperCAmelCase : Tuple = nn.ModuleList([] )
__UpperCAmelCase : int = None
__UpperCAmelCase : Optional[Any] = nn.ModuleList([] )
__UpperCAmelCase : Dict = None
# down
__UpperCAmelCase : str = in_channels
for i, down_block_type in enumerate(UpperCAmelCase_ ):
__UpperCAmelCase : Optional[Any] = output_channel
__UpperCAmelCase : Optional[int] = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
__UpperCAmelCase : Tuple = i == len(UpperCAmelCase_ ) - 1
__UpperCAmelCase : List[str] = get_down_block(
UpperCAmelCase_ , num_layers=UpperCAmelCase_ , in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(UpperCAmelCase_ )
# mid
__UpperCAmelCase : Optional[Any] = get_mid_block(
UpperCAmelCase_ , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=UpperCAmelCase_ , add_downsample=UpperCAmelCase_ , )
# up
__UpperCAmelCase : Tuple = list(reversed(UpperCAmelCase_ ) )
__UpperCAmelCase : Any = reversed_block_out_channels[0]
if out_block_type is None:
__UpperCAmelCase : Union[str, Any] = out_channels
else:
__UpperCAmelCase : Dict = block_out_channels[0]
for i, up_block_type in enumerate(UpperCAmelCase_ ):
__UpperCAmelCase : int = output_channel
__UpperCAmelCase : str = (
reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase_ ) - 1 else final_upsample_channels
)
__UpperCAmelCase : Tuple = i == len(UpperCAmelCase_ ) - 1
__UpperCAmelCase : Dict = get_up_block(
UpperCAmelCase_ , num_layers=UpperCAmelCase_ , in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(UpperCAmelCase_ )
__UpperCAmelCase : Union[str, Any] = output_channel
# out
__UpperCAmelCase : Optional[int] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
__UpperCAmelCase : List[Any] = get_out_block(
out_block_type=UpperCAmelCase_ , num_groups_out=UpperCAmelCase_ , embed_dim=block_out_channels[0] , out_channels=UpperCAmelCase_ , act_fn=UpperCAmelCase_ , fc_dim=block_out_channels[-1] // 4 , )
def lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase_ : torch.FloatTensor , UpperCAmelCase_ : Union[torch.Tensor, float, int] , UpperCAmelCase_ : bool = True , ):
"""simple docstring"""
__UpperCAmelCase : Dict = timestep
if not torch.is_tensor(UpperCAmelCase_ ):
__UpperCAmelCase : List[str] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(UpperCAmelCase_ ) and len(timesteps.shape ) == 0:
__UpperCAmelCase : List[str] = timesteps[None].to(sample.device )
__UpperCAmelCase : List[str] = self.time_proj(UpperCAmelCase_ )
if self.config.use_timestep_embedding:
__UpperCAmelCase : Any = self.time_mlp(UpperCAmelCase_ )
else:
__UpperCAmelCase : Any = timestep_embed[..., None]
__UpperCAmelCase : int = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
__UpperCAmelCase : Dict = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
__UpperCAmelCase : int = ()
for downsample_block in self.down_blocks:
__UpperCAmelCase , __UpperCAmelCase : int = downsample_block(hidden_states=UpperCAmelCase_ , temb=UpperCAmelCase_ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
__UpperCAmelCase : List[str] = self.mid_block(UpperCAmelCase_ , UpperCAmelCase_ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
__UpperCAmelCase : Any = down_block_res_samples[-1:]
__UpperCAmelCase : List[Any] = down_block_res_samples[:-1]
__UpperCAmelCase : str = upsample_block(UpperCAmelCase_ , res_hidden_states_tuple=UpperCAmelCase_ , temb=UpperCAmelCase_ )
# 5. post-process
if self.out_block:
__UpperCAmelCase : Tuple = self.out_block(UpperCAmelCase_ , UpperCAmelCase_ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=UpperCAmelCase_ )
| 37 | 0 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase__ : Dict = mf_knapsack(i - 1 , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
lowercase__ : Optional[Any] = max(
mf_knapsack(i - 1 , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) , mf_knapsack(i - 1 , UpperCAmelCase , UpperCAmelCase , j - wt[i - 1] ) + val[i - 1] , )
lowercase__ : str = val
return f[i][j]
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
lowercase__ : str = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
lowercase__ : Union[str, Any] = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
lowercase__ : Union[str, Any] = dp[i - 1][w_]
return dp[n][w_], dp
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
if not (isinstance(UpperCAmelCase , (list, tuple) ) and isinstance(UpperCAmelCase , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
lowercase__ : Tuple = len(UpperCAmelCase )
if num_items != len(UpperCAmelCase ):
lowercase__ : List[str] = (
'''The number of weights must be the same as the number of values.\n'''
F"""But got {num_items} weights and {len(UpperCAmelCase )} values"""
)
raise ValueError(UpperCAmelCase )
for i in range(UpperCAmelCase ):
if not isinstance(wt[i] , UpperCAmelCase ):
lowercase__ : List[Any] = (
'''All weights must be integers but got weight of '''
F"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(UpperCAmelCase )
lowercase__ , lowercase__ : Union[str, Any] = knapsack(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
lowercase__ : set = set()
_construct_solution(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return optimal_val, example_optional_set
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(UpperCAmelCase , UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
else:
optimal_set.add(UpperCAmelCase )
_construct_solution(UpperCAmelCase , UpperCAmelCase , i - 1 , j - wt[i - 1] , UpperCAmelCase )
if __name__ == "__main__":
__a: Tuple = [3, 2, 4, 4]
__a: Tuple = [4, 3, 2, 3]
__a: int = 4
__a: List[str] = 6
__a: int = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__a , __a: Optional[Any] = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__a , __a: Optional[Any] = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("""optimal_value = """, optimal_solution)
print("""An optimal subset corresponding to the optimal value""", optimal_subset)
| 198 | '''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
from transformers.models.mobilevitva.modeling_mobilevitva import (
MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
make_divisible,
)
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class UpperCAmelCase ( a__ ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ : Union[str, Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowerCAmelCase , '''width_multiplier''' ) )
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=13 , __lowerCAmelCase=64 , __lowerCAmelCase=2 , __lowerCAmelCase=3 , __lowerCAmelCase="swish" , __lowerCAmelCase=3 , __lowerCAmelCase=32 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.0_2 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=10 , __lowerCAmelCase=None , __lowerCAmelCase=0.2_5 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , ) -> List[Any]:
lowercase__ : List[str] = parent
lowercase__ : List[Any] = batch_size
lowercase__ : List[str] = image_size
lowercase__ : Optional[int] = patch_size
lowercase__ : Tuple = num_channels
lowercase__ : List[str] = make_divisible(512 * width_multiplier , divisor=8 )
lowercase__ : Optional[int] = hidden_act
lowercase__ : List[Any] = conv_kernel_size
lowercase__ : Dict = output_stride
lowercase__ : List[Any] = classifier_dropout_prob
lowercase__ : str = use_labels
lowercase__ : List[Any] = is_training
lowercase__ : Tuple = num_labels
lowercase__ : Optional[int] = initializer_range
lowercase__ : Tuple = scope
lowercase__ : List[Any] = width_multiplier
lowercase__ : Optional[int] = ffn_dropout
lowercase__ : int = attn_dropout
def _lowerCAmelCase( self ) -> Optional[int]:
lowercase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Any = None
lowercase__ : Tuple = None
if self.use_labels:
lowercase__ : str = ids_tensor([self.batch_size] , self.num_labels )
lowercase__ : int = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase__ : Dict = self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCAmelCase( self ) -> Tuple:
return MobileViTVaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , width_multiplier=self.width_multiplier , ffn_dropout=self.ffn_dropout_prob , attn_dropout=self.attn_dropout_prob , )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
lowercase__ : Optional[int] = MobileViTVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowercase__ : str = model(__lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
lowercase__ : Optional[Any] = self.num_labels
lowercase__ : Dict = MobileViTVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowercase__ : Optional[Any] = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
lowercase__ : str = self.num_labels
lowercase__ : List[Any] = MobileViTVaForSemanticSegmentation(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
lowercase__ : int = model(__lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
lowercase__ : Union[str, Any] = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _lowerCAmelCase( self ) -> int:
lowercase__ : Union[str, Any] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[str] = config_and_inputs
lowercase__ : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( a__ , a__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (
(MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
"feature-extraction": MobileViTVaModel,
"image-classification": MobileViTVaForImageClassification,
"image-segmentation": MobileViTVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def _lowerCAmelCase( self ) -> int:
lowercase__ : Tuple = MobileViTVaModelTester(self )
lowercase__ : Any = MobileViTVaConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def _lowerCAmelCase( self ) -> List[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds''' )
def _lowerCAmelCase( self ) -> str:
pass
@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings''' )
def _lowerCAmelCase( self ) -> Optional[Any]:
pass
@unittest.skip(reason='''MobileViTV2 does not output attentions''' )
def _lowerCAmelCase( self ) -> Optional[int]:
pass
@require_torch_multi_gpu
@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.''' )
def _lowerCAmelCase( self ) -> Any:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def _lowerCAmelCase( self ) -> str:
pass
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : str = model_class(__lowerCAmelCase )
lowercase__ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : str = [*signature.parameters.keys()]
lowercase__ : Dict = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> str:
lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def _lowerCAmelCase( self ) -> Union[str, Any]:
def check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowercase__ : Optional[int] = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
lowercase__ : List[str] = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
lowercase__ : List[Any] = outputs.hidden_states
lowercase__ : Optional[int] = 5
self.assertEqual(len(__lowerCAmelCase ) , __lowerCAmelCase )
# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
lowercase__ : str = 2
for i in range(len(__lowerCAmelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[str] = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Tuple = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
def _lowerCAmelCase( self ) -> Dict:
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase )
@slow
def _lowerCAmelCase( self ) -> List[str]:
for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : Dict = MobileViTVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def __UpperCamelCase ( ):
lowercase__ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _lowerCAmelCase( self ) -> int:
return (
MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' )
if is_vision_available()
else None
)
@slow
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : Dict = MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' ).to(
__lowerCAmelCase )
lowercase__ : List[Any] = self.default_image_processor
lowercase__ : Optional[int] = prepare_img()
lowercase__ : int = image_processor(images=__lowerCAmelCase , return_tensors='''pt''' ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ : List[str] = model(**__lowerCAmelCase )
# verify the logits
lowercase__ : Optional[Any] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
lowercase__ : int = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
@slow
def _lowerCAmelCase( self ) -> Optional[int]:
lowercase__ : Dict = MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
lowercase__ : int = model.to(__lowerCAmelCase )
lowercase__ : Any = MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
lowercase__ : str = prepare_img()
lowercase__ : Optional[int] = image_processor(images=__lowerCAmelCase , return_tensors='''pt''' ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ : str = model(**__lowerCAmelCase )
lowercase__ : Tuple = outputs.logits
# verify the logits
lowercase__ : List[Any] = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __lowerCAmelCase )
lowercase__ : Union[str, Any] = torch.tensor(
[
[[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
[[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
[[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
] , device=__lowerCAmelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __lowerCAmelCase , atol=1E-4 ) )
@slow
def _lowerCAmelCase( self ) -> Any:
lowercase__ : Optional[int] = MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
lowercase__ : List[str] = model.to(__lowerCAmelCase )
lowercase__ : Optional[int] = MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
lowercase__ : int = prepare_img()
lowercase__ : List[str] = image_processor(images=__lowerCAmelCase , return_tensors='''pt''' ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ : Optional[Any] = model(**__lowerCAmelCase )
lowercase__ : Optional[int] = outputs.logits.detach().cpu()
lowercase__ : Any = image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase , target_sizes=[(50, 60)] )
lowercase__ : Optional[int] = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __lowerCAmelCase )
lowercase__ : Optional[Any] = image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase )
lowercase__ : Union[str, Any] = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __lowerCAmelCase )
| 198 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
lowercase_ = get_tests_dir("""fixtures""")
class _snake_case ( unittest.TestCase):
def A__ ( self : Tuple ):
# A mock response for an HTTP head request to emulate server down
lowercase__ = mock.Mock()
lowercase__ = 500
lowercase__ = {}
lowercase__ = HTTPError
lowercase__ = {}
# Download this model to make sure it's in the cache.
lowercase__ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=__lowercase ) as mock_head:
lowercase__ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# This check we did call the fake head request
mock_head.assert_called()
def A__ ( self : Dict ):
# This test is for deprecated behavior and can be removed in v5
lowercase__ = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json" )
def A__ ( self : List[str] ):
with self.assertRaises(__lowercase ):
# config is in subfolder, the following should not work without specifying the subfolder
lowercase__ = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants" )
lowercase__ = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor" )
self.assertIsNotNone(__lowercase )
@is_staging_test
class _snake_case ( unittest.TestCase):
@classmethod
def A__ ( cls : int ):
lowercase__ = TOKEN
HfFolder.save_token(__lowercase )
@classmethod
def A__ ( cls : Optional[int] ):
try:
delete_repo(token=cls._token, repo_id="test-image-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor" )
except HTTPError:
pass
def A__ ( self : Tuple ):
lowercase__ = ViTImageProcessor.from_pretrained(__lowercase )
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token )
lowercase__ = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__lowercase, getattr(__lowercase, __lowercase ) )
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__lowercase, repo_id="test-image-processor", push_to_hub=__lowercase, use_auth_token=self._token )
lowercase__ = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__lowercase, getattr(__lowercase, __lowercase ) )
def A__ ( self : str ):
lowercase__ = ViTImageProcessor.from_pretrained(__lowercase )
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token )
lowercase__ = ViTImageProcessor.from_pretrained("valid_org/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__lowercase, getattr(__lowercase, __lowercase ) )
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__lowercase, repo_id="valid_org/test-image-processor-org", push_to_hub=__lowercase, use_auth_token=self._token )
lowercase__ = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__lowercase, getattr(__lowercase, __lowercase ) )
def A__ ( self : List[str] ):
CustomImageProcessor.register_for_auto_class()
lowercase__ = CustomImageProcessor.from_pretrained(__lowercase )
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map, {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"}, )
lowercase__ = AutoImageProcessor.from_pretrained(
F'''{USER}/test-dynamic-image-processor''', trust_remote_code=__lowercase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor" )
| 224 |
import fire
from utils import calculate_rouge, save_json
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ):
lowercase__ = [x.strip() for x in open(SCREAMING_SNAKE_CASE_ ).readlines()]
lowercase__ = [x.strip() for x in open(SCREAMING_SNAKE_CASE_ ).readlines()][: len(SCREAMING_SNAKE_CASE_ )]
lowercase__ = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if save_path is not None:
save_json(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , indent=SCREAMING_SNAKE_CASE_ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 224 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase( __a ):
'''simple docstring'''
def __init__( self: List[Any], a_: List[Any], a_: Tuple ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=a_, scheduler=a_ )
@torch.no_grad()
def __call__( self: Optional[Any], a_: int = 1, a_: int = 100, a_: Optional[Union[torch.Generator, List[torch.Generator]]] = None, a_: Optional[float] = None, a_: bool = True, ):
'''simple docstring'''
if audio_length_in_s is None:
_snake_case : Dict = self.unet.config.sample_size / self.unet.config.sample_rate
_snake_case : Any = audio_length_in_s * self.unet.config.sample_rate
_snake_case : int = 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}." )
_snake_case : str = int(a_ )
if sample_size % down_scale_factor != 0:
_snake_case : Optional[Any] = (
(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.""" )
_snake_case : List[Any] = int(a_ )
_snake_case : Dict = next(iter(self.unet.parameters() ) ).dtype
_snake_case : Any = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(a_, a_ ) and len(a_ ) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(a_ )}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators." )
_snake_case : Union[str, Any] = randn_tensor(a_, generator=a_, device=self.device, dtype=a_ )
# set step values
self.scheduler.set_timesteps(a_, device=audio.device )
_snake_case : Union[str, Any] = self.scheduler.timesteps.to(a_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
_snake_case : Any = self.unet(a_, a_ ).sample
# 2. compute previous image: x_t -> t_t-1
_snake_case : int = self.scheduler.step(a_, a_, a_ ).prev_sample
_snake_case : Any = audio.clamp(-1, 1 ).float().cpu().numpy()
_snake_case : str = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=a_ )
| 64 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["image_processor", "tokenizer"]
lowercase__ = "AutoImageProcessor"
lowercase__ = "AutoTokenizer"
def __init__( self: List[str], a_: List[str]=None, a_: Tuple=None, **a_: Tuple ):
'''simple docstring'''
_snake_case : str = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""", a_, )
_snake_case : str = kwargs.pop("""feature_extractor""" )
_snake_case : Union[str, Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(a_, a_ )
_snake_case : Dict = self.image_processor
_snake_case : Any = False
def __call__( self: Any, *a_: Any, **a_: Tuple ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*a_, **a_ )
_snake_case : Dict = kwargs.pop("""images""", a_ )
_snake_case : Optional[Any] = kwargs.pop("""text""", a_ )
if len(a_ ) > 0:
_snake_case : Optional[int] = args[0]
_snake_case : Tuple = args[1:]
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
_snake_case : Tuple = self.image_processor(a_, *a_, **a_ )
if text is not None:
_snake_case : Tuple = self.tokenizer(a_, **a_ )
if text is None:
return inputs
elif images is None:
return encodings
else:
_snake_case : List[str] = encodings["""input_ids"""]
return inputs
def UpperCamelCase_ ( self: Optional[int], *a_: Tuple, **a_: List[str] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*a_, **a_ )
def UpperCamelCase_ ( self: int, *a_: List[str], **a_: int ):
'''simple docstring'''
return self.tokenizer.decode(*a_, **a_ )
@contextmanager
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your images inputs, or in a separate call.""" )
_snake_case : Any = True
_snake_case : Optional[int] = self.tokenizer
yield
_snake_case : int = self.image_processor
_snake_case : Optional[int] = False
def UpperCamelCase_ ( self: Dict, a_: Optional[Any], a_: str=False, a_: Optional[Any]=None ):
'''simple docstring'''
if added_vocab is None:
_snake_case : Dict = self.tokenizer.get_added_vocab()
_snake_case : str = {}
while tokens:
_snake_case : Union[str, Any] = re.search(r"""<s_(.*?)>""", a_, re.IGNORECASE )
if start_token is None:
break
_snake_case : List[Any] = start_token.group(1 )
_snake_case : str = re.search(rf"</s_{key}>", a_, re.IGNORECASE )
_snake_case : Dict = start_token.group()
if end_token is None:
_snake_case : List[Any] = tokens.replace(a_, """""" )
else:
_snake_case : List[str] = end_token.group()
_snake_case : str = re.escape(a_ )
_snake_case : str = re.escape(a_ )
_snake_case : Union[str, Any] = re.search(f"{start_token_escaped}(.*?){end_token_escaped}", a_, re.IGNORECASE )
if content is not None:
_snake_case : int = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
_snake_case : List[Any] = self.tokenajson(a_, is_inner_value=a_, added_vocab=a_ )
if value:
if len(a_ ) == 1:
_snake_case : List[str] = value[0]
_snake_case : List[str] = value
else: # leaf nodes
_snake_case : Tuple = []
for leaf in content.split(r"""<sep/>""" ):
_snake_case : Tuple = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
_snake_case : int = leaf[1:-2] # for categorical special tokens
output[key].append(a_ )
if len(output[key] ) == 1:
_snake_case : int = output[key][0]
_snake_case : Any = tokens[tokens.find(a_ ) + len(a_ ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:], is_inner_value=a_, added_vocab=a_ )
if len(a_ ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def UpperCamelCase_ ( self: Optional[int] ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""", a_, )
return self.image_processor_class
@property
def UpperCamelCase_ ( self: Tuple ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""", a_, )
return self.image_processor
| 64 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = OrderedDict(
[
('align', 'EfficientNetImageProcessor'),
('beit', 'BeitImageProcessor'),
('bit', 'BitImageProcessor'),
('blip', 'BlipImageProcessor'),
('blip-2', 'BlipImageProcessor'),
('bridgetower', 'BridgeTowerImageProcessor'),
('chinese_clip', 'ChineseCLIPImageProcessor'),
('clip', 'CLIPImageProcessor'),
('clipseg', 'ViTImageProcessor'),
('conditional_detr', 'ConditionalDetrImageProcessor'),
('convnext', 'ConvNextImageProcessor'),
('convnextv2', 'ConvNextImageProcessor'),
('cvt', 'ConvNextImageProcessor'),
('data2vec-vision', 'BeitImageProcessor'),
('deformable_detr', 'DeformableDetrImageProcessor'),
('deit', 'DeiTImageProcessor'),
('deta', 'DetaImageProcessor'),
('detr', 'DetrImageProcessor'),
('dinat', 'ViTImageProcessor'),
('donut-swin', 'DonutImageProcessor'),
('dpt', 'DPTImageProcessor'),
('efficientformer', 'EfficientFormerImageProcessor'),
('efficientnet', 'EfficientNetImageProcessor'),
('flava', 'FlavaImageProcessor'),
('focalnet', 'BitImageProcessor'),
('git', 'CLIPImageProcessor'),
('glpn', 'GLPNImageProcessor'),
('groupvit', 'CLIPImageProcessor'),
('imagegpt', 'ImageGPTImageProcessor'),
('instructblip', 'BlipImageProcessor'),
('layoutlmv2', 'LayoutLMv2ImageProcessor'),
('layoutlmv3', 'LayoutLMv3ImageProcessor'),
('levit', 'LevitImageProcessor'),
('mask2former', 'Mask2FormerImageProcessor'),
('maskformer', 'MaskFormerImageProcessor'),
('mgp-str', 'ViTImageProcessor'),
('mobilenet_v1', 'MobileNetV1ImageProcessor'),
('mobilenet_v2', 'MobileNetV2ImageProcessor'),
('mobilevit', 'MobileViTImageProcessor'),
('mobilevit', 'MobileViTImageProcessor'),
('mobilevitv2', 'MobileViTImageProcessor'),
('nat', 'ViTImageProcessor'),
('oneformer', 'OneFormerImageProcessor'),
('owlvit', 'OwlViTImageProcessor'),
('perceiver', 'PerceiverImageProcessor'),
('pix2struct', 'Pix2StructImageProcessor'),
('poolformer', 'PoolFormerImageProcessor'),
('regnet', 'ConvNextImageProcessor'),
('resnet', 'ConvNextImageProcessor'),
('sam', 'SamImageProcessor'),
('segformer', 'SegformerImageProcessor'),
('swiftformer', 'ViTImageProcessor'),
('swin', 'ViTImageProcessor'),
('swin2sr', 'Swin2SRImageProcessor'),
('swinv2', 'ViTImageProcessor'),
('table-transformer', 'DetrImageProcessor'),
('timesformer', 'VideoMAEImageProcessor'),
('tvlt', 'TvltImageProcessor'),
('upernet', 'SegformerImageProcessor'),
('van', 'ConvNextImageProcessor'),
('videomae', 'VideoMAEImageProcessor'),
('vilt', 'ViltImageProcessor'),
('vit', 'ViTImageProcessor'),
('vit_hybrid', 'ViTHybridImageProcessor'),
('vit_mae', 'ViTImageProcessor'),
('vit_msn', 'ViTImageProcessor'),
('xclip', 'CLIPImageProcessor'),
('yolos', 'YolosImageProcessor'),
]
)
__UpperCAmelCase = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def lowercase__ ( __snake_case : str ):
'''simple docstring'''
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
UpperCAmelCase_ : Optional[Any] = model_type_to_module_name(__snake_case )
UpperCAmelCase_ : Optional[Any] = importlib.import_module(F".{module_name}" , 'transformers.models' )
try:
return getattr(__snake_case , __snake_case )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(__snake_case , '__name__' , __snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
UpperCAmelCase_ : Optional[Any] = importlib.import_module('transformers' )
if hasattr(__snake_case , __snake_case ):
return getattr(__snake_case , __snake_case )
return None
def lowercase__ ( __snake_case : Union[str, os.PathLike] , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : List[Any] , ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = get_file_from_repo(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
if resolved_config_file is None:
logger.info(
'Could not locate the image processor configuration file, will try to use the model config instead.' )
return {}
with open(__snake_case , encoding='utf-8' ) as reader:
return json.load(__snake_case )
class lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Optional[Any]:
raise EnvironmentError(
'AutoImageProcessor is designed to be instantiated '
'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.' )
@classmethod
@replace_list_option_in_docstrings(_UpperCamelCase )
def __UpperCAmelCase ( cls , _UpperCamelCase , **_UpperCamelCase ) -> str:
UpperCAmelCase_ : str = kwargs.pop('config' , _UpperCamelCase )
UpperCAmelCase_ : List[str] = kwargs.pop('trust_remote_code' , _UpperCamelCase )
UpperCAmelCase_ : List[Any] = True
UpperCAmelCase_ , UpperCAmelCase_ : Any = ImageProcessingMixin.get_image_processor_dict(_UpperCamelCase , **_UpperCamelCase )
UpperCAmelCase_ : Dict = config_dict.get('image_processor_type' , _UpperCamelCase )
UpperCAmelCase_ : Union[str, Any] = None
if "AutoImageProcessor" in config_dict.get('auto_map' , {} ):
UpperCAmelCase_ : List[str] = config_dict['auto_map']['AutoImageProcessor']
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
UpperCAmelCase_ : Union[str, Any] = config_dict.pop('feature_extractor_type' , _UpperCamelCase )
if feature_extractor_class is not None:
logger.warning(
'Could not find image processor class in the image processor config or the model config. Loading'
' based on pattern matching with the model\'s feature extractor configuration.' )
UpperCAmelCase_ : Dict = feature_extractor_class.replace('FeatureExtractor' , 'ImageProcessor' )
if "AutoFeatureExtractor" in config_dict.get('auto_map' , {} ):
UpperCAmelCase_ : str = config_dict['auto_map']['AutoFeatureExtractor']
UpperCAmelCase_ : Optional[Any] = feature_extractor_auto_map.replace('FeatureExtractor' , 'ImageProcessor' )
logger.warning(
'Could not find image processor auto map in the image processor config or the model config.'
' Loading based on pattern matching with the model\'s feature extractor configuration.' )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
UpperCAmelCase_ : List[Any] = AutoConfig.from_pretrained(_UpperCamelCase , **_UpperCamelCase )
# It could be in `config.image_processor_type``
UpperCAmelCase_ : Dict = getattr(_UpperCamelCase , 'image_processor_type' , _UpperCamelCase )
if hasattr(_UpperCamelCase , 'auto_map' ) and "AutoImageProcessor" in config.auto_map:
UpperCAmelCase_ : List[str] = config.auto_map['AutoImageProcessor']
if image_processor_class is not None:
UpperCAmelCase_ : Union[str, Any] = image_processor_class_from_name(_UpperCamelCase )
UpperCAmelCase_ : str = image_processor_auto_map is not None
UpperCAmelCase_ : Tuple = image_processor_class is not None or type(_UpperCamelCase ) in IMAGE_PROCESSOR_MAPPING
UpperCAmelCase_ : Dict = resolve_trust_remote_code(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if has_remote_code and trust_remote_code:
UpperCAmelCase_ : Tuple = get_class_from_dynamic_module(
_UpperCamelCase , _UpperCamelCase , **_UpperCamelCase )
UpperCAmelCase_ : Optional[Any] = kwargs.pop('code_revision' , _UpperCamelCase )
if os.path.isdir(_UpperCamelCase ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(_UpperCamelCase , **_UpperCamelCase )
elif image_processor_class is not None:
return image_processor_class.from_dict(_UpperCamelCase , **_UpperCamelCase )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(_UpperCamelCase ) in IMAGE_PROCESSOR_MAPPING:
UpperCAmelCase_ : List[str] = IMAGE_PROCESSOR_MAPPING[type(_UpperCamelCase )]
return image_processor_class.from_dict(_UpperCamelCase , **_UpperCamelCase )
raise ValueError(
f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}" )
@staticmethod
def __UpperCAmelCase ( _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
IMAGE_PROCESSOR_MAPPING.register(_UpperCamelCase , _UpperCamelCase )
| 145 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase (_snake_case , _snake_case ):
'''simple docstring'''
@register_to_config
def __init__( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = None ) -> int:
super().__init__()
UpperCAmelCase_ : str = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
UpperCAmelCase_ : Optional[Any] = torch.zeros(_UpperCamelCase , _UpperCamelCase )
else:
UpperCAmelCase_ : Any = None
UpperCAmelCase_ : Any = torch.nn.Parameter(_UpperCamelCase )
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : VQModel
_snake_case : CLIPTextModel
_snake_case : CLIPTokenizer
_snake_case : TransformeraDModel
_snake_case : LearnedClassifierFreeSamplingEmbeddings
_snake_case : VQDiffusionScheduler
def __init__( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ) -> List[Any]:
super().__init__()
self.register_modules(
vqvae=_UpperCamelCase , transformer=_UpperCamelCase , text_encoder=_UpperCamelCase , tokenizer=_UpperCamelCase , scheduler=_UpperCamelCase , learned_classifier_free_sampling_embeddings=_UpperCamelCase , )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[str]:
UpperCAmelCase_ : Union[str, Any] = len(_UpperCamelCase ) if isinstance(_UpperCamelCase , _UpperCamelCase ) else 1
# get prompt text embeddings
UpperCAmelCase_ : str = self.tokenizer(
_UpperCamelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
UpperCAmelCase_ : Optional[Any] = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase_ : List[Any] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f" {self.tokenizer.model_max_length} tokens: {removed_text}" )
UpperCAmelCase_ : str = text_input_ids[:, : self.tokenizer.model_max_length]
UpperCAmelCase_ : str = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
UpperCAmelCase_ : Dict = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=_UpperCamelCase )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase_ : Dict = prompt_embeds.repeat_interleave(_UpperCamelCase , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
UpperCAmelCase_ : List[str] = self.learned_classifier_free_sampling_embeddings.embeddings
UpperCAmelCase_ : List[str] = negative_prompt_embeds.unsqueeze(0 ).repeat(_UpperCamelCase , 1 , 1 )
else:
UpperCAmelCase_ : List[Any] = [''] * batch_size
UpperCAmelCase_ : List[Any] = text_input_ids.shape[-1]
UpperCAmelCase_ : Dict = self.tokenizer(
_UpperCamelCase , padding='max_length' , max_length=_UpperCamelCase , truncation=_UpperCamelCase , return_tensors='pt' , )
UpperCAmelCase_ : Tuple = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
UpperCAmelCase_ : Dict = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=_UpperCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase_ : List[Any] = negative_prompt_embeds.shape[1]
UpperCAmelCase_ : Dict = negative_prompt_embeds.repeat(1 , _UpperCamelCase , 1 )
UpperCAmelCase_ : Any = negative_prompt_embeds.view(batch_size * num_images_per_prompt , _UpperCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase_ : Union[str, Any] = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , _UpperCamelCase , _UpperCamelCase = 1_0_0 , _UpperCamelCase = 5.0 , _UpperCamelCase = 1.0 , _UpperCamelCase = 1 , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = "pil" , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
if isinstance(_UpperCamelCase , _UpperCamelCase ):
UpperCAmelCase_ : Any = 1
elif isinstance(_UpperCamelCase , _UpperCamelCase ):
UpperCAmelCase_ : Tuple = len(_UpperCamelCase )
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase )}" )
UpperCAmelCase_ : Union[str, Any] = batch_size * num_images_per_prompt
UpperCAmelCase_ : Optional[int] = guidance_scale > 1.0
UpperCAmelCase_ : Any = self._encode_prompt(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_UpperCamelCase , _UpperCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(_UpperCamelCase )}." )
# get the initial completely masked latents unless the user supplied it
UpperCAmelCase_ : Optional[int] = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
UpperCAmelCase_ : Tuple = self.transformer.num_vector_embeds - 1
UpperCAmelCase_ : List[Any] = torch.full(_UpperCamelCase , _UpperCamelCase ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
f" {self.transformer.num_vector_embeds - 1} (inclusive)." )
UpperCAmelCase_ : Any = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(_UpperCamelCase , device=self.device )
UpperCAmelCase_ : List[str] = self.scheduler.timesteps.to(self.device )
UpperCAmelCase_ : Union[str, Any] = latents
for i, t in enumerate(self.progress_bar(_UpperCamelCase ) ):
# expand the sample if we are doing classifier free guidance
UpperCAmelCase_ : Union[str, Any] = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
UpperCAmelCase_ : Dict = self.transformer(_UpperCamelCase , encoder_hidden_states=_UpperCamelCase , timestep=_UpperCamelCase ).sample
if do_classifier_free_guidance:
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = model_output.chunk(2 )
UpperCAmelCase_ : Optional[int] = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(_UpperCamelCase , dim=1 , keepdim=_UpperCamelCase )
UpperCAmelCase_ : str = self.truncate(_UpperCamelCase , _UpperCamelCase )
# remove `log(0)`'s (`-inf`s)
UpperCAmelCase_ : Optional[int] = model_output.clamp(-7_0 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase_ : Union[str, Any] = self.scheduler.step(_UpperCamelCase , timestep=_UpperCamelCase , sample=_UpperCamelCase , generator=_UpperCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ : str = self.vqvae.config.vq_embed_dim
UpperCAmelCase_ : Optional[int] = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
UpperCAmelCase_ : int = self.vqvae.quantize.get_codebook_entry(_UpperCamelCase , shape=_UpperCamelCase )
UpperCAmelCase_ : Dict = self.vqvae.decode(_UpperCamelCase , force_not_quantize=_UpperCamelCase ).sample
UpperCAmelCase_ : List[str] = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase_ : Any = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase_ : int = self.numpy_to_pil(_UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase ) -> torch.FloatTensor:
UpperCAmelCase_ , UpperCAmelCase_ : int = torch.sort(_UpperCamelCase , 1 , descending=_UpperCamelCase )
UpperCAmelCase_ : Dict = torch.exp(_UpperCamelCase )
UpperCAmelCase_ : int = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
UpperCAmelCase_ : Tuple = torch.full_like(keep_mask[:, 0:1, :] , _UpperCamelCase )
UpperCAmelCase_ : List[str] = torch.cat((all_true, keep_mask) , dim=1 )
UpperCAmelCase_ : int = keep_mask[:, :-1, :]
UpperCAmelCase_ : Any = keep_mask.gather(1 , indices.argsort(1 ) )
UpperCAmelCase_ : str = log_p_x_0.clone()
UpperCAmelCase_ : Any = -torch.inf # -inf = log(0)
return rv
| 145 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : Tuple = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = '''deta'''
lowerCAmelCase = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self , _UpperCAmelCase=None , _UpperCAmelCase=900 , _UpperCAmelCase=2048 , _UpperCAmelCase=6 , _UpperCAmelCase=2048 , _UpperCAmelCase=8 , _UpperCAmelCase=6 , _UpperCAmelCase=1024 , _UpperCAmelCase=8 , _UpperCAmelCase=0.0 , _UpperCAmelCase=True , _UpperCAmelCase="relu" , _UpperCAmelCase=256 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1.0 , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase="sine" , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=4 , _UpperCAmelCase=True , _UpperCAmelCase=300 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=1 , _UpperCAmelCase=5 , _UpperCAmelCase=2 , _UpperCAmelCase=1 , _UpperCAmelCase=1 , _UpperCAmelCase=5 , _UpperCAmelCase=2 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.25 , **_UpperCAmelCase , ):
'''simple docstring'''
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
__A : Dict = CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'])
else:
if isinstance(_UpperCAmelCase , _UpperCAmelCase):
__A : List[str] = backbone_config.pop('model_type')
__A : str = CONFIG_MAPPING[backbone_model_type]
__A : Any = config_class.from_dict(_UpperCAmelCase)
__A : int = backbone_config
__A : List[str] = num_queries
__A : str = max_position_embeddings
__A : Optional[int] = d_model
__A : Any = encoder_ffn_dim
__A : int = encoder_layers
__A : str = encoder_attention_heads
__A : Dict = decoder_ffn_dim
__A : Optional[Any] = decoder_layers
__A : List[str] = decoder_attention_heads
__A : int = dropout
__A : int = attention_dropout
__A : int = activation_dropout
__A : int = activation_function
__A : Any = init_std
__A : List[Any] = init_xavier_std
__A : List[str] = encoder_layerdrop
__A : Optional[Any] = auxiliary_loss
__A : str = position_embedding_type
# deformable attributes
__A : Optional[Any] = num_feature_levels
__A : Optional[int] = encoder_n_points
__A : int = decoder_n_points
__A : int = two_stage
__A : Union[str, Any] = two_stage_num_proposals
__A : Union[str, Any] = with_box_refine
__A : Dict = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('If two_stage is True, with_box_refine must be True.')
# Hungarian matcher
__A : int = class_cost
__A : str = bbox_cost
__A : str = giou_cost
# Loss coefficients
__A : Tuple = mask_loss_coefficient
__A : Optional[Any] = dice_loss_coefficient
__A : int = bbox_loss_coefficient
__A : str = giou_loss_coefficient
__A : List[str] = eos_coefficient
__A : Union[str, Any] = focal_alpha
super().__init__(is_encoder_decoder=_UpperCAmelCase , **_UpperCAmelCase)
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return self.d_model
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[Any] = copy.deepcopy(self.__dict__)
__A : int = self.backbone_config.to_dict()
__A : Dict = self.__class__.model_type
return output | 190 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : List[str] = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Any = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
lowercase__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 190 | 1 |
import argparse
import os
import re
import packaging.version
lowercase_ = "examples/"
lowercase_ = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
"doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
lowercase_ = {
"init": "src/transformers/__init__.py",
"setup": "setup.py",
}
lowercase_ = "README.md"
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
with open(__SCREAMING_SNAKE_CASE , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
__snake_case : int = f.read()
__snake_case , __snake_case : int = REPLACE_PATTERNS[pattern]
__snake_case : Tuple = replace.replace("""VERSION""" , __SCREAMING_SNAKE_CASE )
__snake_case : Optional[Any] = re_pattern.sub(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
for folder, directories, fnames in os.walk(__SCREAMING_SNAKE_CASE ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE , pattern="""examples""" )
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str]=False ):
'''simple docstring'''
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if not patch:
update_version_in_examples(__SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( ):
'''simple docstring'''
__snake_case : Union[str, Any] = """🤗 Transformers currently provides the following architectures"""
__snake_case : Optional[Any] = """1. Want to contribute a new model?"""
with open(__SCREAMING_SNAKE_CASE , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
__snake_case : str = f.readlines()
# Find the start of the list.
__snake_case : Tuple = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
__snake_case : List[str] = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
__snake_case : str = lines[index].replace(
"""https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , )
index += 1
with open(__SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( ):
'''simple docstring'''
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
__snake_case : Any = f.read()
__snake_case : Tuple = REPLACE_PATTERNS["""init"""][0].search(__SCREAMING_SNAKE_CASE ).groups()[0]
return packaging.version.parse(__SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : List[Any]=False ):
'''simple docstring'''
__snake_case : Optional[Any] = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
__snake_case : str = default_version.base_version
elif patch:
__snake_case : Tuple = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
__snake_case : Optional[int] = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
__snake_case : Optional[int] = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__SCREAMING_SNAKE_CASE ) == 0:
__snake_case : Union[str, Any] = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__SCREAMING_SNAKE_CASE , patch=__SCREAMING_SNAKE_CASE )
if not patch:
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
def __lowerCAmelCase ( ):
'''simple docstring'''
__snake_case : Union[str, Any] = get_version()
__snake_case : int = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
__snake_case : Optional[int] = current_version.base_version
# Check with the user we got that right.
__snake_case : Dict = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__SCREAMING_SNAKE_CASE ) == 0:
__snake_case : Optional[Any] = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__SCREAMING_SNAKE_CASE )
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
lowercase_ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 20 | import math
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : float , __SCREAMING_SNAKE_CASE : float ):
'''simple docstring'''
if (
not isinstance(__SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float value between -1 and 1.""" )
return apparent_power * power_factor
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : float , __SCREAMING_SNAKE_CASE : float ):
'''simple docstring'''
if (
not isinstance(__SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float value between -1 and 1.""" )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 20 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class UpperCamelCase ( snake_case_ , unittest.TestCase ):
UpperCamelCase : Optional[int] = BertTokenizer
UpperCamelCase : str = BertTokenizerFast
UpperCamelCase : List[Any] = True
UpperCamelCase : str = True
UpperCamelCase : Any = filter_non_english
def _lowercase ( self : int ) -> List[Any]:
super().setUp()
_a : Union[str, Any] = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_a : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def _lowercase ( self : Tuple , UpperCAmelCase__ : str ) -> Any:
_a : Dict = """UNwant\u00E9d,running"""
_a : Tuple = """unwanted, running"""
return input_text, output_text
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
_a : Optional[Any] = self.tokenizer_class(self.vocab_file )
_a : Dict = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(UpperCAmelCase__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [9, 6, 7, 12, 10, 11] )
def _lowercase ( self : Tuple ) -> List[Any]:
if not self.test_rust_tokenizer:
return
_a : Union[str, Any] = self.get_tokenizer()
_a : Tuple = self.get_rust_tokenizer()
_a : str = """UNwant\u00E9d,running"""
_a : Union[str, Any] = tokenizer.tokenize(UpperCAmelCase__ )
_a : Any = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : List[Any] = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Dict = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : int = self.get_rust_tokenizer()
_a : int = tokenizer.encode(UpperCAmelCase__ )
_a : List[Any] = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
# With lower casing
_a : str = self.get_tokenizer(do_lower_case=UpperCAmelCase__ )
_a : Tuple = self.get_rust_tokenizer(do_lower_case=UpperCAmelCase__ )
_a : List[str] = """UNwant\u00E9d,running"""
_a : Optional[Any] = tokenizer.tokenize(UpperCAmelCase__ )
_a : List[Any] = rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : List[str] = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Union[str, Any] = rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : Optional[int] = self.get_rust_tokenizer()
_a : Any = tokenizer.encode(UpperCAmelCase__ )
_a : List[str] = rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : Union[str, Any] ) -> List[str]:
_a : str = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def _lowercase ( self : List[Any] ) -> Optional[int]:
_a : Optional[Any] = BasicTokenizer(do_lower_case=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _lowercase ( self : int ) -> Tuple:
_a : Optional[Any] = BasicTokenizer(do_lower_case=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def _lowercase ( self : Any ) -> Dict:
_a : str = BasicTokenizer(do_lower_case=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _lowercase ( self : str ) -> Dict:
_a : Optional[int] = BasicTokenizer(do_lower_case=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _lowercase ( self : str ) -> List[str]:
_a : Union[str, Any] = BasicTokenizer(do_lower_case=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _lowercase ( self : Optional[Any] ) -> int:
_a : Optional[int] = BasicTokenizer(do_lower_case=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
_a : str = BasicTokenizer(do_lower_case=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _lowercase ( self : Optional[int] ) -> Tuple:
_a : Tuple = BasicTokenizer(do_lower_case=UpperCAmelCase__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def _lowercase ( self : Optional[Any] ) -> int:
_a : str = BasicTokenizer()
_a : Union[str, Any] = """a\n'll !!to?'d of, can't."""
_a : Optional[Any] = ["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
self.assertListEqual(tokenizer.tokenize(UpperCAmelCase__ ) , UpperCAmelCase__ )
def _lowercase ( self : List[str] ) -> Dict:
_a : List[str] = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
_a : Tuple = {}
for i, token in enumerate(UpperCAmelCase__ ):
_a : Optional[Any] = i
_a : List[Any] = WordpieceTokenizer(vocab=UpperCAmelCase__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def _lowercase ( self : int ) -> List[str]:
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def _lowercase ( self : Dict ) -> str:
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def _lowercase ( self : Any ) -> str:
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def _lowercase ( self : Union[str, Any] ) -> Tuple:
_a : Optional[int] = self.get_tokenizer()
_a : Optional[Any] = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(UpperCAmelCase__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(UpperCAmelCase__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def _lowercase ( self : Tuple ) -> Optional[int]:
_a : Any = self.tokenizer_class.from_pretrained("""bert-base-uncased""" )
_a : Any = tokenizer.encode("""sequence builders""" , add_special_tokens=UpperCAmelCase__ )
_a : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=UpperCAmelCase__ )
_a : Tuple = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__ )
_a : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__ , UpperCAmelCase__ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def _lowercase ( self : Dict ) -> int:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_a : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
_a : Optional[Any] = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
_a : str = tokenizer_r.encode_plus(
UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , return_token_type_ids=UpperCAmelCase__ , return_offsets_mapping=UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ , )
_a : List[str] = tokenizer_r.do_lower_case if hasattr(UpperCAmelCase__ , """do_lower_case""" ) else False
_a : List[str] = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def _lowercase ( self : Optional[int] ) -> Any:
_a : str = ["""的""", """人""", """有"""]
_a : Optional[int] = """""".join(UpperCAmelCase__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_a : List[str] = True
_a : Union[str, Any] = self.tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
_a : Optional[Any] = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
_a : List[str] = tokenizer_p.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : str = tokenizer_r.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : int = tokenizer_r.convert_ids_to_tokens(UpperCAmelCase__ )
_a : Optional[int] = tokenizer_p.convert_ids_to_tokens(UpperCAmelCase__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
_a : List[str] = False
_a : List[Any] = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
_a : Union[str, Any] = self.tokenizer_class.from_pretrained(UpperCAmelCase__ , **UpperCAmelCase__ )
_a : Any = tokenizer_r.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : int = tokenizer_p.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
_a : Optional[int] = tokenizer_r.convert_ids_to_tokens(UpperCAmelCase__ )
_a : List[Any] = tokenizer_p.convert_ids_to_tokens(UpperCAmelCase__ )
# it is expected that only the first Chinese character is not preceded by "##".
_a : Any = [
f"""##{token}""" if idx != 0 else token for idx, token in enumerate(UpperCAmelCase__ )
]
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
| 294 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> Dict:
if k in (0.0_4, 0.0_6):
_a : List[str] = k
_a : List[Any] = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self : Dict ) -> str:
return str(self.k )
def _lowercase ( self : int , UpperCAmelCase__ : str ) -> tuple[cva.Mat, list[list[int]]]:
_a : Dict = cva.imread(UpperCAmelCase__ , 0 )
_a , _a : List[Any] = img.shape
_a : list[list[int]] = []
_a : List[Any] = img.copy()
_a : int = cva.cvtColor(UpperCAmelCase__ , cva.COLOR_GRAY2RGB )
_a , _a : Any = np.gradient(UpperCAmelCase__ )
_a : Tuple = dx**2
_a : Union[str, Any] = dy**2
_a : Union[str, Any] = dx * dy
_a : int = 0.0_4
_a : List[str] = self.window_size // 2
for y in range(UpperCAmelCase__ , h - offset ):
for x in range(UpperCAmelCase__ , w - offset ):
_a : str = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_a : List[Any] = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_a : Tuple = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_a : Any = (wxx * wyy) - (wxy**2)
_a : Tuple = wxx + wyy
_a : Any = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
_snake_case = HarrisCorner(0.04, 3)
_snake_case , _snake_case = edge_detect.detect('path_to_image')
cva.imwrite('detect.png', color_img)
| 294 | 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,
is_vision_available,
)
a_ : int = {
"""configuration_clip""": [
"""CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPConfig""",
"""CLIPOnnxConfig""",
"""CLIPTextConfig""",
"""CLIPVisionConfig""",
],
"""processing_clip""": ["""CLIPProcessor"""],
"""tokenization_clip""": ["""CLIPTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Tuple = ["""CLIPTokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : int = ["""CLIPFeatureExtractor"""]
a_ : List[str] = ["""CLIPImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[Any] = [
"""CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPModel""",
"""CLIPPreTrainedModel""",
"""CLIPTextModel""",
"""CLIPTextModelWithProjection""",
"""CLIPVisionModel""",
"""CLIPVisionModelWithProjection""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : int = [
"""TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCLIPModel""",
"""TFCLIPPreTrainedModel""",
"""TFCLIPTextModel""",
"""TFCLIPVisionModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[str] = [
"""FlaxCLIPModel""",
"""FlaxCLIPPreTrainedModel""",
"""FlaxCLIPTextModel""",
"""FlaxCLIPTextPreTrainedModel""",
"""FlaxCLIPVisionModel""",
"""FlaxCLIPVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
a_ : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 6 |
'''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
#
########################################################################
a_ : Tuple = 16
a_ : Optional[int] = 32
def a_ ( __snake_case : Accelerator , __snake_case : int = 16 ) -> str:
"""simple docstring"""
lowerCamelCase_ =AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowerCamelCase_ =load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__snake_case : int ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase_ =tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__snake_case , max_length=__snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowerCamelCase_ =datasets.map(
__snake_case , batched=__snake_case , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCamelCase_ =tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__snake_case : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCamelCase_ =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":
lowerCamelCase_ =16
elif accelerator.mixed_precision != "no":
lowerCamelCase_ =8
else:
lowerCamelCase_ =None
return tokenizer.pad(
__snake_case , padding='''longest''' , max_length=__snake_case , pad_to_multiple_of=__snake_case , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowerCamelCase_ =DataLoader(
tokenized_datasets['''train'''] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
lowerCamelCase_ =DataLoader(
tokenized_datasets['''validation'''] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
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
a_ : Tuple = mocked_dataloaders # noqa: F811
def a_ ( __snake_case : List[str] , __snake_case : Tuple ) -> Optional[Any]:
"""simple docstring"""
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __snake_case ) == "1":
lowerCamelCase_ =2
# Initialize accelerator
lowerCamelCase_ =Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase_ =config['''lr''']
lowerCamelCase_ =int(config['''num_epochs'''] )
lowerCamelCase_ =int(config['''seed'''] )
lowerCamelCase_ =int(config['''batch_size'''] )
lowerCamelCase_ =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=__snake_case )
def inner_training_loop(__snake_case : 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(__snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase_ =AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__snake_case )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowerCamelCase_ =model.to(accelerator.device )
# Instantiate optimizer
lowerCamelCase_ =AdamW(params=model.parameters() , lr=__snake_case )
lowerCamelCase_, lowerCamelCase_ =get_dataloaders(__snake_case , __snake_case )
# Instantiate scheduler
lowerCamelCase_ =get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=100 , num_training_steps=(len(__snake_case ) * 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.
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =accelerator.prepare(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
# Now we train the model
for epoch in range(__snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowerCamelCase_ =model(**__snake_case )
lowerCamelCase_ =outputs.loss
accelerator.backward(__snake_case )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCamelCase_ =model(**__snake_case )
lowerCamelCase_ =outputs.logits.argmax(dim=-1 )
lowerCamelCase_, lowerCamelCase_ =accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
lowerCamelCase_ =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , __snake_case )
# 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 a_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=__snake_case , default=__snake_case , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowerCamelCase_ =parser.parse_args()
lowerCamelCase_ ={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main()
| 6 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__A = logging.get_logger(__name__)
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> str:
__lowerCAmelCase: Union[str, Any] = b.T
__lowerCAmelCase: str = np.sum(np.square(__SCREAMING_SNAKE_CASE ) , axis=1 )
__lowerCAmelCase: Optional[Any] = np.sum(np.square(__SCREAMING_SNAKE_CASE ) , axis=0 )
__lowerCAmelCase: int = np.matmul(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = aa[:, None] - 2 * ab + ba[None, :]
return d
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Dict:
__lowerCAmelCase: Union[str, Any] = x.reshape(-1 , 3 )
__lowerCAmelCase: Optional[Any] = squared_euclidean_distance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return np.argmin(__SCREAMING_SNAKE_CASE , axis=1 )
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["""pixel_values"""]
def __init__( self : int , UpperCamelCase__ : Optional[Union[List[List[int]], np.ndarray]] = None , UpperCamelCase__ : bool = True , UpperCamelCase__ : Dict[str, int] = None , UpperCamelCase__ : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase__ : bool = True , UpperCamelCase__ : bool = True , **UpperCamelCase__ : Union[str, Any] , )-> None:
'''simple docstring'''
super().__init__(**UpperCamelCase__)
__lowerCAmelCase: Any = size if size is not None else {"height": 2_5_6, "width": 2_5_6}
__lowerCAmelCase: int = get_size_dict(UpperCamelCase__)
__lowerCAmelCase: List[Any] = np.array(UpperCamelCase__) if clusters is not None else None
__lowerCAmelCase: int = do_resize
__lowerCAmelCase: int = size
__lowerCAmelCase: Tuple = resample
__lowerCAmelCase: Union[str, Any] = do_normalize
__lowerCAmelCase: Union[str, Any] = do_color_quantize
def lowercase_ ( self : Any , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Dict[str, int] , UpperCamelCase__ : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase__ : str , )-> np.ndarray:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = get_size_dict(UpperCamelCase__)
if "height" not in size or "width" not in size:
raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}")
return resize(
UpperCamelCase__ , size=(size["height"], size["width"]) , resample=UpperCamelCase__ , data_format=UpperCamelCase__ , **UpperCamelCase__)
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = None , )-> np.ndarray:
'''simple docstring'''
__lowerCAmelCase: Dict = rescale(image=UpperCamelCase__ , scale=1 / 127.5 , data_format=UpperCamelCase__)
__lowerCAmelCase: Union[str, Any] = image - 1
return image
def lowercase_ ( self : Union[str, Any] , UpperCamelCase__ : ImageInput , UpperCamelCase__ : bool = None , UpperCamelCase__ : Dict[str, int] = None , UpperCamelCase__ : PILImageResampling = None , UpperCamelCase__ : bool = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[Union[List[List[int]], np.ndarray]] = None , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **UpperCamelCase__ : Optional[int] , )-> PIL.Image.Image:
'''simple docstring'''
__lowerCAmelCase: Optional[int] = do_resize if do_resize is not None else self.do_resize
__lowerCAmelCase: List[str] = size if size is not None else self.size
__lowerCAmelCase: Any = get_size_dict(UpperCamelCase__)
__lowerCAmelCase: List[Any] = resample if resample is not None else self.resample
__lowerCAmelCase: int = do_normalize if do_normalize is not None else self.do_normalize
__lowerCAmelCase: Optional[int] = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
__lowerCAmelCase: Optional[Any] = clusters if clusters is not None else self.clusters
__lowerCAmelCase: Tuple = np.array(UpperCamelCase__)
__lowerCAmelCase: Tuple = make_list_of_images(UpperCamelCase__)
if not valid_images(UpperCamelCase__):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray.")
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True.")
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True.")
# All transformations expect numpy arrays.
__lowerCAmelCase: Optional[Any] = [to_numpy_array(UpperCamelCase__) for image in images]
if do_resize:
__lowerCAmelCase: Union[str, Any] = [self.resize(image=UpperCamelCase__ , size=UpperCamelCase__ , resample=UpperCamelCase__) for image in images]
if do_normalize:
__lowerCAmelCase: List[Any] = [self.normalize(image=UpperCamelCase__) for image in images]
if do_color_quantize:
__lowerCAmelCase: Union[str, Any] = [to_channel_dimension_format(UpperCamelCase__ , ChannelDimension.LAST) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
__lowerCAmelCase: int = np.array(UpperCamelCase__)
__lowerCAmelCase: int = color_quantize(UpperCamelCase__ , UpperCamelCase__).reshape(images.shape[:-1])
# flatten to (batch_size, height*width)
__lowerCAmelCase: Optional[Any] = images.shape[0]
__lowerCAmelCase: int = images.reshape(UpperCamelCase__ , -1)
# We need to convert back to a list of images to keep consistent behaviour across processors.
__lowerCAmelCase: Union[str, Any] = list(UpperCamelCase__)
else:
__lowerCAmelCase: Optional[Any] = [to_channel_dimension_format(UpperCamelCase__ , UpperCamelCase__) for image in images]
__lowerCAmelCase: int = {"input_ids": images}
return BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__)
| 217 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case ( __snake_case, unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : List[str] = None
SCREAMING_SNAKE_CASE_ : Tuple = BloomTokenizerFast
SCREAMING_SNAKE_CASE_ : str = BloomTokenizerFast
SCREAMING_SNAKE_CASE_ : int = True
SCREAMING_SNAKE_CASE_ : Tuple = False
SCREAMING_SNAKE_CASE_ : int = """tokenizer_file"""
SCREAMING_SNAKE_CASE_ : List[str] = {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
def lowercase_ ( self : List[Any])-> Dict:
'''simple docstring'''
super().setUp()
__lowerCAmelCase: Optional[Any] = BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
tokenizer.save_pretrained(self.tmpdirname)
def lowercase_ ( self : List[Any] , **UpperCamelCase__ : Union[str, Any])-> Optional[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase__)
def lowercase_ ( self : Union[str, Any])-> Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase: str = self.get_rust_tokenizer()
__lowerCAmelCase: int = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
__lowerCAmelCase: List[str] = [[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]
__lowerCAmelCase: List[str] = tokenizer.batch_encode_plus(UpperCamelCase__)["input_ids"]
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__)
__lowerCAmelCase: List[Any] = tokenizer.batch_decode(UpperCamelCase__)
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : Optional[Any] , UpperCamelCase__ : Tuple=6)-> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
__lowerCAmelCase: Optional[Any] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__)
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
__lowerCAmelCase: Dict = "This is a simple input"
__lowerCAmelCase: str = ["This is a simple input 1", "This is a simple input 2"]
__lowerCAmelCase: int = ("This is a simple input", "This is a pair")
__lowerCAmelCase: Union[str, Any] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
try:
tokenizer_r.encode(UpperCamelCase__ , max_length=UpperCamelCase__)
tokenizer_r.encode_plus(UpperCamelCase__ , max_length=UpperCamelCase__)
tokenizer_r.batch_encode_plus(UpperCamelCase__ , max_length=UpperCamelCase__)
tokenizer_r.encode(UpperCamelCase__ , max_length=UpperCamelCase__)
tokenizer_r.batch_encode_plus(UpperCamelCase__ , max_length=UpperCamelCase__)
except ValueError:
self.fail("Bloom Tokenizer should be able to deal with padding")
__lowerCAmelCase: Tuple = None # Hotfixing padding = None
self.assertRaises(UpperCamelCase__ , tokenizer_r.encode , UpperCamelCase__ , max_length=UpperCamelCase__ , padding="max_length")
# Simple input
self.assertRaises(UpperCamelCase__ , tokenizer_r.encode_plus , UpperCamelCase__ , max_length=UpperCamelCase__ , padding="max_length")
# Simple input
self.assertRaises(
UpperCamelCase__ , tokenizer_r.batch_encode_plus , UpperCamelCase__ , max_length=UpperCamelCase__ , padding="max_length" , )
# Pair input
self.assertRaises(UpperCamelCase__ , tokenizer_r.encode , UpperCamelCase__ , max_length=UpperCamelCase__ , padding="max_length")
# Pair input
self.assertRaises(UpperCamelCase__ , tokenizer_r.encode_plus , UpperCamelCase__ , max_length=UpperCamelCase__ , padding="max_length")
# Pair input
self.assertRaises(
UpperCamelCase__ , tokenizer_r.batch_encode_plus , UpperCamelCase__ , max_length=UpperCamelCase__ , padding="max_length" , )
def lowercase_ ( self : Optional[Any])-> List[str]:
'''simple docstring'''
__lowerCAmelCase: Dict = self.get_rust_tokenizer()
__lowerCAmelCase: List[str] = load_dataset("xnli" , "all_languages" , split="test" , streaming=UpperCamelCase__)
__lowerCAmelCase: Union[str, Any] = next(iter(UpperCamelCase__))["premise"] # pick up one data
__lowerCAmelCase: Any = list(sample_data.values())
__lowerCAmelCase: int = list(map(tokenizer.encode , UpperCamelCase__))
__lowerCAmelCase: str = [tokenizer.decode(UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__) for x in output_tokens]
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__)
def lowercase_ ( self : Optional[int])-> str:
'''simple docstring'''
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map) , 1)
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]) , 1)
| 217 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_A = {
'configuration_owlvit': [
'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'OwlViTConfig',
'OwlViTOnnxConfig',
'OwlViTTextConfig',
'OwlViTVisionConfig',
],
'processing_owlvit': ['OwlViTProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['OwlViTFeatureExtractor']
_A = ['OwlViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'OwlViTModel',
'OwlViTPreTrainedModel',
'OwlViTTextModel',
'OwlViTVisionModel',
'OwlViTForObjectDetection',
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 352 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_A = logging.get_logger(__name__)
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def __init__( self , *A_ , **A_ ) -> None:
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , A_ , )
super().__init__(*A_ , **A_ )
| 117 | 0 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case : int =logging.get_logger(__name__)
def lowerCAmelCase__ ( lowerCamelCase_ : Any ,lowerCamelCase_ : Optional[int]=False ,lowerCamelCase_ : Union[str, Any]=False):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = '''backbone.''' if is_semantic else ''''''
lowerCAmelCase__ : List[str] = []
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight"""))
rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias"""))
rename_keys.append(
(f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight"""))
rename_keys.append(
(f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias"""))
rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight"""))
rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias"""))
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight"""))
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias"""))
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight"""))
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias"""))
# projection layer + position embeddings
rename_keys.extend(
[
(f"""{prefix}cls_token""", '''beit.embeddings.cls_token'''),
(f"""{prefix}patch_embed.proj.weight""", '''beit.embeddings.patch_embeddings.projection.weight'''),
(f"""{prefix}patch_embed.proj.bias""", '''beit.embeddings.patch_embeddings.projection.bias'''),
(f"""{prefix}pos_embed""", '''beit.embeddings.position_embeddings'''),
])
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
])
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
])
return rename_keys
def lowerCAmelCase__ ( lowerCamelCase_ : str ,lowerCamelCase_ : Union[str, Any] ,lowerCamelCase_ : Optional[Any]=False ,lowerCamelCase_ : Tuple=False):
'''simple docstring'''
for i in range(config.num_hidden_layers):
lowerCAmelCase__ : Union[str, Any] = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
lowerCAmelCase__ : Dict = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight""")
lowerCAmelCase__ : str = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias""")
lowerCAmelCase__ : Optional[int] = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias""")
lowerCAmelCase__ : Any = in_proj_weight[
: config.hidden_size, :
]
lowerCAmelCase__ : Any = q_bias
lowerCAmelCase__ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase__ : str = in_proj_weight[
-config.hidden_size :, :
]
lowerCAmelCase__ : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
lowerCAmelCase__ : Dict = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1""")
lowerCAmelCase__ : List[str] = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2""")
lowerCAmelCase__ : Optional[Any] = gamma_a
lowerCAmelCase__ : Optional[Any] = gamma_a
def lowerCAmelCase__ ( lowerCamelCase_ : Dict ,lowerCamelCase_ : int ,lowerCamelCase_ : Union[str, Any]):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = dct.pop(lowerCamelCase_)
lowerCAmelCase__ : str = val
def lowerCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase__ : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCAmelCase__ : Dict = Image.open(requests.get(lowerCamelCase_ ,stream=lowerCamelCase_).raw)
return im
@torch.no_grad()
def lowerCAmelCase__ ( lowerCamelCase_ : List[str] ,lowerCamelCase_ : int ,lowerCamelCase_ : Tuple=False):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = False if '''rvlcdip''' in checkpoint_url else True
lowerCAmelCase__ : List[str] = BeitConfig(use_absolute_position_embeddings=lowerCamelCase_ ,use_mask_token=lowerCamelCase_)
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
lowerCAmelCase__ : Union[str, Any] = 1024
lowerCAmelCase__ : List[str] = 4096
lowerCAmelCase__ : Union[str, Any] = 24
lowerCAmelCase__ : List[str] = 16
# labels
if "rvlcdip" in checkpoint_url:
lowerCAmelCase__ : Optional[int] = 16
lowerCAmelCase__ : Optional[Any] = '''huggingface/label-files'''
lowerCAmelCase__ : str = '''rvlcdip-id2label.json'''
lowerCAmelCase__ : Union[str, Any] = json.load(open(hf_hub_download(lowerCamelCase_ ,lowerCamelCase_ ,repo_type='''dataset''') ,'''r'''))
lowerCAmelCase__ : Optional[Any] = {int(lowerCamelCase_): v for k, v in idalabel.items()}
lowerCAmelCase__ : int = idalabel
lowerCAmelCase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
lowerCAmelCase__ : List[Any] = torch.hub.load_state_dict_from_url(lowerCamelCase_ ,map_location='''cpu''')['''model''']
lowerCAmelCase__ : str = create_rename_keys(lowerCamelCase_ ,has_lm_head=lowerCamelCase_)
for src, dest in rename_keys:
rename_key(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)
read_in_q_k_v(lowerCamelCase_ ,lowerCamelCase_ ,has_lm_head=lowerCamelCase_)
# load HuggingFace model
lowerCAmelCase__ : Optional[Any] = BeitForMaskedImageModeling(lowerCamelCase_) if has_lm_head else BeitForImageClassification(lowerCamelCase_)
model.eval()
model.load_state_dict(lowerCamelCase_)
# Check outputs on an image
lowerCAmelCase__ : Tuple = BeitImageProcessor(
size=config.image_size ,resample=PILImageResampling.BILINEAR ,do_center_crop=lowerCamelCase_)
lowerCAmelCase__ : str = prepare_img()
lowerCAmelCase__ : List[Any] = image_processor(images=lowerCamelCase_ ,return_tensors='''pt''')
lowerCAmelCase__ : Tuple = encoding['''pixel_values''']
lowerCAmelCase__ : str = model(lowerCamelCase_)
lowerCAmelCase__ : List[Any] = outputs.logits
# verify logits
lowerCAmelCase__ : Dict = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
assert logits.shape == torch.Size(lowerCamelCase_), "Shape of logits not as expected"
Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
print(f"""Saving model to {pytorch_dump_folder_path}""")
model.save_pretrained(lowerCamelCase_)
print(f"""Saving image processor to {pytorch_dump_folder_path}""")
image_processor.save_pretrained(lowerCamelCase_)
if push_to_hub:
if has_lm_head:
lowerCAmelCase__ : str = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
lowerCAmelCase__ : Optional[int] = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(lowerCamelCase_ ,lowerCamelCase_) ,organization='''nielsr''' ,commit_message='''Add image processor''' ,use_temp_dir=lowerCamelCase_ ,)
model.push_to_hub(
repo_path_or_name=Path(lowerCamelCase_ ,lowerCamelCase_) ,organization='''nielsr''' ,commit_message='''Add model''' ,use_temp_dir=lowerCamelCase_ ,)
if __name__ == "__main__":
__snake_case : Union[str, Any] =argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
type=str,
help='URL to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
__snake_case : List[Any] =parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 129 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def __UpperCAmelCase ( A : List[str] , A : Any , A : Optional[int] , A : Optional[int] ) -> Optional[Any]:
if isinstance(A , A ):
UpperCAmelCase_ : Any = np.full((len(A ), sequence_length, 2) , A )
else:
UpperCAmelCase_ : int = np.full((len(A ), sequence_length) , A )
for i, tensor in enumerate(A ):
if padding_side == "right":
if isinstance(A , A ):
UpperCAmelCase_ : Tuple = tensor[:sequence_length]
else:
UpperCAmelCase_ : Dict = tensor[:sequence_length]
else:
if isinstance(A , A ):
UpperCAmelCase_ : Optional[Any] = tensor[:sequence_length]
else:
UpperCAmelCase_ : int = tensor[:sequence_length]
return out_tensor.tolist()
def __UpperCAmelCase ( A : List[Any] ) -> str:
UpperCAmelCase_ : Dict = ord(A )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
UpperCAmelCase_ : Union[str, Any] = unicodedata.category(A )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class snake_case__ ( UpperCamelCase):
a_ = 42
a_ = True
a_ = None
a_ = None
a_ = -100
a_ = "pt"
def A ( self : List[Any] , _A : Dict ) -> Tuple:
import torch
UpperCAmelCase_ : Dict = '''label''' if '''label''' in features[0].keys() else '''labels'''
UpperCAmelCase_ : List[Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCAmelCase_ : Tuple = self.tokenizer.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
UpperCAmelCase_ : Any = torch.tensor(batch['''entity_ids'''] ).shape[1]
UpperCAmelCase_ : Union[str, Any] = self.tokenizer.padding_side
if padding_side == "right":
UpperCAmelCase_ : Optional[Any] = [
list(_A ) + [self.label_pad_token_id] * (sequence_length - len(_A )) for label in labels
]
else:
UpperCAmelCase_ : Any = [
[self.label_pad_token_id] * (sequence_length - len(_A )) + list(_A ) for label in labels
]
UpperCAmelCase_ : Union[str, Any] = [feature['''ner_tags'''] for feature in features]
UpperCAmelCase_ : Union[str, Any] = padding_tensor(_A , -1 , _A , _A )
UpperCAmelCase_ : List[str] = [feature['''original_entity_spans'''] for feature in features]
UpperCAmelCase_ : int = padding_tensor(_A , (-1, -1) , _A , _A )
UpperCAmelCase_ : Union[str, Any] = {k: torch.tensor(_A , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 304 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase=13 , lowerCamelCase=7 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=99 , lowerCamelCase=64 , lowerCamelCase=5 , lowerCamelCase=4 , lowerCamelCase=37 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=512 , lowerCamelCase=16 , lowerCamelCase=2 , lowerCamelCase=0.02 , lowerCamelCase=3 , lowerCamelCase=4 , lowerCamelCase=None , ):
'''simple docstring'''
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = scope
_lowerCAmelCase = vocab_size - 1
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = self.get_config()
return config, input_ids, input_mask, token_labels
def A__ (self ):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase = True
return config, input_ids, input_mask, token_labels
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = GPTNeoXModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase )
_lowerCAmelCase = model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = True
_lowerCAmelCase = GPTNeoXModel(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = GPTNeoXForCausalLM(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = GPTNeoXForQuestionAnswering(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = GPTNeoXForSequenceClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = GPTNeoXForTokenClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = True
_lowerCAmelCase = GPTNeoXForCausalLM(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
# first forward pass
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , use_cache=lowerCamelCase )
_lowerCAmelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_lowerCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_lowerCAmelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_lowerCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_lowerCAmelCase = torch.cat([input_mask, next_mask] , dim=-1 )
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , output_hidden_states=lowerCamelCase )
_lowerCAmelCase = output_from_no_past["""hidden_states"""][0]
_lowerCAmelCase = model(
lowerCamelCase , attention_mask=lowerCamelCase , past_key_values=lowerCamelCase , output_hidden_states=lowerCamelCase , )["""hidden_states"""][0]
# select random slice
_lowerCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_lowerCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
_lowerCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-3 ) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __lowerCamelCase ( __lowercase , __lowercase , __lowercase , unittest.TestCase ):
__UpperCamelCase = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (GPTNeoXForCausalLM,) if is_torch_available() else ()
__UpperCamelCase = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = GPTNeoXModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=lowerCamelCase , hidden_size=64 , num_attention_heads=8 )
def A__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
_lowerCAmelCase = None
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
@unittest.skip(reason="""Feed forward chunking is not implemented""" )
def A__ (self ):
'''simple docstring'''
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = ids_tensor([1, 10] , config.vocab_size )
_lowerCAmelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase = GPTNeoXModel(lowerCamelCase )
original_model.to(lowerCamelCase )
original_model.eval()
_lowerCAmelCase = original_model(lowerCamelCase ).last_hidden_state
_lowerCAmelCase = original_model(lowerCamelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase = {"""type""": scaling_type, """factor""": 10.0}
_lowerCAmelCase = GPTNeoXModel(lowerCamelCase )
scaled_model.to(lowerCamelCase )
scaled_model.eval()
_lowerCAmelCase = scaled_model(lowerCamelCase ).last_hidden_state
_lowerCAmelCase = scaled_model(lowerCamelCase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-5 ) )
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
for checkpointing in [True, False]:
_lowerCAmelCase = GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCamelCase )
_lowerCAmelCase = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(lowerCamelCase )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
_lowerCAmelCase = """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""
_lowerCAmelCase = model.generate(**lowerCamelCase , do_sample=lowerCamelCase , max_new_tokens=20 )
_lowerCAmelCase = tokenizer.batch_decode(lowerCamelCase )[0]
self.assertEqual(lowerCamelCase , lowerCamelCase ) | 317 |
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
_lowerCAmelCase = limit + 1
_lowerCAmelCase = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase = 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
_lowerCAmelCase = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'{solution() = }') | 317 | 1 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if not isinstance(snake_case , snake_case ):
_lowerCAmelCase = F'Input value of [number={number}] must be an integer'
raise TypeError(snake_case )
if number < 0:
return False
_lowerCAmelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 |
from math import pi, sqrt, tan
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
__lowerCamelCase = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(UpperCamelCase__ , 2 ) * torus_radius * tube_radius
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
__lowerCamelCase = (sidea + sidea + sidea) / 2
__lowerCamelCase = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f'''Rectangle: {area_rectangle(10, 20) = }''')
print(f'''Square: {area_square(10) = }''')
print(f'''Triangle: {area_triangle(10, 10) = }''')
print(f'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(f'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(f'''Rhombus: {area_rhombus(10, 20) = }''')
print(f'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(f'''Circle: {area_circle(20) = }''')
print(f'''Ellipse: {area_ellipse(10, 20) = }''')
print("\nSurface Areas of various geometric shapes: \n")
print(f'''Cube: {surface_area_cube(20) = }''')
print(f'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(f'''Sphere: {surface_area_sphere(20) = }''')
print(f'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(f'''Cone: {surface_area_cone(10, 20) = }''')
print(f'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(f'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(f'''Torus: {surface_area_torus(20, 10) = }''')
print(f'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(f'''Square: {area_reg_polygon(4, 10) = }''')
print(f'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')
| 90 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class lowerCAmelCase__ ( __magic_name__ ):
SCREAMING_SNAKE_CASE_ ='''Salesforce/blip-image-captioning-base'''
SCREAMING_SNAKE_CASE_ =(
'''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
'''image to caption, and returns a text that contains the description in English.'''
)
SCREAMING_SNAKE_CASE_ ='''image_captioner'''
SCREAMING_SNAKE_CASE_ =AutoModelForVisionaSeq
SCREAMING_SNAKE_CASE_ =['''image''']
SCREAMING_SNAKE_CASE_ =['''text''']
def __init__( self : str , *snake_case__ : Dict , **snake_case__ : Tuple ):
'''simple docstring'''
requires_backends(self , ["vision"] )
super().__init__(*snake_case__ , **snake_case__ )
def __a ( self : Tuple , snake_case__ : "Image" ):
'''simple docstring'''
return self.pre_processor(images=snake_case__ , return_tensors="pt" )
def __a ( self : List[Any] , snake_case__ : Optional[int] ):
'''simple docstring'''
return self.model.generate(**snake_case__ )
def __a ( self : str , snake_case__ : str ):
'''simple docstring'''
return self.pre_processor.batch_decode(snake_case__ , skip_special_tokens=snake_case__ )[0].strip()
| 351 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class lowerCAmelCase__ :
def __init__( self : Any , snake_case__ : Union[str, Any] , snake_case__ : str=1_0_0 , snake_case__ : str=1_3 , snake_case__ : Optional[int]=3_0 , snake_case__ : List[Any]=2 , snake_case__ : Any=3 , snake_case__ : Union[str, Any]=True , snake_case__ : List[Any]=True , snake_case__ : Any=3_2 , snake_case__ : List[str]=4 , snake_case__ : Any=4 , snake_case__ : Dict=3_7 , snake_case__ : str="gelu" , snake_case__ : Union[str, Any]=0.1 , snake_case__ : int=0.1 , snake_case__ : List[Any]=1_0 , snake_case__ : Any=0.02 , snake_case__ : List[str]=3 , snake_case__ : Tuple=None , snake_case__ : Tuple=[0, 1, 2, 3] , ):
'''simple docstring'''
UpperCAmelCase__ : int = parent
UpperCAmelCase__ : List[str] = 1_0_0
UpperCAmelCase__ : List[Any] = batch_size
UpperCAmelCase__ : int = image_size
UpperCAmelCase__ : List[Any] = patch_size
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Any = is_training
UpperCAmelCase__ : str = use_labels
UpperCAmelCase__ : Any = hidden_size
UpperCAmelCase__ : Dict = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : str = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[int] = type_sequence_label_size
UpperCAmelCase__ : Any = initializer_range
UpperCAmelCase__ : Any = scope
UpperCAmelCase__ : Optional[Any] = out_indices
UpperCAmelCase__ : int = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase__ : List[Any] = (image_size // patch_size) ** 2
UpperCAmelCase__ : Optional[int] = num_patches + 1
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : str = None
UpperCAmelCase__ : Optional[int] = None
if self.use_labels:
UpperCAmelCase__ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Any = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCAmelCase__ : Tuple = self.get_config()
return config, pixel_values, labels, pixel_labels
def __a ( self : int ):
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def __a ( self : int , snake_case__ : str , snake_case__ : str , snake_case__ : Dict , snake_case__ : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = BeitModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : Dict = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self : Any , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : Dict , snake_case__ : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = BeitForMaskedImageModeling(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : List[Any] = model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __a ( self : Optional[Any] , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : str , snake_case__ : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.type_sequence_label_size
UpperCAmelCase__ : Union[str, Any] = BeitForImageClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase__ : Any = 1
UpperCAmelCase__ : List[Any] = BeitForImageClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[Any] = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __a ( self : Union[str, Any] , snake_case__ : int , snake_case__ : str , snake_case__ : Any , snake_case__ : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.num_labels
UpperCAmelCase__ : int = BeitForSemanticSegmentation(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase__ : int = model(snake_case__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
UpperCAmelCase__ : Dict = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def __a ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[str] = config_and_inputs
UpperCAmelCase__ : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ =(
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ =(
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ =False
SCREAMING_SNAKE_CASE_ =False
SCREAMING_SNAKE_CASE_ =False
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Dict = BeitModelTester(self )
UpperCAmelCase__ : List[str] = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=3_7 )
def __a ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="BEiT does not use inputs_embeds" )
def __a ( self : List[Any] ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def __a ( self : List[str] ):
'''simple docstring'''
pass
def __a ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Dict = model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , nn.Linear ) )
def __a ( self : str ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(snake_case__ )
UpperCAmelCase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : str = [*signature.parameters.keys()]
UpperCAmelCase__ : int = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case__ )
def __a ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case__ )
def __a ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__ )
def __a ( self : List[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Optional[int] = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(snake_case__ ), BeitForMaskedImageModeling]:
continue
UpperCAmelCase__ : Optional[Any] = model_class(snake_case__ )
model.to(snake_case__ )
model.train()
UpperCAmelCase__ : Optional[int] = self._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
UpperCAmelCase__ : Tuple = model(**snake_case__ ).loss
loss.backward()
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : List[str] = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(snake_case__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
UpperCAmelCase__ : List[Any] = model_class(snake_case__ )
model.gradient_checkpointing_enable()
model.to(snake_case__ )
model.train()
UpperCAmelCase__ : Dict = self._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
UpperCAmelCase__ : Optional[Any] = model(**snake_case__ ).loss
loss.backward()
def __a ( self : str ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Union[str, Any] = _config_zero_init(snake_case__ )
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(config=snake_case__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@slow
def __a ( self : Any ):
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Optional[Any] = BeitModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def SCREAMING_SNAKE_CASE__ ( )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
@cached_property
def __a ( self : Union[str, Any] ):
'''simple docstring'''
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(snake_case__ )
UpperCAmelCase__ : int = self.default_image_processor
UpperCAmelCase__ : List[Any] = prepare_img()
UpperCAmelCase__ : Dict = image_processor(images=snake_case__ , return_tensors="pt" ).pixel_values.to(snake_case__ )
# prepare bool_masked_pos
UpperCAmelCase__ : Union[str, Any] = torch.ones((1, 1_9_6) , dtype=torch.bool ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(pixel_values=snake_case__ , bool_masked_pos=snake_case__ )
UpperCAmelCase__ : str = outputs.logits
# verify the logits
UpperCAmelCase__ : int = torch.Size((1, 1_9_6, 8_1_9_2) )
self.assertEqual(logits.shape , snake_case__ )
UpperCAmelCase__ : Any = torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , snake_case__ , atol=1e-2 ) )
@slow
def __a ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(snake_case__ )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : Dict = prepare_img()
UpperCAmelCase__ : Tuple = image_processor(images=snake_case__ , return_tensors="pt" ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Union[str, Any] = model(**snake_case__ )
UpperCAmelCase__ : Any = outputs.logits
# verify the logits
UpperCAmelCase__ : Optional[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(logits.shape , snake_case__ )
UpperCAmelCase__ : Optional[Any] = torch.tensor([-1.2385, -1.0987, -1.0108] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[0, :3] , snake_case__ , atol=1e-4 ) )
UpperCAmelCase__ : List[str] = 2_8_1
self.assertEqual(logits.argmax(-1 ).item() , snake_case__ )
@slow
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : int = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to(
snake_case__ )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : Any = prepare_img()
UpperCAmelCase__ : Union[str, Any] = image_processor(images=snake_case__ , return_tensors="pt" ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[Any] = model(**snake_case__ )
UpperCAmelCase__ : int = outputs.logits
# verify the logits
UpperCAmelCase__ : int = torch.Size((1, 2_1_8_4_1) )
self.assertEqual(logits.shape , snake_case__ )
UpperCAmelCase__ : int = torch.tensor([1.6881, -0.2787, 0.5901] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[0, :3] , snake_case__ , atol=1e-4 ) )
UpperCAmelCase__ : Any = 2_3_9_6
self.assertEqual(logits.argmax(-1 ).item() , snake_case__ )
@slow
def __a ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
UpperCAmelCase__ : List[Any] = model.to(snake_case__ )
UpperCAmelCase__ : int = BeitImageProcessor(do_resize=snake_case__ , size=6_4_0 , do_center_crop=snake_case__ )
UpperCAmelCase__ : Any = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
UpperCAmelCase__ : List[Any] = Image.open(ds[0]["file"] )
UpperCAmelCase__ : str = image_processor(images=snake_case__ , return_tensors="pt" ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**snake_case__ )
UpperCAmelCase__ : Dict = outputs.logits
# verify the logits
UpperCAmelCase__ : Any = torch.Size((1, 1_5_0, 1_6_0, 1_6_0) )
self.assertEqual(logits.shape , snake_case__ )
UpperCAmelCase__ : List[str] = version.parse(PIL.__version__ ) < version.parse("9.0.0" )
if is_pillow_less_than_a:
UpperCAmelCase__ : Optional[Any] = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=snake_case__ , )
else:
UpperCAmelCase__ : int = torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=snake_case__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , snake_case__ , atol=1e-4 ) )
@slow
def __a ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : str = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
UpperCAmelCase__ : Any = model.to(snake_case__ )
UpperCAmelCase__ : Dict = BeitImageProcessor(do_resize=snake_case__ , size=6_4_0 , do_center_crop=snake_case__ )
UpperCAmelCase__ : Tuple = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
UpperCAmelCase__ : Optional[int] = Image.open(ds[0]["file"] )
UpperCAmelCase__ : Optional[int] = image_processor(images=snake_case__ , return_tensors="pt" ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**snake_case__ )
UpperCAmelCase__ : int = outputs.logits.detach().cpu()
UpperCAmelCase__ : str = image_processor.post_process_semantic_segmentation(outputs=snake_case__ , target_sizes=[(5_0_0, 3_0_0)] )
UpperCAmelCase__ : List[Any] = torch.Size((5_0_0, 3_0_0) )
self.assertEqual(segmentation[0].shape , snake_case__ )
UpperCAmelCase__ : Any = image_processor.post_process_semantic_segmentation(outputs=snake_case__ )
UpperCAmelCase__ : int = torch.Size((1_6_0, 1_6_0) )
self.assertEqual(segmentation[0].shape , snake_case__ )
| 298 | 0 |
def _a ( UpperCamelCase_ : int , UpperCamelCase_ : int ) -> float:
"""simple docstring"""
return base * power(__a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
a_ = int(input('''Enter the base: ''').strip())
a_ = int(input('''Enter the exponent: ''').strip())
a_ = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
a_ = 1 / result
print(F"{base} to the power of {exponent} is {result}")
| 340 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class A (unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : List[str]=13 , __lowerCAmelCase : int=7 , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : int=True , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : List[Any]=99 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Optional[Any]=5 , __lowerCAmelCase : Tuple=4 , __lowerCAmelCase : Any=37 , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Optional[Any]=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=5_12 , __lowerCAmelCase : int=16 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : List[Any]=0.0_2 , __lowerCAmelCase : Tuple=4 , ) -> Dict:
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_attention_mask
A__ = use_token_type_ids
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = type_sequence_label_size
A__ = initializer_range
A__ = num_choices
def a_ ( self : Any ) -> str:
"""simple docstring"""
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = None
if self.use_attention_mask:
A__ = random_attention_mask([self.batch_size, self.seq_length] )
A__ = None
if self.use_token_type_ids:
A__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A__ = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a_ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ , A__ = config_and_inputs
A__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class A (SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a_ ( self : str ) -> Optional[int]:
"""simple docstring"""
A__ = FlaxAlbertModelTester(self )
@slow
def a_ ( self : int ) -> Tuple:
"""simple docstring"""
for model_class_name in self.all_model_classes:
A__ = model_class_name.from_pretrained("""albert-base-v2""" )
A__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(__lowerCAmelCase )
@require_flax
class A (unittest.TestCase ):
'''simple docstring'''
@slow
def a_ ( self : Dict ) -> List[Any]:
"""simple docstring"""
A__ = FlaxAlbertModel.from_pretrained("""albert-base-v2""" )
A__ = np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
A__ = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
A__ = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase )[0]
A__ = (1, 11, 7_68)
self.assertEqual(output.shape , __lowerCAmelCase )
A__ = np.array(
[[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __lowerCAmelCase , atol=1e-4 ) )
| 274 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : str = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
'''tokenization_convbert''': ['''ConvBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : str = ['''ConvBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Any = [
'''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvBertForMaskedLM''',
'''ConvBertForMultipleChoice''',
'''ConvBertForQuestionAnswering''',
'''ConvBertForSequenceClassification''',
'''ConvBertForTokenClassification''',
'''ConvBertLayer''',
'''ConvBertModel''',
'''ConvBertPreTrainedModel''',
'''load_tf_weights_in_convbert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[Any] = [
'''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFConvBertForMaskedLM''',
'''TFConvBertForMultipleChoice''',
'''TFConvBertForQuestionAnswering''',
'''TFConvBertForSequenceClassification''',
'''TFConvBertForTokenClassification''',
'''TFConvBertLayer''',
'''TFConvBertModel''',
'''TFConvBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : int = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class A_ ( _a ):
lowerCAmelCase__ = 'mobilenet_v1'
def __init__( self: Tuple ,__lowerCAmelCase: int=3 ,__lowerCAmelCase: Dict=224 ,__lowerCAmelCase: int=1.0 ,__lowerCAmelCase: Tuple=8 ,__lowerCAmelCase: List[str]="relu6" ,__lowerCAmelCase: int=True ,__lowerCAmelCase: List[Any]=0.9_99 ,__lowerCAmelCase: Optional[int]=0.02 ,__lowerCAmelCase: Optional[int]=0.0_01 ,**__lowerCAmelCase: str ,):
'''simple docstring'''
super().__init__(**__lowerCAmelCase )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
_lowerCamelCase : List[str] = num_channels
_lowerCamelCase : Union[str, Any] = image_size
_lowerCamelCase : List[Any] = depth_multiplier
_lowerCamelCase : Any = min_depth
_lowerCamelCase : Tuple = hidden_act
_lowerCamelCase : Dict = tf_padding
_lowerCamelCase : Union[str, Any] = classifier_dropout_prob
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : List[Any] = layer_norm_eps
class A_ ( _a ):
lowerCAmelCase__ = version.parse('1.11' )
@property
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("logits", {0: "batch"})] )
else:
return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )
@property
def _lowercase ( self: Any ):
'''simple docstring'''
return 1e-4 | 340 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__lowerCAmelCase : Dict = logging.get_logger('transformers.models.speecht5')
def a__ ( A_, A_, A_ ):
'''simple docstring'''
hf_model.apply_weight_norm()
__magic_name__ = checkpoint["""input_conv.weight_g"""]
__magic_name__ = checkpoint["""input_conv.weight_v"""]
__magic_name__ = checkpoint["""input_conv.bias"""]
for i in range(len(config.upsample_rates ) ):
__magic_name__ = checkpoint[f'''upsamples.{i}.1.weight_g''']
__magic_name__ = checkpoint[f'''upsamples.{i}.1.weight_v''']
__magic_name__ = checkpoint[f'''upsamples.{i}.1.bias''']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
__magic_name__ = checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
__magic_name__ = checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
__magic_name__ = checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']
__magic_name__ = checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
__magic_name__ = checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
__magic_name__ = checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']
__magic_name__ = checkpoint["""output_conv.1.weight_g"""]
__magic_name__ = checkpoint["""output_conv.1.weight_v"""]
__magic_name__ = checkpoint["""output_conv.1.bias"""]
hf_model.remove_weight_norm()
@torch.no_grad()
def a__ ( A_, A_, A_, A_=None, A_=None, ):
'''simple docstring'''
if config_path is not None:
__magic_name__ = SpeechTaHifiGanConfig.from_pretrained(A_ )
else:
__magic_name__ = SpeechTaHifiGanConfig()
__magic_name__ = SpeechTaHifiGan(A_ )
__magic_name__ = torch.load(A_ )
load_weights(orig_checkpoint["""model"""]["""generator"""], A_, A_ )
__magic_name__ = np.load(A_ )
__magic_name__ = stats[0].reshape(-1 )
__magic_name__ = stats[1].reshape(-1 )
__magic_name__ = torch.from_numpy(A_ ).float()
__magic_name__ = torch.from_numpy(A_ ).float()
model.save_pretrained(A_ )
if repo_id:
print("""Pushing to the hub...""" )
model.push_to_hub(A_ )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
__lowerCAmelCase : List[Any] = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 88 |
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(A_ ) == 0:
raise ValueError("""Input list must be a non empty list""" )
if len(A_ ) == 1:
return True
__magic_name__ = series[1] - series[0]
for index in range(len(A_ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(A_ ) == 0:
raise ValueError("""Input list must be a non empty list""" )
__magic_name__ = 0
for val in series:
answer += val
return answer / len(A_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88 | 1 |
from maths.prime_factors import prime_factors
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
if not isinstance(__a , __a ):
A__ = F'Input value of [number={number}] must be an integer'
raise TypeError(__a )
if number < 1:
raise ValueError("""Input must be a positive integer""" )
return -1 if len(prime_factors(__a ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 276 |
import math
def __lowerCamelCase ( __a :int ) -> bool:
"""simple docstring"""
A__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__a )
def __lowerCamelCase ( __a :float = 1 / 1_2_3_4_5 ) -> int:
"""simple docstring"""
A__ = 0
A__ = 0
A__ = 3
while True:
A__ = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__a ):
A__ = int(__a )
total_partitions += 1
if check_partition_perfect(__a ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__a )
integer += 1
if __name__ == "__main__":
print(F'''{solution() = }''')
| 276 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> List[str]:
"""simple docstring"""
_UpperCAmelCase = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')
_UpperCAmelCase = (
('layer.', 'layer_'),
('word_embeddings.weight', 'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'),
('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'),
('weight', 'kernel'),
)
if not os.path.isdir(__lowerCAmelCase ):
os.makedirs(__lowerCAmelCase )
_UpperCAmelCase = model.state_dict()
def to_tf_var_name(__lowerCAmelCase ):
for patt, repl in iter(__lowerCAmelCase ):
_UpperCAmelCase = name.replace(__lowerCAmelCase , __lowerCAmelCase )
return F"""bert/{name}"""
def create_tf_var(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase = tf.dtypes.as_dtype(tensor.dtype )
_UpperCAmelCase = tf.get_variable(dtype=__lowerCAmelCase , shape=tensor.shape , name=__lowerCAmelCase , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__lowerCAmelCase )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
_UpperCAmelCase = to_tf_var_name(__lowerCAmelCase )
_UpperCAmelCase = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
_UpperCAmelCase = torch_tensor.T
_UpperCAmelCase = create_tf_var(tensor=__lowerCAmelCase , name=__lowerCAmelCase , session=__lowerCAmelCase )
tf.keras.backend.set_value(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = session.run(__lowerCAmelCase )
print(F"""Successfully created {tf_name}: {np.allclose(__lowerCAmelCase , __lowerCAmelCase )}""" )
_UpperCAmelCase = tf.train.Saver(tf.trainable_variables() )
saver.save(__lowerCAmelCase , os.path.join(__lowerCAmelCase , model_name.replace('-' , '_' ) + '.ckpt' ) )
def __A ( __lowerCAmelCase=None )-> Any:
"""simple docstring"""
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=__lowerCAmelCase , required=__lowerCAmelCase , help='model name e.g. bert-base-uncased' )
parser.add_argument(
'--cache_dir' , type=__lowerCAmelCase , default=__lowerCAmelCase , required=__lowerCAmelCase , help='Directory containing pytorch model' )
parser.add_argument('--pytorch_model_path' , type=__lowerCAmelCase , required=__lowerCAmelCase , help='/path/to/<pytorch-model-name>.bin' )
parser.add_argument('--tf_cache_dir' , type=__lowerCAmelCase , required=__lowerCAmelCase , help='Directory in which to save tensorflow model' )
_UpperCAmelCase = parser.parse_args(__lowerCAmelCase )
_UpperCAmelCase = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=__lowerCAmelCase , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 39 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : Optional[Any] = '''focalnet'''
def __init__( self ,__UpperCAmelCase=224 ,__UpperCAmelCase=4 ,__UpperCAmelCase=3 ,__UpperCAmelCase=96 ,__UpperCAmelCase=False ,__UpperCAmelCase=[192, 384, 768, 768] ,__UpperCAmelCase=[2, 2, 6, 2] ,__UpperCAmelCase=[2, 2, 2, 2] ,__UpperCAmelCase=[3, 3, 3, 3] ,__UpperCAmelCase="gelu" ,__UpperCAmelCase=4.0 ,__UpperCAmelCase=0.0 ,__UpperCAmelCase=0.1 ,__UpperCAmelCase=False ,__UpperCAmelCase=1E-4 ,__UpperCAmelCase=False ,__UpperCAmelCase=False ,__UpperCAmelCase=False ,__UpperCAmelCase=0.0_2 ,__UpperCAmelCase=1E-5 ,__UpperCAmelCase=32 ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,**__UpperCAmelCase ,) -> Optional[Any]:
super().__init__(**__UpperCAmelCase )
lowerCAmelCase__ : Dict = image_size
lowerCAmelCase__ : int = patch_size
lowerCAmelCase__ : str = num_channels
lowerCAmelCase__ : Dict = embed_dim
lowerCAmelCase__ : List[str] = use_conv_embed
lowerCAmelCase__ : List[Any] = hidden_sizes
lowerCAmelCase__ : Dict = depths
lowerCAmelCase__ : List[str] = focal_levels
lowerCAmelCase__ : List[str] = focal_windows
lowerCAmelCase__ : Dict = hidden_act
lowerCAmelCase__ : Dict = mlp_ratio
lowerCAmelCase__ : Tuple = hidden_dropout_prob
lowerCAmelCase__ : Tuple = drop_path_rate
lowerCAmelCase__ : Dict = use_layerscale
lowerCAmelCase__ : Optional[Any] = layerscale_value
lowerCAmelCase__ : str = use_post_layernorm
lowerCAmelCase__ : Union[str, Any] = use_post_layernorm_in_modulation
lowerCAmelCase__ : int = normalize_modulator
lowerCAmelCase__ : Optional[Any] = initializer_range
lowerCAmelCase__ : List[str] = layer_norm_eps
lowerCAmelCase__ : List[Any] = encoder_stride
lowerCAmelCase__ : Dict = ["""stem"""] + [F"""stage{idx}""" for idx in range(1 ,len(self.depths ) + 1 )]
lowerCAmelCase__ , lowerCAmelCase__ : Any = get_aligned_output_features_output_indices(
out_features=__UpperCAmelCase ,out_indices=__UpperCAmelCase ,stage_names=self.stage_names )
| 37 | 0 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Tuple:
A_ = [False] * len(UpperCAmelCase__ )
A_ = [-1] * len(UpperCAmelCase__ )
def dfs(UpperCAmelCase__, UpperCAmelCase__ ):
A_ = True
A_ = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCAmelCase__, 1 - c )
for i in range(len(UpperCAmelCase__ ) ):
if not visited[i]:
dfs(UpperCAmelCase__, 0 )
for i in range(len(UpperCAmelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__lowerCamelCase = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 101 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class A__ ( _snake_case ):
def snake_case_ ( self ) -> Dict:
'''simple docstring'''
A_ = SMALL_MODEL_IDENTIFIER
A_ = """pt"""
A_ = """tf"""
def snake_case_ ( self , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
A_ = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
A_ = TFAutoModel.from_pretrained(self.test_model , from_pt=UpperCamelCase__ )
model_tf.save_pretrained(UpperCamelCase__ )
def snake_case_ ( self ) -> Dict:
'''simple docstring'''
A_ = """mock_framework"""
# Framework provided - return whatever the user provides
A_ = FeaturesManager.determine_framework(self.test_model , UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(UpperCamelCase__ )
A_ = FeaturesManager.determine_framework(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(UpperCamelCase__ )
A_ = FeaturesManager.determine_framework(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
def snake_case_ ( self ) -> int:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(UpperCamelCase__ )
A_ = FeaturesManager.determine_framework(UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(UpperCamelCase__ )
A_ = FeaturesManager.determine_framework(UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(UpperCamelCase__ ):
A_ = FeaturesManager.determine_framework(UpperCamelCase__ )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = MagicMock(return_value=UpperCamelCase__ )
with patch("""transformers.onnx.features.is_tf_available""" , UpperCamelCase__ ):
A_ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(UpperCamelCase__ , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
A_ = MagicMock(return_value=UpperCamelCase__ )
with patch("""transformers.onnx.features.is_torch_available""" , UpperCamelCase__ ):
A_ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(UpperCamelCase__ , self.framework_tf )
# Both in environment -> use PyTorch
A_ = MagicMock(return_value=UpperCamelCase__ )
A_ = MagicMock(return_value=UpperCamelCase__ )
with patch("""transformers.onnx.features.is_tf_available""" , UpperCamelCase__ ), patch(
"""transformers.onnx.features.is_torch_available""" , UpperCamelCase__ ):
A_ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(UpperCamelCase__ , self.framework_pt )
# Both not in environment -> raise error
A_ = MagicMock(return_value=UpperCamelCase__ )
A_ = MagicMock(return_value=UpperCamelCase__ )
with patch("""transformers.onnx.features.is_tf_available""" , UpperCamelCase__ ), patch(
"""transformers.onnx.features.is_torch_available""" , UpperCamelCase__ ):
with self.assertRaises(UpperCamelCase__ ):
A_ = FeaturesManager.determine_framework(self.test_model )
| 101 | 1 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
lowercase__ : Optional[int] = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
lowercase__ : Union[str, Any] = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
lowercase__ : Tuple = R"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self : str ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'] , )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : int=False ):
lowerCAmelCase_ : List[Any] = spearmanr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 224 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowercase__ : List[str] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def UpperCamelCase_ ( lowerCAmelCase__ : Dict ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ : List[Any] = test_results.split(' ' )
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : Any = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
lowerCAmelCase_ : Any = expressions[-2] if '=' in expressions[-1] else expressions[-1]
for i, expression in enumerate(lowerCAmelCase__ ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def UpperCamelCase_ ( lowerCAmelCase__ : Dict ) -> str:
"""simple docstring"""
lowerCAmelCase_ : Optional[Any] = {}
lowerCAmelCase_ : List[str] = None
lowerCAmelCase_ : List[str] = False
for line in failures_short_lines.split('\n' ):
if re.search(R'_ \[doctest\]' , lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : Union[str, Any] = line.split(' ' )[2]
elif in_error and not line.split(' ' )[0].isdigit():
lowerCAmelCase_ : Union[str, Any] = line
lowerCAmelCase_ : int = False
return failures
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict ):
lowerCAmelCase_ : str = title
lowerCAmelCase_ : Optional[int] = doc_test_results['time_spent'].split(',' )[0]
lowerCAmelCase_ : int = doc_test_results['success']
lowerCAmelCase_ : Dict = doc_test_results['failures']
lowerCAmelCase_ : Optional[int] = self.n_success + self.n_failures
# Failures and success of the modeling tests
lowerCAmelCase_ : Any = doc_test_results
@property
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : int = [self._time_spent]
lowerCAmelCase_ : Any = 0
for time in time_spent:
lowerCAmelCase_ : Optional[Any] = time.split(':' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(SCREAMING_SNAKE_CASE_ ) == 1:
lowerCAmelCase_ : Any = [0, 0, time_parts[0]]
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ : List[str] = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ : Union[str, Any] = total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
return F"{int(SCREAMING_SNAKE_CASE_ )}h{int(SCREAMING_SNAKE_CASE_ )}m{int(SCREAMING_SNAKE_CASE_ )}s"
@property
def SCREAMING_SNAKE_CASE__ ( self : int ):
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def SCREAMING_SNAKE_CASE__ ( self : str ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": F"🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.",
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
F"There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"
F" {self.time}."
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
lowerCAmelCase_ : int = 4_0
lowerCAmelCase_ : List[Any] = {k: v['failed'] for k, v in doc_test_results.items() if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )}
lowerCAmelCase_ : List[str] = ''
for category, failures in category_failures.items():
if len(SCREAMING_SNAKE_CASE_ ) == 0:
continue
if report != "":
report += "\n\n"
report += F"*{category} failures*:".ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(SCREAMING_SNAKE_CASE_ )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": F"The following examples had failures:\n\n\n{report}\n",
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self : int ):
lowerCAmelCase_ : int = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(SCREAMING_SNAKE_CASE_ )
@staticmethod
def SCREAMING_SNAKE_CASE__ ( ):
lowerCAmelCase_ : Tuple = [
{
'type': 'section',
'text': {
'type': 'plain_text',
'text': 'There was an issue running the tests.',
},
'accessory': {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
'url': F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
},
}
]
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(SCREAMING_SNAKE_CASE_ )} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , text='There was an issue running the tests.' , blocks=SCREAMING_SNAKE_CASE_ , )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(self.payload )} ) )
lowerCAmelCase_ : Any = F"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else 'All tests passed.'
lowerCAmelCase_ : str = client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , blocks=self.payload , text=SCREAMING_SNAKE_CASE_ , )
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Dict ):
lowerCAmelCase_ : List[Any] = ''
for key, value in failures.items():
lowerCAmelCase_ : List[Any] = value[:2_0_0] + ' [Truncated]' if len(SCREAMING_SNAKE_CASE_ ) > 2_5_0 else value
failures_text += F"*{key}*\n_{value}_\n\n"
lowerCAmelCase_ : int = job_name
lowerCAmelCase_ : Dict = {'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}
if job_link is not None:
lowerCAmelCase_ : str = {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
'url': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
if self.thread_ts is None:
raise ValueError('Can only post reply if a post has been made.' )
lowerCAmelCase_ : Dict = self.doc_test_results.pop('job_link' )
self.doc_test_results.pop('failures' )
self.doc_test_results.pop('success' )
self.doc_test_results.pop('time_spent' )
lowerCAmelCase_ : Dict = sorted(self.doc_test_results.items() , key=lambda SCREAMING_SNAKE_CASE_ : t[0] )
for job, job_result in sorted_dict:
if len(job_result['failures'] ):
lowerCAmelCase_ : Tuple = F"*Num failures* :{len(job_result['failed'] )} \n"
lowerCAmelCase_ : List[str] = job_result['failures']
lowerCAmelCase_ : Union[str, Any] = self.get_reply_blocks(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , text=SCREAMING_SNAKE_CASE_ )
print('Sending the following reply' )
print(json.dumps({'blocks': blocks} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , text=F"Results for {job}" , blocks=SCREAMING_SNAKE_CASE_ , thread_ts=self.thread_ts['ts'] , )
time.sleep(1 )
def UpperCamelCase_ ( ) -> str:
"""simple docstring"""
lowerCAmelCase_ : Union[str, Any] = os.environ['GITHUB_RUN_ID']
lowerCAmelCase_ : int = f"https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"
lowerCAmelCase_ : str = requests.get(lowerCAmelCase__ ).json()
lowerCAmelCase_ : List[str] = {}
try:
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
lowerCAmelCase_ : Optional[Any] = math.ceil((result['total_count'] - 100) / 100 )
for i in range(lowerCAmelCase__ ):
lowerCAmelCase_ : int = requests.get(url + f"&page={i + 2}" ).json()
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
return jobs
except Exception as e:
print('Unknown error, could not fetch links.' , lowerCAmelCase__ )
return {}
def UpperCamelCase_ ( lowerCAmelCase__ : str ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase_ : int = {}
if os.path.exists(lowerCAmelCase__ ):
lowerCAmelCase_ : Tuple = os.listdir(lowerCAmelCase__ )
for file in files:
try:
with open(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) , encoding='utf-8' ) as f:
lowerCAmelCase_ : List[str] = f.read()
except UnicodeDecodeError as e:
raise ValueError(f"Could not open {os.path.join(lowerCAmelCase__ , lowerCAmelCase__ )}." ) from e
return _artifact
def UpperCamelCase_ ( ) -> Dict:
"""simple docstring"""
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : str ):
lowerCAmelCase_ : List[Any] = name
lowerCAmelCase_ : Tuple = []
def __str__( self : Optional[Any] ):
return self.name
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , SCREAMING_SNAKE_CASE_ : str ):
self.paths.append({'name': self.name, 'path': path} )
lowerCAmelCase_ : Dict[str, Artifact] = {}
lowerCAmelCase_ : Any = filter(os.path.isdir , os.listdir() )
for directory in directories:
lowerCAmelCase_ : int = directory
if artifact_name not in _available_artifacts:
lowerCAmelCase_ : Optional[Any] = Artifact(lowerCAmelCase__ )
_available_artifacts[artifact_name].add_path(lowerCAmelCase__ )
return _available_artifacts
if __name__ == "__main__":
lowercase__ : Optional[int] = get_job_links()
lowercase__ : Any = retrieve_available_artifacts()
lowercase__ : str = collections.OrderedDict(
[
("""*.py""", """API Examples"""),
("""*.md""", """MD Examples"""),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
lowercase__ : Dict = {
v: {
"""failed""": [],
"""failures""": {},
}
for v in docs.values()
}
# Link to the GitHub Action job
lowercase__ : str = github_actions_job_links.get("""run_doctests""")
lowercase__ : int = available_artifacts["""doc_tests_gpu_test_reports"""].paths[0]
lowercase__ : List[str] = retrieve_artifact(artifact_path["""name"""])
if "stats" in artifact:
lowercase__ , lowercase__ , lowercase__ : str = handle_test_results(artifact["""stats"""])
lowercase__ : Any = failed
lowercase__ : str = success
lowercase__ : int = time_spent[1:-1] + """, """
lowercase__ : Tuple = extract_first_line_failure(artifact["""failures_short"""])
for line in artifact["summary_short"].split("""\n"""):
if re.search("""FAILED""", line):
lowercase__ : List[str] = line.replace("""FAILED """, """""")
lowercase__ : Union[str, Any] = line.split()[0].replace("""\n""", """""")
if "::" in line:
lowercase__ , lowercase__ : Optional[Any] = line.split("""::""")
else:
lowercase__ , lowercase__ : int = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
lowercase__ : str = docs[file_regex]
doc_test_results[category]["failed"].append(test)
lowercase__ : List[Any] = all_failures[test] if test in all_failures else """N/A"""
lowercase__ : List[Any] = failure
break
lowercase__ : Union[str, Any] = Message("""🤗 Results of the doc tests.""", doc_test_results)
message.post()
message.post_reply()
| 224 | 1 |
"""simple docstring"""
import numpy as np
def SCREAMING_SNAKE_CASE__ ( snake_case : np.ndarray , snake_case : np.ndarray , snake_case : float = 1E-1_2 , snake_case : int = 100 , )-> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(snake_case )[0] == np.shape(snake_case )[1]
# Ensure proper dimensionality.
assert np.shape(snake_case )[0] == np.shape(snake_case )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(snake_case ) == np.iscomplexobj(snake_case )
UpperCAmelCase__ : Dict = np.iscomplexobj(snake_case )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(snake_case , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
UpperCAmelCase__ : Any = False
UpperCAmelCase__ : List[str] = 0
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : int = 1E1_2
while not convergence:
# Multiple matrix by the vector.
UpperCAmelCase__ : Union[str, Any] = np.dot(snake_case , snake_case )
# Normalize the resulting output vector.
UpperCAmelCase__ : Optional[Any] = w / np.linalg.norm(snake_case )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
UpperCAmelCase__ : Union[str, Any] = vector.conj().T if is_complex else vector.T
UpperCAmelCase__ : List[Any] = np.dot(snake_case , np.dot(snake_case , snake_case ) )
# Check convergence.
UpperCAmelCase__ : Tuple = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
UpperCAmelCase__ : Union[str, Any] = True
UpperCAmelCase__ : int = lambda_
if is_complex:
UpperCAmelCase__ : List[str] = np.real(lambda_ )
return lambda_, vector
def SCREAMING_SNAKE_CASE__ ( )-> None:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
UpperCAmelCase__ : Optional[Any] = np.array([41, 4, 20] )
UpperCAmelCase__ : Dict = real_input_matrix.astype(np.complexaaa )
UpperCAmelCase__ : Union[str, Any] = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
UpperCAmelCase__ : Optional[int] = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
UpperCAmelCase__ : int = real_input_matrix
UpperCAmelCase__ : Dict = real_vector
elif problem_type == "complex":
UpperCAmelCase__ : Optional[Any] = complex_input_matrix
UpperCAmelCase__ : Dict = complex_vector
# Our implementation.
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = power_iteration(snake_case , snake_case )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
UpperCAmelCase__ , UpperCAmelCase__ : str = np.linalg.eigh(snake_case )
# Last eigenvalue is the maximum one.
UpperCAmelCase__ : Union[str, Any] = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
UpperCAmelCase__ : List[Any] = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(snake_case ) - np.abs(snake_case ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 298 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
_lowerCAmelCase : Optional[int] = get_logger(__name__)
_lowerCAmelCase : Any = Path(__file__).parent / """model_card_template.md"""
_lowerCAmelCase : Dict = uuida().hex
_lowerCAmelCase : Optional[int] = os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
_lowerCAmelCase : Optional[int] = os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
_lowerCAmelCase : int = HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""
def SCREAMING_SNAKE_CASE__ ( snake_case : Union[Dict, str, None] = None )-> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = f'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f'; torch/{_torch_version}'
if is_flax_available():
ua += f'; jax/{_jax_version}'
ua += f'; flax/{_flax_version}'
if is_onnx_available():
ua += f'; onnxruntime/{_onnxruntime_version}'
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI" , "" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(snake_case , snake_case ):
ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items() )
elif isinstance(snake_case , snake_case ):
ua += "; " + user_agent
return ua
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : Optional[str] = None , snake_case : Optional[str] = None )-> List[str]:
'''simple docstring'''
if token is None:
UpperCAmelCase__ : Optional[Any] = HfFolder.get_token()
if organization is None:
UpperCAmelCase__ : Tuple = whoami(snake_case )["name"]
return f'{username}/{model_id}'
else:
return f'{organization}/{model_id}'
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : List[Any] )-> List[Any]:
'''simple docstring'''
if not is_jinja_available():
raise ValueError(
"Modelcard rendering is based on Jinja templates."
" Please make sure to have `jinja` installed before using `create_model_card`."
" To install it, please run `pip install Jinja2`." )
if hasattr(snake_case , "local_rank" ) and args.local_rank not in [-1, 0]:
return
UpperCAmelCase__ : int = args.hub_token if hasattr(snake_case , "hub_token" ) else None
UpperCAmelCase__ : Optional[Any] = get_full_repo_name(snake_case , token=snake_case )
UpperCAmelCase__ : Tuple = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="en" , license="apache-2.0" , library_name="diffusers" , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=snake_case , model_name=snake_case , repo_name=snake_case , dataset_name=args.dataset_name if hasattr(snake_case , "dataset_name" ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(snake_case , "gradient_accumulation_steps" ) else None
) , adam_betaa=args.adam_betaa if hasattr(snake_case , "adam_beta1" ) else None , adam_betaa=args.adam_betaa if hasattr(snake_case , "adam_beta2" ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(snake_case , "adam_weight_decay" ) else None , adam_epsilon=args.adam_epsilon if hasattr(snake_case , "adam_epsilon" ) else None , lr_scheduler=args.lr_scheduler if hasattr(snake_case , "lr_scheduler" ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(snake_case , "lr_warmup_steps" ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(snake_case , "ema_inv_gamma" ) else None , ema_power=args.ema_power if hasattr(snake_case , "ema_power" ) else None , ema_max_decay=args.ema_max_decay if hasattr(snake_case , "ema_max_decay" ) else None , mixed_precision=args.mixed_precision , )
UpperCAmelCase__ : List[str] = os.path.join(args.output_dir , "README.md" )
model_card.save(snake_case )
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[str] , snake_case : Optional[str] = None )-> Tuple:
'''simple docstring'''
if resolved_file is None or commit_hash is not None:
return commit_hash
UpperCAmelCase__ : Dict = str(Path(snake_case ).as_posix() )
UpperCAmelCase__ : Optional[int] = re.search(r"snapshots/([^/]+)/" , snake_case )
if search is None:
return None
UpperCAmelCase__ : Dict = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(snake_case ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_lowerCAmelCase : Dict = os.path.expanduser(
os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
_lowerCAmelCase : List[Any] = os.path.join(hf_cache_home, """diffusers""")
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[str] = None , snake_case : Optional[str] = None )-> None:
'''simple docstring'''
if new_cache_dir is None:
UpperCAmelCase__ : Union[str, Any] = DIFFUSERS_CACHE
if old_cache_dir is None:
UpperCAmelCase__ : str = old_diffusers_cache
UpperCAmelCase__ : List[str] = Path(snake_case ).expanduser()
UpperCAmelCase__ : Any = Path(snake_case ).expanduser()
for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
UpperCAmelCase__ : Dict = new_cache_dir / old_blob_path.relative_to(snake_case )
new_blob_path.parent.mkdir(parents=snake_case , exist_ok=snake_case )
os.replace(snake_case , snake_case )
try:
os.symlink(snake_case , snake_case )
except OSError:
logger.warning(
"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
_lowerCAmelCase : Tuple = os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
_lowerCAmelCase : Any = 0
else:
with open(cache_version_file) as f:
try:
_lowerCAmelCase : List[str] = int(f.read())
except ValueError:
_lowerCAmelCase : Optional[int] = 0
if cache_version < 1:
_lowerCAmelCase : List[str] = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"""The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
"""existing cached models. This is a one-time operation, you can interrupt it or run it """
"""later by calling `diffusers.utils.hub_utils.move_cache()`."""
)
try:
move_cache()
except Exception as e:
_lowerCAmelCase : Dict = """\n""".join(traceback.format_tb(e.__traceback__))
logger.error(
F"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
"""file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
"""message and we will do our best to help."""
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, """w""") as f:
f.write("""1""")
except Exception:
logger.warning(
F"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
"""the directory exists and can be written to."""
)
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : Optional[str] = None )-> str:
'''simple docstring'''
if variant is not None:
UpperCAmelCase__ : int = weights_name.split("." )
UpperCAmelCase__ : Optional[Any] = splits[:-1] + [variant] + splits[-1:]
UpperCAmelCase__ : Optional[int] = ".".join(snake_case )
return weights_name
def SCREAMING_SNAKE_CASE__ ( snake_case : Tuple , *,
snake_case : Union[str, Any] , snake_case : Optional[Any] , snake_case : str , snake_case : List[str] , snake_case : Dict , snake_case : Any , snake_case : Any , snake_case : Tuple , snake_case : List[str] , snake_case : Any , snake_case : Optional[int]=None , )-> Tuple:
'''simple docstring'''
UpperCAmelCase__ : List[str] = str(snake_case )
if os.path.isfile(snake_case ):
return pretrained_model_name_or_path
elif os.path.isdir(snake_case ):
if os.path.isfile(os.path.join(snake_case , snake_case ) ):
# Load from a PyTorch checkpoint
UpperCAmelCase__ : Any = os.path.join(snake_case , snake_case )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(snake_case , snake_case , snake_case ) ):
UpperCAmelCase__ : str = os.path.join(snake_case , snake_case , snake_case )
return model_file
else:
raise EnvironmentError(
f'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(snake_case ).base_version ) >= version.parse("0.20.0" )
):
try:
UpperCAmelCase__ : List[Any] = hf_hub_download(
snake_case , filename=_add_variant(snake_case , snake_case ) , cache_dir=snake_case , force_download=snake_case , proxies=snake_case , resume_download=snake_case , local_files_only=snake_case , use_auth_token=snake_case , user_agent=snake_case , subfolder=snake_case , revision=revision or commit_hash , )
warnings.warn(
f'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.' , snake_case , )
return model_file
except: # noqa: E722
warnings.warn(
f'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(snake_case , snake_case )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(snake_case , snake_case )}\' so that the correct variant file can be added.' , snake_case , )
try:
# 2. Load model file as usual
UpperCAmelCase__ : Dict = hf_hub_download(
snake_case , filename=snake_case , cache_dir=snake_case , force_download=snake_case , proxies=snake_case , resume_download=snake_case , local_files_only=snake_case , use_auth_token=snake_case , user_agent=snake_case , subfolder=snake_case , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`." )
except RevisionNotFoundError:
raise EnvironmentError(
f'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
"this model name. Check the model page at "
f'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.' )
except EntryNotFoundError:
raise EnvironmentError(
f'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.' )
except HTTPError as err:
raise EnvironmentError(
f'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}' )
except ValueError:
raise EnvironmentError(
f'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
f' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
f' directory containing a file named {weights_name} or'
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
except EnvironmentError:
raise EnvironmentError(
f'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
f'containing a file named {weights_name}' )
| 298 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Any = ['''pixel_values''']
def __init__( self : str , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : Dict[str, int] = None , lowerCAmelCase__ : PILImageResampling = PIL.Image.BICUBIC , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : Dict[str, int] = None , lowerCAmelCase__ : Union[int, float] = 1 / 2_5_5 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : Optional[Union[float, List[float]]] = None , lowerCAmelCase__ : Optional[Union[float, List[float]]] = None , **lowerCAmelCase__ : int , ) -> None:
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
_UpperCAmelCase : List[str] = size if size is not None else {"height": 2_5_6, "width": 2_5_6}
_UpperCAmelCase : Dict = get_size_dict(lowerCAmelCase__ )
_UpperCAmelCase : Tuple = crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
_UpperCAmelCase : Any = get_size_dict(lowerCAmelCase__ , param_name="crop_size" )
_UpperCAmelCase : Optional[Any] = do_resize
_UpperCAmelCase : Optional[int] = size
_UpperCAmelCase : Optional[Any] = resample
_UpperCAmelCase : List[Any] = do_center_crop
_UpperCAmelCase : Dict = crop_size
_UpperCAmelCase : Optional[Any] = do_rescale
_UpperCAmelCase : Union[str, Any] = rescale_factor
_UpperCAmelCase : List[str] = do_normalize
_UpperCAmelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCAmelCase : Optional[int] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowerCAmelCase ( self : int , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : Dict[str, int] , lowerCAmelCase__ : PILImageResampling = PIL.Image.BICUBIC , lowerCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ : int , ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : int = get_size_dict(lowerCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
return resize(
lowerCAmelCase__ , size=(size["height"], size["width"]) , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : Dict[str, int] , lowerCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ : int , ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = get_size_dict(lowerCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(lowerCAmelCase__ , size=(size["height"], size["width"]) , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : Union[int, float] , lowerCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ : Tuple , ) -> Any:
"""simple docstring"""
return rescale(lowerCAmelCase__ , scale=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : Any , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : Union[float, List[float]] , lowerCAmelCase__ : Union[float, List[float]] , lowerCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ : Dict , ) -> np.ndarray:
"""simple docstring"""
return normalize(lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _lowerCAmelCase ( self : Dict , lowerCAmelCase__ : ImageInput , lowerCAmelCase__ : bool = None , lowerCAmelCase__ : Dict[str, int] = None , lowerCAmelCase__ : Any=None , lowerCAmelCase__ : bool = None , lowerCAmelCase__ : Dict[str, int] = None , lowerCAmelCase__ : bool = None , lowerCAmelCase__ : float = None , lowerCAmelCase__ : bool = None , lowerCAmelCase__ : Optional[Union[float, List[float]]] = None , lowerCAmelCase__ : Optional[Union[float, List[float]]] = None , lowerCAmelCase__ : Optional[Union[str, TensorType]] = None , lowerCAmelCase__ : ChannelDimension = ChannelDimension.FIRST , **lowerCAmelCase__ : str , ) -> PIL.Image.Image:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase : List[str] = resample if resample is not None else self.resample
_UpperCAmelCase : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase : str = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase : Tuple = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase : int = image_std if image_std is not None else self.image_std
_UpperCAmelCase : Tuple = size if size is not None else self.size
_UpperCAmelCase : Optional[int] = get_size_dict(lowerCAmelCase__ )
_UpperCAmelCase : int = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase : int = get_size_dict(lowerCAmelCase__ , param_name="crop_size" )
_UpperCAmelCase : Optional[Any] = make_list_of_images(lowerCAmelCase__ )
if not valid_images(lowerCAmelCase__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
_UpperCAmelCase : Optional[int] = [to_numpy_array(lowerCAmelCase__ ) for image in images]
if do_resize:
_UpperCAmelCase : Tuple = [self.resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ ) for image in images]
if do_center_crop:
_UpperCAmelCase : Dict = [self.center_crop(image=lowerCAmelCase__ , size=lowerCAmelCase__ ) for image in images]
if do_rescale:
_UpperCAmelCase : Dict = [self.rescale(image=lowerCAmelCase__ , scale=lowerCAmelCase__ ) for image in images]
if do_normalize:
_UpperCAmelCase : int = [self.normalize(image=lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ ) for image in images]
_UpperCAmelCase : Optional[int] = [to_channel_dimension_format(lowerCAmelCase__ , lowerCAmelCase__ ) for image in images]
_UpperCAmelCase : Union[str, Any] = {"pixel_values": images}
return BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ ) | 145 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
__a = None
__a = logging.get_logger(__name__)
__a = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
__a = {
'vocab_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
),
},
}
__a = {
'moussaKam/mbarthez': 1_024,
'moussaKam/barthez': 1_024,
'moussaKam/barthez-orangesum-title': 1_024,
}
__a = '▁'
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = VOCAB_FILES_NAMES
UpperCamelCase_ : Any = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : int = ['''input_ids''', '''attention_mask''']
UpperCamelCase_ : List[str] = BarthezTokenizer
def __init__( self : Optional[Any] , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : Any=None , lowerCAmelCase__ : str="<s>" , lowerCAmelCase__ : Tuple="</s>" , lowerCAmelCase__ : Dict="</s>" , lowerCAmelCase__ : Tuple="<s>" , lowerCAmelCase__ : Any="<unk>" , lowerCAmelCase__ : Any="<pad>" , lowerCAmelCase__ : List[str]="<mask>" , **lowerCAmelCase__ : Dict , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
super().__init__(
lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , **lowerCAmelCase__ , )
_UpperCAmelCase : Any = vocab_file
_UpperCAmelCase : Optional[Any] = False if not self.vocab_file else True
def _lowerCAmelCase ( self : Optional[int] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase : Optional[Any] = [self.cls_token_id]
_UpperCAmelCase : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowerCAmelCase ( self : str , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowerCAmelCase ( self : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[str] = os.path.join(
lowerCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,) | 145 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _lowerCAmelCase ( lowercase ,unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Dict = KandinskyVaaImgaImgPipeline
__UpperCAmelCase : Tuple = ["image_embeds", "negative_image_embeds", "image"]
__UpperCAmelCase : Tuple = [
"image_embeds",
"negative_image_embeds",
"image",
]
__UpperCAmelCase : Any = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
__UpperCAmelCase : Tuple = False
@property
def _lowercase ( self : Tuple ):
return 3_2
@property
def _lowercase ( self : Tuple ):
return 3_2
@property
def _lowercase ( self : Optional[Any] ):
return self.time_input_dim
@property
def _lowercase ( self : List[Any] ):
return self.time_input_dim * 4
@property
def _lowercase ( self : str ):
return 1_0_0
@property
def _lowercase ( self : Dict ):
torch.manual_seed(0 )
__lowercase = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
__lowercase = UNetaDConditionModel(**UpperCAmelCase__ )
return model
@property
def _lowercase ( self : Dict ):
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _lowercase ( self : int ):
torch.manual_seed(0 )
__lowercase = VQModel(**self.dummy_movq_kwargs )
return model
def _lowercase ( self : int ):
__lowercase = self.dummy_unet
__lowercase = self.dummy_movq
__lowercase = {
"num_train_timesteps": 1_0_0_0,
"beta_schedule": "linear",
"beta_start": 0.00_085,
"beta_end": 0.012,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
__lowercase = DDIMScheduler(**UpperCAmelCase__ )
__lowercase = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def _lowercase ( self : Any, UpperCAmelCase__ : Any, UpperCAmelCase__ : Any=0 ):
__lowercase = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
__lowercase = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1 ) ).to(
UpperCAmelCase__ )
# create init_image
__lowercase = floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
__lowercase = image.cpu().permute(0, 2, 3, 1 )[0]
__lowercase = Image.fromarray(np.uinta(UpperCAmelCase__ ) ).convert("RGB" ).resize((2_5_6, 2_5_6) )
if str(UpperCAmelCase__ ).startswith("mps" ):
__lowercase = torch.manual_seed(UpperCAmelCase__ )
else:
__lowercase = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
__lowercase = {
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 6_4,
"width": 6_4,
"num_inference_steps": 1_0,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def _lowercase ( self : List[Any] ):
__lowercase = "cpu"
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**UpperCAmelCase__ )
__lowercase = pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
__lowercase = pipe(**self.get_dummy_inputs(UpperCAmelCase__ ) )
__lowercase = output.images
__lowercase = pipe(
**self.get_dummy_inputs(UpperCAmelCase__ ), return_dict=UpperCAmelCase__, )[0]
__lowercase = image[0, -3:, -3:, -1]
__lowercase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__lowercase = np.array(
[0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Tuple ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : str ):
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_img2img_frog.npy" )
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
__lowercase = "A red cartoon frog, 4k"
__lowercase = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.floataa )
pipe_prior.to(UpperCAmelCase__ )
__lowercase = KandinskyVaaImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.floataa )
__lowercase = pipeline.to(UpperCAmelCase__ )
pipeline.set_progress_bar_config(disable=UpperCAmelCase__ )
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase ,__lowercase = pipe_prior(
UpperCAmelCase__, generator=UpperCAmelCase__, num_inference_steps=5, negative_prompt="", ).to_tuple()
__lowercase = pipeline(
image=UpperCAmelCase__, image_embeds=UpperCAmelCase__, negative_image_embeds=UpperCAmelCase__, generator=UpperCAmelCase__, num_inference_steps=1_0_0, height=7_6_8, width=7_6_8, strength=0.2, output_type="np", )
__lowercase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(UpperCAmelCase__, UpperCAmelCase__ )
| 144 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_a = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : str = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__UpperCAmelCase : str = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__UpperCAmelCase : Optional[Any] = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__UpperCAmelCase : Tuple = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def _lowercase ( self : Optional[int] ):
__lowercase = pipeline(
task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt" )
__lowercase = text_classifier("This is great !" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "LABEL_0", "score": 0.504}] )
__lowercase = text_classifier("This is great !", top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}] )
__lowercase = text_classifier(["This is great !", "This is bad"], top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), [
[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
], )
__lowercase = text_classifier("This is great !", top_k=1 )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "LABEL_0", "score": 0.504}] )
# Legacy behavior
__lowercase = text_classifier("This is great !", return_all_scores=UpperCAmelCase__ )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "LABEL_0", "score": 0.504}] )
__lowercase = text_classifier("This is great !", return_all_scores=UpperCAmelCase__ )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]] )
__lowercase = text_classifier(["This is great !", "Something else"], return_all_scores=UpperCAmelCase__ )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), [
[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
], )
__lowercase = text_classifier(["This is great !", "Something else"], return_all_scores=UpperCAmelCase__ )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), [
{"label": "LABEL_0", "score": 0.504},
{"label": "LABEL_0", "score": 0.504},
], )
@require_torch
def _lowercase ( self : Dict ):
import torch
__lowercase = pipeline(
task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt", device=torch.device("cpu" ), )
__lowercase = text_classifier("This is great !" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "LABEL_0", "score": 0.504}] )
@require_tf
def _lowercase ( self : Union[str, Any] ):
__lowercase = pipeline(
task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf" )
__lowercase = text_classifier("This is great !" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "LABEL_0", "score": 0.504}] )
@slow
@require_torch
def _lowercase ( self : Dict ):
__lowercase = pipeline("text-classification" )
__lowercase = text_classifier("This is great !" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "POSITIVE", "score": 1.0}] )
__lowercase = text_classifier("This is bad !" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "NEGATIVE", "score": 1.0}] )
__lowercase = text_classifier("Birds are a type of animal" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "POSITIVE", "score": 0.988}] )
@slow
@require_tf
def _lowercase ( self : Tuple ):
__lowercase = pipeline("text-classification", framework="tf" )
__lowercase = text_classifier("This is great !" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "POSITIVE", "score": 1.0}] )
__lowercase = text_classifier("This is bad !" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "NEGATIVE", "score": 1.0}] )
__lowercase = text_classifier("Birds are a type of animal" )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": "POSITIVE", "score": 0.988}] )
def _lowercase ( self : Union[str, Any], UpperCAmelCase__ : str, UpperCAmelCase__ : int, UpperCAmelCase__ : Tuple ):
__lowercase = TextClassificationPipeline(model=UpperCAmelCase__, tokenizer=UpperCAmelCase__ )
return text_classifier, ["HuggingFace is in", "This is another test"]
def _lowercase ( self : Any, UpperCAmelCase__ : List[Any], UpperCAmelCase__ : str ):
__lowercase = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
__lowercase = "HuggingFace is in"
__lowercase = text_classifier(UpperCAmelCase__ )
self.assertEqual(nested_simplify(UpperCAmelCase__ ), [{"label": ANY(UpperCAmelCase__ ), "score": ANY(UpperCAmelCase__ )}] )
self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
__lowercase = ["HuggingFace is in ", "Paris is in France"]
__lowercase = text_classifier(UpperCAmelCase__ )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), [{"label": ANY(UpperCAmelCase__ ), "score": ANY(UpperCAmelCase__ )}, {"label": ANY(UpperCAmelCase__ ), "score": ANY(UpperCAmelCase__ )}], )
self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
self.assertTrue(outputs[1]["label"] in model.config.idalabel.values() )
# Forcing to get all results with `top_k=None`
# This is NOT the legacy format
__lowercase = text_classifier(UpperCAmelCase__, top_k=UpperCAmelCase__ )
__lowercase = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), [[{"label": ANY(UpperCAmelCase__ ), "score": ANY(UpperCAmelCase__ )}] * N, [{"label": ANY(UpperCAmelCase__ ), "score": ANY(UpperCAmelCase__ )}] * N], )
__lowercase = {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
__lowercase = text_classifier(UpperCAmelCase__ )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), {"label": ANY(UpperCAmelCase__ ), "score": ANY(UpperCAmelCase__ )}, )
self.assertTrue(outputs["label"] in model.config.idalabel.values() )
# This might be used a text pair, but tokenizer + pipe interaction
# makes it hard to understand that it's not using the pair properly
# https://github.com/huggingface/transformers/issues/17305
# We disabled this usage instead as it was outputting wrong outputs.
__lowercase = [["HuggingFace is in ", "Paris is in France"]]
with self.assertRaises(UpperCAmelCase__ ):
text_classifier(UpperCAmelCase__ )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
__lowercase = text_classifier([[["HuggingFace is in ", "Paris is in France"]]] )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ), [{"label": ANY(UpperCAmelCase__ ), "score": ANY(UpperCAmelCase__ )}], )
self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
| 144 | 1 |
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