code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : List[str] , UpperCamelCase : Optional[Any] , UpperCamelCase : Tuple , UpperCamelCase : List[Any] , UpperCamelCase : str ):
if index == r:
for j in range(_a ):
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
UpperCAmelCase : str = arr[i]
combination_util(_a , _a , _a , index + 1 , _a , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(_a , _a , _a , _a , _a , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def _snake_case ( UpperCamelCase : Optional[int] , UpperCamelCase : List[str] , UpperCamelCase : Tuple ):
# A temporary array to store all combination one by one
UpperCAmelCase : Any = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(_a , _a , _a , 0 , _a , 0 )
if __name__ == "__main__":
# Driver code to check the function above
A: Union[str, Any] = [1_0, 2_0, 3_0, 4_0, 5_0]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : List[Any] = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Any = ['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : int = [
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = [
'''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDistilBertForMaskedLM''',
'''TFDistilBertForMultipleChoice''',
'''TFDistilBertForQuestionAnswering''',
'''TFDistilBertForSequenceClassification''',
'''TFDistilBertForTokenClassification''',
'''TFDistilBertMainLayer''',
'''TFDistilBertModel''',
'''TFDistilBertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''FlaxDistilBertForMaskedLM''',
'''FlaxDistilBertForMultipleChoice''',
'''FlaxDistilBertForQuestionAnswering''',
'''FlaxDistilBertForSequenceClassification''',
'''FlaxDistilBertForTokenClassification''',
'''FlaxDistilBertModel''',
'''FlaxDistilBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
lowercase__ : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase__ : Optional[int] = abspath(join(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 a_ ( lowerCamelCase ):
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def a_ ( lowerCamelCase ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_a )
def a_ ( lowerCamelCase ):
from transformers.testing_utils import pytest_terminal_summary_main
UpperCAmelCase__ = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_a , id=_a )
def a_ ( lowerCamelCase , lowerCamelCase ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
UpperCAmelCase__ = 0
# Doctest custom flag to ignore output.
lowerCAmelCase__ : Union[str, Any] = doctest.register_optionflag('IGNORE_RESULT')
lowerCAmelCase__ : Tuple = doctest.OutputChecker
class snake_case ( lowerCAmelCase__ ):
"""simple docstring"""
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : List[Any] ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,lowercase_ ,lowercase_ ,lowercase_ )
lowerCAmelCase__ : Any = CustomOutputChecker
lowerCAmelCase__ : str = HfDoctestModule
lowerCAmelCase__ : Tuple = HfDocTestParser
| 98 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = logging.get_logger(__name__)
def __lowercase ( _a , _a=False ):
snake_case_ : List[str] = []
# fmt: off
# stem:
rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''') )
rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''') )
rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''') )
# backbone
rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case_ : Optional[int] = [(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'''),
] )
# fmt: on
return rename_keys
def __lowercase ( _a , _a , _a=False ):
for i in range(config.num_hidden_layers ):
if base_model:
snake_case_ : List[str] = ''''''
else:
snake_case_ : Dict = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ : List[str] = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
snake_case_ : Optional[int] = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Any = in_proj_weight[
: config.hidden_size, :
]
snake_case_ : Dict = in_proj_bias[: config.hidden_size]
snake_case_ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case_ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case_ : Dict = in_proj_weight[
-config.hidden_size :, :
]
snake_case_ : str = in_proj_bias[-config.hidden_size :]
def __lowercase ( _a ):
snake_case_ : Dict = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(_a , _a )
def __lowercase ( _a , _a , _a ):
snake_case_ : Union[str, Any] = dct.pop(_a )
snake_case_ : Union[str, Any] = val
def __lowercase ( ):
snake_case_ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ : Tuple = Image.open(requests.get(_a , stream=_a ).raw )
return im
@torch.no_grad()
def __lowercase ( _a , _a , _a=False ):
snake_case_ : str = BitConfig(
global_padding='''same''' , layer_type='''bottleneck''' , depths=(3, 4, 9) , out_features=['''stage3'''] , embedding_dynamic_padding=_a , )
snake_case_ : Tuple = ViTHybridConfig(backbone_config=_a , image_size=384 , num_labels=1_000 )
snake_case_ : int = False
# load original model from timm
snake_case_ : str = timm.create_model(_a , pretrained=_a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case_ : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_a )
snake_case_ : int = create_rename_keys(_a , _a )
for src, dest in rename_keys:
rename_key(_a , _a , _a )
read_in_q_k_v(_a , _a , _a )
snake_case_ : Optional[Any] = '''huggingface/label-files'''
snake_case_ : Any = '''imagenet-1k-id2label.json'''
snake_case_ : Dict = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
snake_case_ : Dict = {int(_a ): v for k, v in idalabel.items()}
snake_case_ : Optional[int] = idalabel
snake_case_ : Optional[Any] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
snake_case_ : Optional[Any] = ViTHybridModel(_a ).eval()
else:
snake_case_ : Any = ViTHybridForImageClassification(_a ).eval()
model.load_state_dict(_a )
# create image processor
snake_case_ : Optional[Any] = create_transform(**resolve_data_config({} , model=_a ) )
snake_case_ : List[Any] = transform.transforms
snake_case_ : Optional[Any] = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
snake_case_ : List[Any] = ViTHybridImageProcessor(
do_resize=_a , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_a , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=_a , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Optional[int] = transform(_a ).unsqueeze(0 )
snake_case_ : int = processor(_a , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(_a , _a )
# verify logits
with torch.no_grad():
snake_case_ : List[str] = model(_a )
snake_case_ : Any = outputs.logits
print('''Predicted class:''' , logits.argmax(-1 ).item() )
if base_model:
snake_case_ : Optional[Any] = timm_model.forward_features(_a )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_a , outputs.pooler_output , atol=1E-3 )
else:
snake_case_ : int = timm_model(_a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_a , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(f"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_a )
if push_to_hub:
print(f"Pushing model and processor to the hub {vit_name}" )
model.push_to_hub(f"ybelkada/{vit_name}" )
processor.push_to_hub(f"ybelkada/{vit_name}" )
if __name__ == "__main__":
lowercase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--vit_name''',
default='''vit_base_r50_s16_384''',
type=str,
help='''Name of the hybrid ViT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
lowercase__ : Any = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 264 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
a_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class _lowercase ( nn.Module ):
def __init__( self : Any , snake_case : List[Any] ) -> List[Any]:
"""simple docstring"""
super().__init__()
UpperCamelCase_ : str = torchvision.models.resnetaaa(pretrained=lowercase_ )
UpperCamelCase_ : Optional[Any] = list(model.children() )[:-2]
UpperCamelCase_ : str = nn.Sequential(*lowercase_ )
UpperCamelCase_ : Optional[int] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , snake_case : List[str] ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = self.pool(self.model(lowercase_ ) )
UpperCamelCase_ : List[str] = torch.flatten(lowercase_ , start_dim=2 )
UpperCamelCase_ : List[Any] = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class _lowercase ( lowerCAmelCase__ ):
def __init__( self : Dict , snake_case : List[Any] , snake_case : List[Any] , snake_case : List[str] , snake_case : Optional[int] , snake_case : int ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : Tuple = [json.loads(lowercase_ ) for l in open(lowercase_ )]
UpperCamelCase_ : List[Any] = os.path.dirname(lowercase_ )
UpperCamelCase_ : List[Any] = tokenizer
UpperCamelCase_ : str = labels
UpperCamelCase_ : Tuple = len(lowercase_ )
UpperCamelCase_ : str = max_seq_length
UpperCamelCase_ : Dict = transforms
def __len__( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
return len(self.data )
def __getitem__( self : Optional[Any] , snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : int = torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] , add_special_tokens=lowercase_ ) )
UpperCamelCase_ : List[Any] = sentence[0], sentence[1:-1], sentence[-1]
UpperCamelCase_ : Any = sentence[: self.max_seq_length]
UpperCamelCase_ : str = torch.zeros(self.n_classes )
UpperCamelCase_ : Tuple = 1
UpperCamelCase_ : Optional[int] = Image.open(os.path.join(self.data_dir , self.data[index]['img'] ) ).convert('RGB' )
UpperCamelCase_ : str = self.transforms(lowercase_ )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> str:
"""simple docstring"""
UpperCamelCase_ : List[str] = Counter()
for row in self.data:
label_freqs.update(row['label'] )
return label_freqs
def __lowercase ( lowerCamelCase : List[Any] ):
UpperCamelCase_ : Optional[Any] = [len(row['sentence'] ) for row in batch]
UpperCamelCase_ : List[Any] = len(_a ), max(_a )
UpperCamelCase_ : int = torch.zeros(_a , _a , dtype=torch.long )
UpperCamelCase_ : int = torch.zeros(_a , _a , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(_a , _a ) ):
UpperCamelCase_ : Optional[Any] = input_row['''sentence''']
UpperCamelCase_ : Optional[int] = 1
UpperCamelCase_ : Optional[int] = torch.stack([row['image'] for row in batch] )
UpperCamelCase_ : Union[str, Any] = torch.stack([row['label'] for row in batch] )
UpperCamelCase_ : Tuple = torch.stack([row['image_start_token'] for row in batch] )
UpperCamelCase_ : Union[str, Any] = torch.stack([row['image_end_token'] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __lowercase ( ):
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def __lowercase ( ):
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7] , std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9] , ),
] )
| 175 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
lowercase__ : str = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def __lowercase ( _a , _a ):
snake_case_ : Optional[int] = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
snake_case_ : List[Any] = int(re.match(r'''.*layer_(\d*).*''' , _a )[1] )
layer_number -= 3
return f"h.{layer_number}." + key
def __lowercase ( _a ):
if dtype == torch.bool:
return 1 / 8
snake_case_ : Dict = re.search(r'''[^\d](\d+)$''' , str(_a ) )
if bit_search is None:
raise ValueError(f"`dtype` is not a valid dtype: {dtype}." )
snake_case_ : Optional[int] = int(bit_search.groups()[0] )
return bit_size // 8
def __lowercase ( _a , _a , _a , _a , _a ):
# Construct model
if bloom_config_file == "":
snake_case_ : int = BloomConfig()
else:
snake_case_ : List[str] = BloomConfig.from_json_file(_a )
if shard_model:
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : int = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[str] = {'''weight_map''': {}, '''metadata''': {}}
snake_case_ : Any = 0
snake_case_ : Union[str, Any] = None
snake_case_ : List[str] = BloomConfig()
for j, file in enumerate(_a ):
print('''Processing file: {}'''.format(_a ) )
snake_case_ : Dict = None
for i in range(_a ):
# load all TP files
snake_case_ : Union[str, Any] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : List[str] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : Any = temp.pop(_a )
if tensors is None:
snake_case_ : Any = temp
else:
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : List[str] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Any = tensors[key] / pretraining_tp
torch.save(
_a , os.path.join(
_a , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
snake_case_ : List[str] = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
snake_case_ : List[str] = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) )
snake_case_ : int = BloomConfig()
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Dict = total_size
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(_a , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f:
snake_case_ : Tuple = json.dumps(_a , indent=2 , sort_keys=_a ) + '''\n'''
f.write(_a )
else:
snake_case_ : Union[str, Any] = BloomModel(_a )
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : Dict = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[Any] = None
for i, file in enumerate(_a ):
snake_case_ : Optional[Any] = None
for i in range(_a ):
# load all TP files
snake_case_ : List[str] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : Optional[Any] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : str = temp.pop(_a )
if tensors is None:
snake_case_ : int = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : Optional[Any] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Union[str, Any] = tensors[key] / pretraining_tp
snake_case_ : Any = model.load_state_dict(_a , strict=_a )
assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
if missing_keys is None:
snake_case_ : Optional[int] = set(other_keys.missing_keys )
else:
snake_case_ : Tuple = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f"The keys {missing_keys} are missing"
# Save pytorch-model
os.makedirs(_a , exist_ok=_a )
snake_case_ : List[str] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}" )
if config.torch_dtype is not None:
snake_case_ : Optional[Any] = model.to(config.torch_dtype )
torch.save(model.state_dict() , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
lowercase__ : List[Any] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 264 | 0 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
A_ : Optional[int] = get_tests_dir("""fixtures/dummy-config.json""")
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = 0
def _snake_case ( self ) -> Optional[Any]:
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def _snake_case ( self ) -> str:
_UpperCAmelCase : List[str] = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(lowercase_ ,lowercase_ )
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = AutoConfig.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ ,lowercase_ )
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Tuple = AutoConfig.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ ,lowercase_ )
def _snake_case ( self ) -> int:
_UpperCAmelCase : List[str] = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(lowercase_ ,lowercase_ )
def _snake_case ( self ) -> int:
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
_UpperCAmelCase : List[str] = os.path.join(lowercase_ ,"""fake-roberta""" )
os.makedirs(lowercase_ ,exist_ok=lowercase_ )
with open(os.path.join(lowercase_ ,"""config.json""" ) ,"""w""" ) as f:
f.write(json.dumps({} ) )
_UpperCAmelCase : int = AutoConfig.from_pretrained(lowercase_ )
self.assertEqual(type(lowercase_ ) ,lowercase_ )
def _snake_case ( self ) -> Union[str, Any]:
try:
AutoConfig.register("""custom""" ,lowercase_ )
# Wrong model type will raise an error
with self.assertRaises(lowercase_ ):
AutoConfig.register("""model""" ,lowercase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase_ ):
AutoConfig.register("""bert""" ,lowercase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
_UpperCAmelCase : int = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
_UpperCAmelCase : List[Any] = AutoConfig.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ ,lowercase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def _snake_case ( self ) -> Tuple:
with self.assertRaisesRegex(
lowercase_ ,"""bert-base is not a local folder and is not a valid model identifier""" ):
_UpperCAmelCase : Optional[int] = AutoConfig.from_pretrained("""bert-base""" )
def _snake_case ( self ) -> Dict:
with self.assertRaisesRegex(
lowercase_ ,r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
_UpperCAmelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase_ ,revision="""aaaaaa""" )
def _snake_case ( self ) -> Optional[int]:
with self.assertRaisesRegex(
lowercase_ ,"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" ,):
_UpperCAmelCase : Dict = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def _snake_case ( self ) -> Any:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowercase_ ):
_UpperCAmelCase : Tuple = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowercase_ ):
_UpperCAmelCase : Optional[Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowercase_ )
_UpperCAmelCase : Optional[Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowercase_ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ )
_UpperCAmelCase : List[str] = AutoConfig.from_pretrained(lowercase_ ,trust_remote_code=lowercase_ )
self.assertEqual(reloaded_config.__class__.__name__ ,"""NewModelConfig""" )
def _snake_case ( self ) -> Dict:
class lowercase ( lowerCAmelCase__ ):
"""simple docstring"""
UpperCAmelCase = """new-model"""
try:
AutoConfig.register("""new-model""" ,lowercase_ )
# If remote code is not set, the default is to use local
_UpperCAmelCase : Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
_UpperCAmelCase : Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowercase_ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
_UpperCAmelCase : Optional[int] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowercase_ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 215 |
"""simple docstring"""
def __lowercase ( _a , _a , _a=False ):
if isinstance(_a , _a ) and isinstance(_a , _a ):
snake_case_ : Union[str, Any] = len(set_a.intersection(_a ) )
if alternative_union:
snake_case_ : Any = len(_a ) + len(_a )
else:
snake_case_ : str = len(set_a.union(_a ) )
return intersection / union
if isinstance(_a , (list, tuple) ) and isinstance(_a , (list, tuple) ):
snake_case_ : str = [element for element in set_a if element in set_b]
if alternative_union:
snake_case_ : Tuple = len(_a ) + len(_a )
return len(_a ) / union
else:
snake_case_ : List[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_a ) / len(_a )
return len(_a ) / len(_a )
return None
if __name__ == "__main__":
lowercase__ : Any = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowercase__ : Optional[Any] = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 264 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ : Tuple =logging.get_logger(__name__)
A__ : List[str] ={'''tokenizer_file''': '''tokenizer.json'''}
A__ : Dict ={
'''tokenizer_file''': {
'''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
'''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
'''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
'''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
'''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
'''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
'''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
},
}
class UpperCAmelCase ( lowerCAmelCase__ ):
_lowercase: Tuple = VOCAB_FILES_NAMES
_lowercase: Tuple = PRETRAINED_VOCAB_FILES_MAP
_lowercase: Union[str, Any] = ["""input_ids""", """attention_mask"""]
_lowercase: Any = None
def __init__( self : Union[str, Any] , __snake_case : Union[str, Any]=None , __snake_case : Optional[Any]=None , __snake_case : Optional[Any]=None , __snake_case : Optional[Any]="<unk>" , __snake_case : Optional[int]="<s>" , __snake_case : Tuple="</s>" , __snake_case : Optional[Any]="<pad>" , __snake_case : Union[str, Any]=False , __snake_case : Union[str, Any]=False , **__snake_case : List[str] , ) -> Any:
super().__init__(
lowercase_ , lowercase_ , tokenizer_file=lowercase_ , unk_token=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , pad_token=lowercase_ , add_prefix_space=lowercase_ , clean_up_tokenization_spaces=lowercase_ , **lowercase_ , )
_lowerCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , lowercase_ ) != add_prefix_space:
_lowerCAmelCase = getattr(lowercase_ , pre_tok_state.pop("""type""" ) )
_lowerCAmelCase = add_prefix_space
_lowerCAmelCase = pre_tok_class(**lowercase_ )
_lowerCAmelCase = add_prefix_space
def lowercase__ ( self : int , *__snake_case : List[str] , **__snake_case : str ) -> int:
_lowerCAmelCase = kwargs.get("""is_split_into_words""" , lowercase_ )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
""" pretokenized inputs.""" )
return super()._batch_encode_plus(*lowercase_ , **lowercase_ )
def lowercase__ ( self : List[Any] , *__snake_case : List[str] , **__snake_case : List[str] ) -> Any:
_lowerCAmelCase = kwargs.get("""is_split_into_words""" , lowercase_ )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
""" pretokenized inputs.""" )
return super()._encode_plus(*lowercase_ , **lowercase_ )
def lowercase__ ( self : str , __snake_case : str , __snake_case : Optional[str] = None ) -> Optional[int]:
_lowerCAmelCase = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
def lowercase__ ( self : Optional[Any] , __snake_case : "Conversation" ) -> Tuple:
_lowerCAmelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowercase_ , add_special_tokens=lowercase_ ) + [self.eos_token_id] )
if len(lowercase_ ) > self.model_max_length:
_lowerCAmelCase = input_ids[-self.model_max_length :]
return input_ids
| 70 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowercase__ : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __lowercase ( ):
snake_case_ : Optional[Any] = Github(os.environ['''GITHUB_TOKEN'''] )
snake_case_ : Any = g.get_repo('''huggingface/diffusers''' )
snake_case_ : Any = repo.get_issues(state='''open''' )
for issue in open_issues:
snake_case_ : str = sorted(issue.get_comments() , key=lambda _a : i.created_at , reverse=_a )
snake_case_ : Dict = comments[0] if len(_a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 264 | 0 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
_a = logging.get_logger(__name__)
_a = '''T5Config'''
class A_ (lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = """mt5"""
SCREAMING_SNAKE_CASE__ : Tuple = MTaConfig
class A_ (lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = """mt5"""
SCREAMING_SNAKE_CASE__ : Any = MTaConfig
class A_ (lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """mt5"""
SCREAMING_SNAKE_CASE__ : List[str] = MTaConfig
| 61 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowercase ( _a , _a ):
# Load checkpoint
snake_case_ : Optional[Any] = torch.load(_a , map_location='''cpu''' )
snake_case_ : Union[str, Any] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
snake_case_ : Dict = {}
for k, v in state_dict.items():
if "pred_layer" in k:
snake_case_ : Union[str, Any] = v
else:
snake_case_ : Dict = v
snake_case_ : Union[str, Any] = chkpt['''params''']
snake_case_ : int = {n: v for n, v in config.items() if not isinstance(_a , (torch.FloatTensor, numpy.ndarray) )}
snake_case_ : int = chkpt['''dico_word2id''']
snake_case_ : str = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(f"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(_a , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
print(f"Save vocab file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
if __name__ == "__main__":
lowercase__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowercase__ : List[str] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 264 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__snake_case : int = logging.get_logger(__name__)
class A__(lowerCAmelCase__ ):
"""simple docstring"""
_A : str = ["""pixel_values"""]
def __init__( self , _lowercase = True , _lowercase = None , _lowercase = PILImageResampling.BILINEAR , _lowercase = True , _lowercase = None , _lowercase = True , _lowercase = 1 / 255 , _lowercase = True , _lowercase = None , _lowercase = None , **_lowercase , ) -> int:
super().__init__(**lowercase_ )
a_ : List[str] = size if size is not None else {'''shortest_edge''': 256}
a_ : Optional[int] = get_size_dict(lowercase_ , default_to_square=lowercase_ )
a_ : Dict = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
a_ : Dict = get_size_dict(lowercase_ )
a_ : List[str] = do_resize
a_ : Optional[int] = size
a_ : Union[str, Any] = resample
a_ : Dict = do_center_crop
a_ : List[str] = crop_size
a_ : int = do_rescale
a_ : List[str] = rescale_factor
a_ : int = do_normalize
a_ : int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
a_ : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase = PILImageResampling.BICUBIC , _lowercase = None , **_lowercase , ) -> Tuple:
a_ : str = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
a_ : Dict = get_resize_output_image_size(lowercase_ , size=size["""shortest_edge"""] , default_to_square=lowercase_ )
return resize(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase , ) -> Optional[Any]:
a_ : Union[str, Any] = get_size_dict(lowercase_ )
return center_crop(lowercase_ , size=(size["""height"""], size["""width"""]) , data_format=lowercase_ , **lowercase_ )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase ) -> int:
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase = None , **_lowercase , ) -> List[str]:
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCamelCase__ ( self , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = ChannelDimension.FIRST , **_lowercase , ) -> Any:
a_ : Optional[int] = do_resize if do_resize is not None else self.do_resize
a_ : int = size if size is not None else self.size
a_ : Dict = get_size_dict(lowercase_ , default_to_square=lowercase_ )
a_ : str = resample if resample is not None else self.resample
a_ : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
a_ : Union[str, Any] = crop_size if crop_size is not None else self.crop_size
a_ : Union[str, Any] = get_size_dict(lowercase_ )
a_ : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
a_ : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
a_ : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
a_ : Any = image_mean if image_mean is not None else self.image_mean
a_ : List[Any] = image_std if image_std is not None else self.image_std
a_ : str = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
a_ : List[Any] = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
a_ : Optional[Any] = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_center_crop:
a_ : List[str] = [self.center_crop(image=lowercase_ , size=lowercase_ ) for image in images]
if do_rescale:
a_ : int = [self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
if do_normalize:
a_ : Dict = [self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ ) for image in images]
a_ : Tuple = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
a_ : int = {'''pixel_values''': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 248 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 264 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
lowercase__ : List[Any] = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE ) ->Any:
lowerCAmelCase = data
lowerCAmelCase = self
lowerCAmelCase = 0
class lowercase_ ( Generic[T] ):
"""simple docstring"""
def __init__( self ) ->Any:
# map from node name to the node object
lowerCAmelCase = {}
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->int:
# create a new set with x as its member
lowerCAmelCase = DisjointSetTreeNode(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->int:
# find the set x belongs to (with path-compression)
lowerCAmelCase = self.map[data]
if elem_ref != elem_ref.parent:
lowerCAmelCase = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ->int:
# helper function for union operation
if nodea.rank > nodea.rank:
lowerCAmelCase = nodea
else:
lowerCAmelCase = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ->Dict:
# merge 2 disjoint sets
self.link(self.find_set(lowercase_ ) , self.find_set(lowercase_ ) )
class lowercase_ ( Generic[T] ):
"""simple docstring"""
def __init__( self ) ->List[Any]:
# connections: map from the node to the neighbouring nodes (with weights)
lowerCAmelCase = {}
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->Optional[Any]:
# add a node ONLY if its not present in the graph
if node not in self.connections:
lowerCAmelCase = {}
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ->List[str]:
# add an edge with the given weight
self.add_node(lowercase_ )
self.add_node(lowercase_ )
lowerCAmelCase = weight
lowerCAmelCase = weight
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
lowerCAmelCase = []
lowerCAmelCase = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __SCREAMING_SNAKE_CASE : x[2] )
# creating the disjoint set
lowerCAmelCase = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(lowercase_ )
# MST generation
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
lowerCAmelCase = edges[index]
index += 1
lowerCAmelCase = disjoint_set.find_set(lowercase_ )
lowerCAmelCase = disjoint_set.find_set(lowercase_ )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(lowercase_ , lowercase_ , lowercase_ )
disjoint_set.union(lowercase_ , lowercase_ )
return graph
| 338 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __lowercase ( _a="" ):
snake_case_ : List[str] = tempfile.mkdtemp()
return os.path.join(_a , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : str ):
snake_case_ : int = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : Optional[int] = AgentAudio(lowercase_ )
snake_case_ : List[str] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowercase_ ) )
# Ensure that the file contains the same value as the original tensor
snake_case_, snake_case_ : int = sf.read(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , torch.tensor(lowercase_ ) , atol=1E-4 ) )
def _snake_case ( self : Optional[int] ):
snake_case_ : Any = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : List[str] = get_new_path(suffix='''.wav''' )
sf.write(lowercase_ , lowercase_ , 16000 )
snake_case_ : Tuple = AgentAudio(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
self.assertEqual(agent_type.to_string() , lowercase_ )
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Tuple ):
snake_case_ : List[Any] = torch.randint(0 , 256 , (64, 64, 3) )
snake_case_ : str = AgentImage(lowercase_ )
snake_case_ : Union[str, Any] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type._tensor , atol=1E-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : Any = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Optional[int] = Image.open(lowercase_ )
snake_case_ : Tuple = AgentImage(lowercase_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : int = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Dict = Image.open(lowercase_ )
snake_case_ : List[str] = AgentImage(lowercase_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Any ):
snake_case_ : Tuple = '''Hey!'''
snake_case_ : Optional[Any] = AgentText(lowercase_ )
self.assertEqual(lowercase_ , agent_type.to_string() )
self.assertEqual(lowercase_ , agent_type.to_raw() )
self.assertEqual(lowercase_ , lowercase_ )
| 264 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : str ) -> str:
__magic_name__ : Tuple = '''ylacombe/bark-small'''
__magic_name__ : Dict = tempfile.mkdtemp()
__magic_name__ : Union[str, Any] = '''en_speaker_1'''
__magic_name__ : Optional[Any] = '''This is a test string'''
__magic_name__ : Dict = '''speaker_embeddings_path.json'''
__magic_name__ : Union[str, Any] = '''speaker_embeddings'''
def __lowerCAmelCase ( self : Union[str, Any] , **_A : str ) -> Any:
return AutoTokenizer.from_pretrained(self.checkpoint , **lowercase_ )
def __lowerCAmelCase ( self : Any ) -> Tuple:
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Any ) -> str:
__magic_name__ : Dict = self.get_tokenizer()
__magic_name__ : Optional[Any] = BarkProcessor(tokenizer=lowercase_ )
processor.save_pretrained(self.tmpdirname )
__magic_name__ : Any = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def __lowerCAmelCase ( self : List[Any] ) -> List[Any]:
__magic_name__ : List[Any] = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
__magic_name__ : List[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__magic_name__ : Optional[Any] = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='(BOS)' , eos_token='(EOS)' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def __lowerCAmelCase ( self : int ) -> List[str]:
__magic_name__ : int = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
__magic_name__ : str = 35
__magic_name__ : int = 2
__magic_name__ : Optional[int] = 8
__magic_name__ : int = {
'''semantic_prompt''': np.ones(lowercase_ ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
__magic_name__ : str = processor(text=self.input_string , voice_preset=lowercase_ )
__magic_name__ : Optional[Any] = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(lowercase_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
__magic_name__ : List[Any] = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(lowercase_ , **lowercase_ )
__magic_name__ : Optional[Any] = processor(text=self.input_string , voice_preset=lowercase_ )
__magic_name__ : List[Any] = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(lowercase_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
__magic_name__ : Union[str, Any] = processor(text=self.input_string , voice_preset=self.voice_preset )
def __lowerCAmelCase ( self : Tuple ) -> List[Any]:
__magic_name__ : Optional[int] = self.get_tokenizer()
__magic_name__ : Any = BarkProcessor(tokenizer=lowercase_ )
__magic_name__ : Optional[int] = processor(text=self.input_string )
__magic_name__ : List[str] = tokenizer(
self.input_string , padding='max_length' , max_length=256 , add_special_tokens=lowercase_ , return_attention_mask=lowercase_ , return_token_type_ids=lowercase_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() ) | 331 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : str = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
lowercase__ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCamelCase : Dict = (7_2_0, 1_2_8_0) # Height, Width
_UpperCamelCase : Optional[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
_UpperCamelCase : Dict = 1 / 1_0_0
_UpperCamelCase : List[str] = ''''''
_UpperCamelCase : str = ''''''
_UpperCamelCase : Optional[int] = ''''''
_UpperCamelCase : Tuple = 2_5_0
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = get_dataset(_a , _a )
for index in range(_a ):
lowercase = random.sample(range(len(_a ) ) , 4 )
lowercase = update_image_and_anno(
_a , _a , _a , _a , _a , filter_scale=_a , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
lowercase = random_chars(32 )
lowercase = path.split(os.sep )[-1].rsplit('.' , 1 )[0]
lowercase = f'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
cva.imwrite(f'{file_root}.jpg' , _a , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}' )
lowercase = []
for anno in new_annos:
lowercase = anno[3] - anno[1]
lowercase = anno[4] - anno[2]
lowercase = anno[1] + width / 2
lowercase = anno[2] + height / 2
lowercase = f'{anno[0]} {x_center} {y_center} {width} {height}'
annos_list.append(_a )
with open(f'{file_root}.txt' , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[int] , __snake_case : Tuple ):
'''simple docstring'''
lowercase = []
lowercase = []
for label_file in glob.glob(os.path.join(_a , '*.txt' ) ):
lowercase = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(_a ) as in_file:
lowercase = in_file.readlines()
lowercase = os.path.join(_a , f'{label_name}.jpg' )
lowercase = []
for obj_list in obj_lists:
lowercase = obj_list.rstrip('\n' ).split(' ' )
lowercase = float(obj[1] ) - float(obj[3] ) / 2
lowercase = float(obj[2] ) - float(obj[4] ) / 2
lowercase = float(obj[1] ) + float(obj[3] ) / 2
lowercase = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(_a )
labels.append(_a )
return img_paths, labels
def _SCREAMING_SNAKE_CASE ( __snake_case : Any , __snake_case : List[Any] , __snake_case : List[Any] , __snake_case : str , __snake_case : str , __snake_case : Optional[Any] = 0.0 , ):
'''simple docstring'''
lowercase = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
lowercase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
lowercase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
lowercase = int(scale_x * output_size[1] )
lowercase = int(scale_y * output_size[0] )
lowercase = []
lowercase = []
for i, index in enumerate(_a ):
lowercase = all_img_list[index]
path_list.append(_a )
lowercase = all_annos[index]
lowercase = cva.imread(_a )
if i == 0: # top-left
lowercase = cva.resize(_a , (divid_point_x, divid_point_y) )
lowercase = img
for bbox in img_annos:
lowercase = bbox[1] * scale_x
lowercase = bbox[2] * scale_y
lowercase = bbox[3] * scale_x
lowercase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
lowercase = cva.resize(_a , (output_size[1] - divid_point_x, divid_point_y) )
lowercase = img
for bbox in img_annos:
lowercase = scale_x + bbox[1] * (1 - scale_x)
lowercase = bbox[2] * scale_y
lowercase = scale_x + bbox[3] * (1 - scale_x)
lowercase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
lowercase = cva.resize(_a , (divid_point_x, output_size[0] - divid_point_y) )
lowercase = img
for bbox in img_annos:
lowercase = bbox[1] * scale_x
lowercase = scale_y + bbox[2] * (1 - scale_y)
lowercase = bbox[3] * scale_x
lowercase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
lowercase = cva.resize(
_a , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
lowercase = img
for bbox in img_annos:
lowercase = scale_x + bbox[1] * (1 - scale_x)
lowercase = scale_y + bbox[2] * (1 - scale_y)
lowercase = scale_x + bbox[3] * (1 - scale_x)
lowercase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
lowercase = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def _SCREAMING_SNAKE_CASE ( __snake_case : List[str] ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
lowercase = ascii_lowercase + digits
return "".join(random.choice(_a ) for _ in range(_a ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 220 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : str = XLMRobertaTokenizer
_lowerCAmelCase : int = XLMRobertaTokenizerFast
_lowerCAmelCase : str = True
_lowerCAmelCase : Dict = True
def _snake_case ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ : List[str] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def _snake_case ( self : str ):
snake_case_ : List[Any] = '''<pad>'''
snake_case_ : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def _snake_case ( self : Union[str, Any] ):
snake_case_ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowercase_ ) , 1002 )
def _snake_case ( self : Union[str, Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def _snake_case ( self : Dict ):
snake_case_ : Optional[Any] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
snake_case_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowercase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
snake_case_ : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
snake_case_ : List[Any] = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _snake_case ( self : List[str] ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
snake_case_ : int = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : int = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : Tuple = tokenizer_r.save_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
snake_case_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : Union[str, Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[str] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : List[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[Any] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : Tuple = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
snake_case_ : Optional[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : Dict = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@cached_property
def _snake_case ( self : List[str] ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _snake_case ( self : Optional[Any] ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowercase_ , f.name )
snake_case_ : Any = XLMRobertaTokenizer(f.name , keep_accents=lowercase_ )
snake_case_ : List[Any] = pickle.dumps(lowercase_ )
pickle.loads(lowercase_ )
def _snake_case ( self : Tuple ):
if not self.test_rust_tokenizer:
return
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ : Optional[int] = self.get_rust_tokenizer()
snake_case_ : Dict = '''I was born in 92000, and this is falsé.'''
snake_case_ : Optional[int] = tokenizer.tokenize(lowercase_ )
snake_case_ : Tuple = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : List[str] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
snake_case_ : str = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ : Any = tokenizer.encode(lowercase_ )
snake_case_ : int = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
@slow
def _snake_case ( self : Tuple ):
snake_case_ : int = '''Hello World!'''
snake_case_ : int = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : List[Any] ):
snake_case_ : Any = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
snake_case_ : Optional[int] = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : Dict ):
# fmt: off
snake_case_ : int = {'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 264 | 0 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class snake_case__( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = False , __lowercase = False , __lowercase = None , **__lowercase , ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = path_or_paths
lowerCAmelCase_ : int = split if split or isinstance(lowercase_ , lowercase_ ) else '''train'''
lowerCAmelCase_ : Optional[int] = features
lowerCAmelCase_ : List[Any] = cache_dir
lowerCAmelCase_ : Any = keep_in_memory
lowerCAmelCase_ : Tuple = streaming
lowerCAmelCase_ : Optional[int] = num_proc
lowerCAmelCase_ : Any = kwargs
@abstractmethod
def lowercase_ ( self ) -> List[Any]:
pass
class snake_case__( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __lowercase = None , __lowercase = None , __lowercase = False , __lowercase = False , __lowercase = None , **__lowercase , ) -> Tuple:
lowerCAmelCase_ : str = features
lowerCAmelCase_ : Dict = cache_dir
lowerCAmelCase_ : Optional[int] = keep_in_memory
lowerCAmelCase_ : Dict = streaming
lowerCAmelCase_ : Any = num_proc
lowerCAmelCase_ : List[str] = kwargs
@abstractmethod
def lowercase_ ( self ) -> Optional[int]:
pass | 262 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[Any] = """gpt_neox"""
def __init__( self : List[str] , lowercase_ : str=50432 , lowercase_ : List[Any]=6144 , lowercase_ : List[Any]=44 , lowercase_ : Union[str, Any]=64 , lowercase_ : List[str]=24576 , lowercase_ : List[Any]="gelu" , lowercase_ : str=0.25 , lowercase_ : Optional[int]=10000 , lowercase_ : Optional[int]=0.0 , lowercase_ : Optional[int]=0.0 , lowercase_ : int=0.1 , lowercase_ : Tuple=2048 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : List[str]=1E-5 , lowercase_ : str=True , lowercase_ : str=0 , lowercase_ : Union[str, Any]=2 , lowercase_ : List[str]=False , lowercase_ : Optional[int]=True , lowercase_ : List[Any]=None , **lowercase_ : Optional[int] , ):
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : Dict = num_attention_heads
snake_case_ : List[Any] = intermediate_size
snake_case_ : List[Any] = hidden_act
snake_case_ : str = rotary_pct
snake_case_ : Dict = rotary_emb_base
snake_case_ : Optional[int] = attention_dropout
snake_case_ : Tuple = hidden_dropout
snake_case_ : Tuple = classifier_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Any = use_cache
snake_case_ : Optional[int] = tie_word_embeddings
snake_case_ : Any = use_parallel_residual
snake_case_ : Union[str, Any] = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def _snake_case ( self : Optional[int] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , lowercase_ ) 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}" )
snake_case_ : Any = self.rope_scaling.get('''type''' , lowercase_ )
snake_case_ : Union[str, Any] = self.rope_scaling.get('''factor''' , lowercase_ )
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(lowercase_ , lowercase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 264 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase__ ):
__lowerCAmelCase : str
__lowerCAmelCase : int
def _snake_case ( UpperCamelCase : Dict ):
if not isinstance(_a , _a ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(_a ) )]
def _snake_case ( UpperCamelCase : Tuple ):
if not isinstance(_a , _a ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
UpperCAmelCase : List[str] = all_rotations(_a )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
UpperCAmelCase : BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(_a ),
}
return response
def _snake_case ( UpperCamelCase : Dict , UpperCamelCase : Dict ):
if not isinstance(_a , _a ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
UpperCAmelCase : Union[str, Any] = int(_a )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(_a ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
UpperCAmelCase : Dict = [''''''] * len(_a )
for _ in range(len(_a ) ):
for i in range(len(_a ) ):
UpperCAmelCase : int = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
A: str = '''Provide a string that I will generate its BWT transform: '''
A: str = input(entry_msg).strip()
A: Optional[int] = bwt_transform(s)
print(
f"""Burrows Wheeler transform for string \'{s}\' results """
f"""in \'{result['bwt_string']}\'"""
)
A: str = reverse_bwt(result["bwt_string"], result["idx_original_string"])
print(
f"""Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' """
f"""we get original string \'{original_string}\'"""
)
| 109 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowercase__ : int = None
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : List[str] = '''▁'''
lowercase__ : Optional[int] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
lowercase__ : str = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
'''tokenizer_file''': {
'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
},
}
lowercase__ : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
_lowerCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Tuple = PegasusTokenizer
_lowerCAmelCase : str = ["""input_ids""", """attention_mask"""]
def __init__( self : Any , lowercase_ : Optional[Any]=None , lowercase_ : int=None , lowercase_ : Tuple="<pad>" , lowercase_ : int="</s>" , lowercase_ : Tuple="<unk>" , lowercase_ : str="<mask_2>" , lowercase_ : Optional[Any]="<mask_1>" , lowercase_ : str=None , lowercase_ : List[str]=103 , **lowercase_ : List[Any] , ):
snake_case_ : Dict = offset
if additional_special_tokens is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError(
f"additional_special_tokens should be of type {type(lowercase_ )}, but is"
f" {type(lowercase_ )}" )
snake_case_ : str = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f"<unk_{i}>" for i in range(len(lowercase_ ) , self.offset - 1 )
]
if len(set(lowercase_ ) ) != len(lowercase_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}." )
snake_case_ : Union[str, Any] = additional_special_tokens_extended
else:
snake_case_ : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f"<unk_{i}>" for i in range(2 , self.offset )]
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , pad_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , mask_token=lowercase_ , mask_token_sent=lowercase_ , offset=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
snake_case_ : List[Any] = vocab_file
snake_case_ : List[Any] = False if not self.vocab_file else True
def _snake_case ( self : str , lowercase_ : Union[str, Any] ):
snake_case_ : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
f" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}" )
return [1 if x in all_special_ids else 0 for x in seq]
def _snake_case ( self : int , lowercase_ : List , lowercase_ : Optional[List] = None , lowercase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(lowercase_ )
elif token_ids_a is None:
return self._special_token_mask(lowercase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _snake_case ( self : List[Any] , lowercase_ : Optional[int] , lowercase_ : str=None ):
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 _snake_case ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ : Dict = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
return (out_vocab_file,)
| 264 | 0 |
"""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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase__ : Any = '''▁'''
lowerCAmelCase__ : List[str] = {'''vocab_file''': '''sentencepiece.bpe.model'''}
lowerCAmelCase__ : Dict = {
'''vocab_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
),
}
}
lowerCAmelCase__ : List[Any] = {
'''facebook/nllb-200-distilled-600M''': 1_024,
}
# fmt: off
lowerCAmelCase__ : int = ['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']
class snake_case ( lowerCAmelCase__ ):
"""simple docstring"""
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = PRETRAINED_VOCAB_FILES_MAP
snake_case__ = ["""input_ids""", """attention_mask"""]
snake_case__ = []
snake_case__ = []
def __init__( self : Tuple ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Tuple="<s>" ,lowerCamelCase__ : Optional[int]="</s>" ,lowerCamelCase__ : Optional[int]="</s>" ,lowerCamelCase__ : str="<s>" ,lowerCamelCase__ : List[str]="<unk>" ,lowerCamelCase__ : int="<pad>" ,lowerCamelCase__ : List[Any]="<mask>" ,lowerCamelCase__ : List[Any]=None ,lowerCamelCase__ : Any=None ,lowerCamelCase__ : Any=None ,lowerCamelCase__ : Optional[Dict[str, Any]] = None ,lowerCamelCase__ : Any=None ,lowerCamelCase__ : List[Any]=False ,**lowerCamelCase__ : int ,):
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase__ = AddedToken(lowercase_ ,lstrip=lowercase_ ,rstrip=lowercase_ ) if isinstance(lowercase_ ,lowercase_ ) else mask_token
UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase__ = legacy_behaviour
super().__init__(
bos_token=lowercase_ ,eos_token=lowercase_ ,unk_token=lowercase_ ,sep_token=lowercase_ ,cls_token=lowercase_ ,pad_token=lowercase_ ,mask_token=lowercase_ ,tokenizer_file=lowercase_ ,src_lang=lowercase_ ,tgt_lang=lowercase_ ,additional_special_tokens=lowercase_ ,sp_model_kwargs=self.sp_model_kwargs ,legacy_behaviour=lowercase_ ,**lowercase_ ,)
UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowercase_ ) )
UpperCAmelCase__ = 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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCAmelCase__ = {'''<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
UpperCAmelCase__ = 1
UpperCAmelCase__ = len(self.sp_model )
UpperCAmelCase__ = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase_ )
}
UpperCAmelCase__ = {v: k for k, v in self.lang_code_to_id.items()}
UpperCAmelCase__ = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
UpperCAmelCase__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
UpperCAmelCase__ = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
UpperCAmelCase__ = src_lang if src_lang is not None else '''eng_Latn'''
UpperCAmelCase__ = self.lang_code_to_id[self._src_lang]
UpperCAmelCase__ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : List[Any] ):
UpperCAmelCase__ = self.__dict__.copy()
UpperCAmelCase__ = None
UpperCAmelCase__ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Dict ,lowerCamelCase__ : Tuple ):
UpperCAmelCase__ = d
# for backward compatibility
if not hasattr(self ,'sp_model_kwargs' ):
UpperCAmelCase__ = {}
UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def __lowerCAmelCase ( self : Optional[Any] ):
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def __lowerCAmelCase ( self : Optional[int] ):
return self._src_lang
@src_lang.setter
def __lowerCAmelCase ( self : Any ,lowerCamelCase__ : str ):
UpperCAmelCase__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ,lowerCamelCase__ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ ,token_ids_a=lowercase_ ,already_has_special_tokens=lowercase_ )
UpperCAmelCase__ = [1] * len(self.prefix_tokens )
UpperCAmelCase__ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(lowercase_ )) + suffix_ones
return prefix_ones + ([0] * len(lowercase_ )) + ([0] * len(lowercase_ )) + suffix_ones
def __lowerCAmelCase ( self : List[Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ):
UpperCAmelCase__ = [self.sep_token_id]
UpperCAmelCase__ = [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 : List[str] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] ,lowerCamelCase__ : Optional[str] ,**lowerCamelCase__ : Tuple ):
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
UpperCAmelCase__ = src_lang
UpperCAmelCase__ = self(lowercase_ ,add_special_tokens=lowercase_ ,return_tensors=lowercase_ ,**lowercase_ )
UpperCAmelCase__ = self.convert_tokens_to_ids(lowercase_ )
UpperCAmelCase__ = tgt_lang_id
return inputs
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : str ):
return self.sp_model.encode(lowercase_ ,out_type=lowercase_ )
def __lowerCAmelCase ( self : int ,lowerCamelCase__ : List[Any] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCAmelCase__ = self.sp_model.PieceToId(lowercase_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __lowerCAmelCase ( self : List[str] ,lowerCamelCase__ : Optional[Any] ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : Optional[int] ):
UpperCAmelCase__ = ''''''.join(lowercase_ ).replace(lowercase_ ,' ' ).strip()
return out_string
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] = None ):
if not os.path.isdir(lowercase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase__ = os.path.join(
lowercase_ ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ ,'wb' ) as fi:
UpperCAmelCase__ = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : str = "eng_Latn" ,lowerCamelCase__ : Optional[List[str]] = None ,lowerCamelCase__ : str = "fra_Latn" ,**lowerCamelCase__ : Dict ,):
UpperCAmelCase__ = src_lang
UpperCAmelCase__ = tgt_lang
return super().prepare_seqaseq_batch(lowercase_ ,lowercase_ ,**lowercase_ )
def __lowerCAmelCase ( self : str ):
return self.set_src_lang_special_tokens(self.src_lang )
def __lowerCAmelCase ( self : List[str] ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __lowerCAmelCase ( self : Optional[int] ,lowerCamelCase__ : int ):
UpperCAmelCase__ = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
UpperCAmelCase__ = []
UpperCAmelCase__ = [self.eos_token_id, self.cur_lang_code]
else:
UpperCAmelCase__ = [self.cur_lang_code]
UpperCAmelCase__ = [self.eos_token_id]
def __lowerCAmelCase ( self : Any ,lowerCamelCase__ : str ):
UpperCAmelCase__ = self.lang_code_to_id[lang]
if self.legacy_behaviour:
UpperCAmelCase__ = []
UpperCAmelCase__ = [self.eos_token_id, self.cur_lang_code]
else:
UpperCAmelCase__ = [self.cur_lang_code]
UpperCAmelCase__ = [self.eos_token_id]
| 98 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int=13 , lowercase_ : Optional[int]=7 , lowercase_ : Any=True , lowercase_ : Dict=True , lowercase_ : Dict=True , lowercase_ : Optional[Any]=99 , lowercase_ : Union[str, Any]=32 , lowercase_ : str=5 , lowercase_ : Union[str, Any]=4 , lowercase_ : Any=37 , lowercase_ : Tuple="gelu" , lowercase_ : Dict=0.1 , lowercase_ : Tuple=0.1 , lowercase_ : Optional[int]=512 , lowercase_ : Optional[Any]=16 , lowercase_ : Optional[Any]=2 , lowercase_ : Optional[Any]=0.02 , lowercase_ : List[Any]=3 , lowercase_ : Union[str, Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ : Any = parent
snake_case_ : List[str] = batch_size
snake_case_ : List[Any] = seq_length
snake_case_ : Optional[int] = is_training
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Optional[Any] = use_labels
snake_case_ : Union[str, Any] = vocab_size
snake_case_ : Any = hidden_size
snake_case_ : List[Any] = num_hidden_layers
snake_case_ : Any = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : Tuple = max_position_embeddings
snake_case_ : int = type_vocab_size
snake_case_ : Tuple = type_sequence_label_size
snake_case_ : str = initializer_range
snake_case_ : Tuple = num_labels
snake_case_ : str = num_choices
snake_case_ : Any = scope
snake_case_ : Dict = self.vocab_size - 1
def _snake_case ( self : int ):
snake_case_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Optional[Any] = None
if self.use_token_type_ids:
snake_case_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : str = None
snake_case_ : Dict = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ : int = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
snake_case_ : Any = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _snake_case ( self : Tuple , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , *lowercase_ : Dict ):
snake_case_ : List[Any] = OpenAIGPTModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Any = model(lowercase_ , token_type_ids=lowercase_ , head_mask=lowercase_ )
snake_case_ : Optional[Any] = model(lowercase_ , token_type_ids=lowercase_ )
snake_case_ : Optional[Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : List[Any] , *lowercase_ : Optional[Any] ):
snake_case_ : Union[str, Any] = OpenAIGPTLMHeadModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Union[str, Any] = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : List[str] , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Dict , *lowercase_ : Union[str, Any] ):
snake_case_ : Tuple = OpenAIGPTDoubleHeadsModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Dict = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : Any , lowercase_ : str , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , *lowercase_ : Any ):
snake_case_ : int = self.num_labels
snake_case_ : Any = OpenAIGPTForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Optional[Any] = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : int ):
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : str = config_and_inputs
snake_case_ : str = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : Dict = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_lowerCAmelCase : int = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_lowerCAmelCase : Union[str, Any] = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _snake_case ( self : Tuple , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _snake_case ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str]=False ):
snake_case_ : Dict = super()._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowercase_ , )
snake_case_ : int = inputs_dict['''labels''']
snake_case_ : Optional[Any] = inputs_dict['''labels''']
snake_case_ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowercase_ , )
snake_case_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase_ )
return inputs_dict
def _snake_case ( self : Any ):
snake_case_ : List[str] = OpenAIGPTModelTester(self )
snake_case_ : Dict = ConfigTester(self , config_class=lowercase_ , n_embd=37 )
def _snake_case ( self : List[str] ):
self.config_tester.run_common_tests()
def _snake_case ( self : Optional[Any] ):
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowercase_ )
def _snake_case ( self : List[str] ):
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowercase_ )
def _snake_case ( self : int ):
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowercase_ )
def _snake_case ( self : List[str] ):
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_ )
@slow
def _snake_case ( self : Dict ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Optional[Any] = OpenAIGPTModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
@slow
def _snake_case ( self : Optional[int] ):
snake_case_ : Optional[Any] = OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
model.to(lowercase_ )
snake_case_ : List[str] = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=lowercase_ ) # the president is
snake_case_ : List[Any] = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
snake_case_ : Optional[Any] = model.generate(lowercase_ , do_sample=lowercase_ )
self.assertListEqual(output_ids[0].tolist() , lowercase_ )
| 264 | 0 |
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def __lowercase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Dict , lowerCamelCase : Tuple=0 ):
# Format the message.
if name is None:
UpperCamelCase_ : str = None
else:
UpperCamelCase_ : Optional[int] = '''.''' * max(0 , spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
UpperCamelCase_ : Optional[Any] = fmt.format(_a )
# Print and recurse (if needed).
if isinstance(_a , _a ):
if msg is not None:
print(_a )
for k in val.keys():
recursive_print(_a , val[k] , spaces + 2 )
elif isinstance(_a , torch.Tensor ):
print(_a , ':' , val.size() )
else:
print(_a , ':' , _a )
def __lowercase ( lowerCamelCase : List[Any] , lowerCamelCase : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Tuple , lowerCamelCase : Tuple ):
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
UpperCamelCase_ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
UpperCamelCase_ : str = (num_heads, hidden_size, num_splits) + input_shape[1:]
UpperCamelCase_ : Any = param.view(*_a )
UpperCamelCase_ : Tuple = param.transpose(0 , 2 )
UpperCamelCase_ : int = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
UpperCamelCase_ : Tuple = (num_heads, num_splits, hidden_size) + input_shape[1:]
UpperCamelCase_ : Any = param.view(*_a )
UpperCamelCase_ : Optional[Any] = param.transpose(0 , 1 ).contiguous()
UpperCamelCase_ : Union[str, Any] = param.view(*_a )
return param
def __lowercase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] ):
# The converted output model.
UpperCamelCase_ : List[str] = {}
# old versions did not store training args
UpperCamelCase_ : List[str] = input_state_dict.get('args' , _a )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
UpperCamelCase_ : int = ds_args.padded_vocab_size
UpperCamelCase_ : List[Any] = ds_args.max_position_embeddings
UpperCamelCase_ : List[Any] = ds_args.hidden_size
UpperCamelCase_ : str = ds_args.num_layers
UpperCamelCase_ : int = ds_args.num_attention_heads
UpperCamelCase_ : int = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
UpperCamelCase_ : Any = config.n_head
# The hidden_size per head.
UpperCamelCase_ : Any = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
UpperCamelCase_ : Any = input_state_dict['''checkpoint_version''']
else:
UpperCamelCase_ : str = 0.0
# The model.
UpperCamelCase_ : int = input_state_dict['''model''']
# The language model.
UpperCamelCase_ : Union[str, Any] = model['''language_model''']
# The embeddings.
UpperCamelCase_ : str = lm['''embedding''']
# The word embeddings.
UpperCamelCase_ : Dict = embeddings['''word_embeddings''']['''weight''']
# Truncate the embedding table to vocab_size rows.
UpperCamelCase_ : List[str] = word_embeddings[: config.vocab_size, :]
UpperCamelCase_ : int = word_embeddings
# The position embeddings.
UpperCamelCase_ : List[str] = embeddings['''position_embeddings''']['''weight''']
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
UpperCamelCase_ : str = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match" )
# Store the position embeddings.
UpperCamelCase_ : Dict = pos_embeddings
# The transformer.
UpperCamelCase_ : Union[str, Any] = lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']
# The regex to extract layer names.
UpperCamelCase_ : Dict = re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)' )
# The simple map of names for "automated" rules.
UpperCamelCase_ : Dict = {
'''attention.dense''': '''.attn.c_proj.''',
'''self_attention.dense''': '''.attn.c_proj.''',
'''mlp.dense_h_to_4h''': '''.mlp.c_fc.''',
'''mlp.dense_4h_to_h''': '''.mlp.c_proj.''',
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
UpperCamelCase_ : Union[str, Any] = layer_re.match(_a )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
UpperCamelCase_ : Tuple = int(m.group(1 ) )
# The name of the operation.
UpperCamelCase_ : List[Any] = m.group(2 )
# Is it a weight or a bias?
UpperCamelCase_ : int = m.group(3 )
# The name of the layer.
UpperCamelCase_ : Any = F"transformer.h.{layer_idx}"
# For layernorm(s), simply store the layer norm.
if op_name.endswith('layernorm' ):
UpperCamelCase_ : str = '''ln_1''' if op_name.startswith('input' ) else '''ln_2'''
UpperCamelCase_ : Optional[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
UpperCamelCase_ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , _a , _a )
UpperCamelCase_ : List[Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
UpperCamelCase_ : Optional[Any] = torch.tensor(-1e4 , dtype=torch.floataa )
UpperCamelCase_ : List[str] = masked_bias
UpperCamelCase_ : Any = fix_query_key_value_ordering(_a , _a , 3 , _a , _a )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
UpperCamelCase_ : List[Any] = out_val.transpose(0 , 1 ).contiguous()
# Store.
UpperCamelCase_ : Tuple = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
UpperCamelCase_ : Tuple = fix_query_key_value_ordering(_a , _a , 3 , _a , _a )
# Store. No change of shape.
UpperCamelCase_ : List[str] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
UpperCamelCase_ : Union[str, Any] = megatron_to_transformers[op_name]
UpperCamelCase_ : List[Any] = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
UpperCamelCase_ : Optional[int] = megatron_to_transformers[op_name]
UpperCamelCase_ : Union[str, Any] = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
UpperCamelCase_ : Optional[int] = transformer['''final_layernorm.weight''']
UpperCamelCase_ : int = transformer['''final_layernorm.bias''']
# For LM head, transformers' wants the matrix to weight embeddings.
UpperCamelCase_ : str = word_embeddings
# It should be done!
return output_state_dict
def __lowercase ( ):
# Create the argument parser.
UpperCamelCase_ : Dict = argparse.ArgumentParser()
parser.add_argument('--print-checkpoint-structure' , action='store_true' )
parser.add_argument(
'path_to_checkpoint' , type=_a , help='Path to the checkpoint file (.zip archive or direct .pt file)' , )
parser.add_argument(
'--config_file' , default='' , type=_a , help='An optional config json file describing the pre-trained model.' , )
UpperCamelCase_ : List[str] = parser.parse_args()
# Extract the basename.
UpperCamelCase_ : int = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F"Extracting PyTorch state dictionary from {args.path_to_checkpoint}" )
if args.path_to_checkpoint.endswith('.zip' ):
with zipfile.ZipFile(args.path_to_checkpoint , 'r' ) as checkpoint:
with checkpoint.open('release/mp_rank_00/model_optim_rng.pt' ) as pytorch_dict:
UpperCamelCase_ : str = torch.load(_a , map_location='cpu' )
else:
UpperCamelCase_ : Any = torch.load(args.path_to_checkpoint , map_location='cpu' )
UpperCamelCase_ : Tuple = input_state_dict.get('args' , _a )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
UpperCamelCase_ : List[Any] = '''gelu_fast'''
elif ds_args.openai_gelu:
UpperCamelCase_ : int = '''gelu_new'''
else:
UpperCamelCase_ : List[str] = '''gelu'''
else:
# in the very early days this used to be "gelu_new"
UpperCamelCase_ : List[str] = '''gelu_new'''
# Spell out all parameters in case the defaults change.
UpperCamelCase_ : str = GPTaConfig(
vocab_size=50257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=_a , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1e-5 , initializer_range=0.0_2 , summary_type='cls_index' , summary_use_proj=_a , summary_activation=_a , summary_proj_to_labels=_a , summary_first_dropout=0.1 , scale_attn_weights=_a , use_cache=_a , bos_token_id=50256 , eos_token_id=50256 , )
else:
UpperCamelCase_ : List[Any] = GPTaConfig.from_json_file(args.config_file )
UpperCamelCase_ : str = ['''GPT2LMHeadModel''']
# Convert.
print('Converting' )
UpperCamelCase_ : Union[str, Any] = convert_megatron_checkpoint(_a , _a , _a )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(_a , _a )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
UpperCamelCase_ : Dict = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
UpperCamelCase_ : List[Any] = '''gpt2'''
elif tokenizer_type == "PretrainedFromHF":
UpperCamelCase_ : int = ds_args.tokenizer_name_or_path
else:
raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}" )
else:
UpperCamelCase_ : str = '''gpt2'''
UpperCamelCase_ : Optional[int] = AutoTokenizer.from_pretrained(_a )
UpperCamelCase_ : Optional[int] = type(_a ).__name__
UpperCamelCase_ : List[str] = tokenizer_class
# Store the config to file.
print('Saving config' )
config.save_pretrained(_a )
# Save tokenizer based on args
print(F"Adding {tokenizer_class} tokenizer files" )
tokenizer.save_pretrained(_a )
# Store the state_dict to file.
UpperCamelCase_ : Any = os.path.join(_a , 'pytorch_model.bin' )
print(F"Saving checkpoint to \"{output_checkpoint_file}\"" )
torch.save(_a , _a )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 175 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Any , lowercase_ : TransformeraDModel , lowercase_ : AutoencoderKL , lowercase_ : KarrasDiffusionSchedulers , lowercase_ : Optional[Dict[int, str]] = None , ):
super().__init__()
self.register_modules(transformer=lowercase_ , vae=lowercase_ , scheduler=lowercase_ )
# create a imagenet -> id dictionary for easier use
snake_case_ : Tuple = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''',''' ):
snake_case_ : str = int(lowercase_ )
snake_case_ : Any = dict(sorted(self.labels.items() ) )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, List[str]] ):
if not isinstance(lowercase_ , lowercase_ ):
snake_case_ : Tuple = list(lowercase_ )
for l in label:
if l not in self.labels:
raise ValueError(
f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}." )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self : Optional[int] , lowercase_ : List[int] , lowercase_ : float = 4.0 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : int = 50 , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , ):
snake_case_ : Any = len(lowercase_ )
snake_case_ : List[str] = self.transformer.config.sample_size
snake_case_ : Union[str, Any] = self.transformer.config.in_channels
snake_case_ : str = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowercase_ , device=self.device , dtype=self.transformer.dtype , )
snake_case_ : Optional[Any] = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
snake_case_ : Optional[int] = torch.tensor(lowercase_ , device=self.device ).reshape(-1 )
snake_case_ : Dict = torch.tensor([1000] * batch_size , device=self.device )
snake_case_ : Tuple = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
snake_case_ : List[Any] = latent_model_input[: len(lowercase_ ) // 2]
snake_case_ : Union[str, Any] = torch.cat([half, half] , dim=0 )
snake_case_ : Optional[Any] = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
snake_case_ : int = t
if not torch.is_tensor(lowercase_ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
snake_case_ : Tuple = latent_model_input.device.type == '''mps'''
if isinstance(lowercase_ , lowercase_ ):
snake_case_ : List[str] = torch.floataa if is_mps else torch.floataa
else:
snake_case_ : Optional[int] = torch.intaa if is_mps else torch.intaa
snake_case_ : List[Any] = torch.tensor([timesteps] , dtype=lowercase_ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
snake_case_ : str = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
snake_case_ : Tuple = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
snake_case_ : List[Any] = self.transformer(
lowercase_ , timestep=lowercase_ , class_labels=lowercase_ ).sample
# perform guidance
if guidance_scale > 1:
snake_case_, snake_case_ : Dict = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
snake_case_, snake_case_ : Any = torch.split(lowercase_ , len(lowercase_ ) // 2 , dim=0 )
snake_case_ : int = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
snake_case_ : str = torch.cat([half_eps, half_eps] , dim=0 )
snake_case_ : List[Any] = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
snake_case_, snake_case_ : Optional[Any] = torch.split(lowercase_ , lowercase_ , dim=1 )
else:
snake_case_ : List[str] = noise_pred
# compute previous image: x_t -> x_t-1
snake_case_ : int = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
if guidance_scale > 1:
snake_case_, snake_case_ : Optional[Any] = latent_model_input.chunk(2 , dim=0 )
else:
snake_case_ : Dict = latent_model_input
snake_case_ : Union[str, Any] = 1 / self.vae.config.scaling_factor * latents
snake_case_ : Tuple = self.vae.decode(lowercase_ ).sample
snake_case_ : str = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ : Union[str, Any] = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case_ : Union[str, Any] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowercase_ )
| 264 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
A_ : int = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}
class lowercase ( lowerCAmelCase__ ):
"""simple docstring"""
UpperCAmelCase = """ernie_m"""
UpperCAmelCase = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self ,a_ = 250_002 ,a_ = 768 ,a_ = 12 ,a_ = 12 ,a_ = 3_072 ,a_ = "gelu" ,a_ = 0.1 ,a_ = 0.1 ,a_ = 514 ,a_ = 0.02 ,a_ = 1 ,a_ = 1E-0_5 ,a_=None ,a_=False ,a_=0.0 ,**a_ ,) -> Any:
super().__init__(pad_token_id=lowercase_ ,**lowercase_ )
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : List[str] = intermediate_size
_UpperCAmelCase : int = hidden_act
_UpperCAmelCase : str = hidden_dropout_prob
_UpperCAmelCase : Tuple = attention_probs_dropout_prob
_UpperCAmelCase : List[str] = max_position_embeddings
_UpperCAmelCase : Union[str, Any] = initializer_range
_UpperCAmelCase : Any = layer_norm_eps
_UpperCAmelCase : int = classifier_dropout
_UpperCAmelCase : List[str] = is_decoder
_UpperCAmelCase : Dict = act_dropout
| 215 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self : List[Any] ):
snake_case_ : List[str] = ''''''
snake_case_ : Tuple = ''''''
snake_case_ : int = []
snake_case_ : Optional[int] = 0
snake_case_ : Optional[Any] = 256
snake_case_ : Tuple = 0
snake_case_ : Tuple = 0
snake_case_ : Optional[Any] = 0
snake_case_ : Any = 0
def _snake_case ( self : Optional[Any] , lowercase_ : List[Any] ):
snake_case_ : List[Any] = cva.imread(lowercase_ , 0 )
snake_case_ : Tuple = copy.deepcopy(self.img )
snake_case_, snake_case_, snake_case_ : List[Any] = plt.hist(self.img.ravel() , 256 , [0, 256] , label='''x''' )
snake_case_ : str = np.sum(lowercase_ )
for i in range(len(lowercase_ ) ):
snake_case_ : Optional[Any] = x[i] / self.k
self.sk += prk
snake_case_ : Any = (self.L - 1) * self.sk
if self.rem != 0:
snake_case_ : Dict = int(last % last )
snake_case_ : Union[str, Any] = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowercase_ )
snake_case_ : int = int(np.ma.count(self.img ) / self.img[1].size )
snake_case_ : Tuple = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
snake_case_ : Union[str, Any] = self.img[j][i]
if num != self.last_list[num]:
snake_case_ : List[str] = self.last_list[num]
cva.imwrite('''output_data/output.jpg''' , self.img )
def _snake_case ( self : Tuple ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _snake_case ( self : int ):
cva.imshow('''Output-Image''' , self.img )
cva.imshow('''Input-Image''' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowercase__ : Any = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
lowercase__ : Any = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 264 | 0 |
'''simple docstring'''
import re
import subprocess
import sys
A__ : List[str] =subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
A__ : Union[str, Any] =(
subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()
)
A__ : Union[str, Any] ='''|'''.join(sys.argv[1:])
A__ : Tuple =re.compile(rF"""^({joined_dirs}).*?\.py$""")
A__ : str =[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')
| 70 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] ):
snake_case_ : str = []
def _snake_case ( self : List[Any] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : List[str] , **lowercase_ : Tuple ):
self.events.append('''on_init_end''' )
def _snake_case ( self : List[Any] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_train_begin''' )
def _snake_case ( self : Any , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_train_end''' )
def _snake_case ( self : str , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any] , **lowercase_ : List[Any] ):
self.events.append('''on_epoch_begin''' )
def _snake_case ( self : Tuple , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ):
self.events.append('''on_epoch_end''' )
def _snake_case ( self : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , **lowercase_ : Optional[Any] ):
self.events.append('''on_step_begin''' )
def _snake_case ( self : int , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , **lowercase_ : List[str] ):
self.events.append('''on_step_end''' )
def _snake_case ( self : str , lowercase_ : int , lowercase_ : Dict , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_evaluate''' )
def _snake_case ( self : Dict , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : List[Any] , **lowercase_ : str ):
self.events.append('''on_predict''' )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int , **lowercase_ : Union[str, Any] ):
self.events.append('''on_save''' )
def _snake_case ( self : str , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : Any ):
self.events.append('''on_log''' )
def _snake_case ( self : Dict , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Union[str, Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_prediction_step''' )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : List[str] ):
snake_case_ : Tuple = tempfile.mkdtemp()
def _snake_case ( self : Tuple ):
shutil.rmtree(self.output_dir )
def _snake_case ( self : int , lowercase_ : Union[str, Any]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=64 , lowercase_ : Union[str, Any]=64 , lowercase_ : Union[str, Any]=None , lowercase_ : Any=False , **lowercase_ : List[Any] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
snake_case_ : int = RegressionDataset(length=lowercase_ )
snake_case_ : Any = RegressionDataset(length=lowercase_ )
snake_case_ : int = RegressionModelConfig(a=lowercase_ , b=lowercase_ )
snake_case_ : Tuple = RegressionPreTrainedModel(lowercase_ )
snake_case_ : Any = TrainingArguments(self.output_dir , disable_tqdm=lowercase_ , report_to=[] , **lowercase_ )
return Trainer(
lowercase_ , lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , callbacks=lowercase_ , )
def _snake_case ( self : Optional[int] , lowercase_ : Any , lowercase_ : List[Any] ):
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
# Order doesn't matter
snake_case_ : Any = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
snake_case_ : List[str] = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
for cba, cba in zip(lowercase_ , lowercase_ ):
if isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , lowercase_ )
elif isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , cba.__class__ )
elif not isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(cba.__class__ , lowercase_ )
else:
self.assertEqual(lowercase_ , lowercase_ )
def _snake_case ( self : Optional[Any] , lowercase_ : Tuple ):
snake_case_ : Tuple = ['''on_init_end''', '''on_train_begin''']
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = len(trainer.get_eval_dataloader() )
snake_case_ : List[Any] = ['''on_prediction_step'''] * len(trainer.get_eval_dataloader() ) + ['''on_log''', '''on_evaluate''']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('''on_epoch_begin''' )
for _ in range(lowercase_ ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('''on_log''' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('''on_save''' )
expected_events.append('''on_epoch_end''' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _snake_case ( self : List[str] ):
snake_case_ : Union[str, Any] = self.get_trainer()
snake_case_ : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# Callbacks passed at init are added to the default callbacks
snake_case_ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
snake_case_ : Optional[int] = self.get_trainer(disable_tqdm=lowercase_ )
snake_case_ : List[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : int ):
snake_case_ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
snake_case_ : List[Any] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : Dict = self.get_trainer()
snake_case_ : Optional[int] = trainer.pop_callback(lowercase_ )
self.assertEqual(cb.__class__ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# We can also add, pop, or remove by instance
snake_case_ : Optional[int] = self.get_trainer()
snake_case_ : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : List[Any] = self.get_trainer()
snake_case_ : Optional[int] = trainer.callback_handler.callbacks[0]
snake_case_ : Optional[Any] = trainer.pop_callback(lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : List[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='''ignore''' , category=lowercase_ )
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# Independent log/save/eval
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
snake_case_ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='''steps''' )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='''epoch''' )
trainer.train()
snake_case_ : Dict = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# A bit of everything
snake_case_ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy='''steps''' , )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# warning should be emitted for duplicated callbacks
with patch('''transformers.trainer_callback.logger.warning''' ) as warn_mock:
snake_case_ : Dict = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(lowercase_ ) in warn_mock.call_args[0][0]
| 264 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __a ( ):
UpperCAmelCase_ : int = HfArgumentParser(_a )
UpperCAmelCase_ : List[str] = parser.parse_args_into_dataclasses()[0]
UpperCAmelCase_ : Optional[Any] = TensorFlowBenchmark(args=_a )
try:
UpperCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
UpperCAmelCase_ : Union[str, Any] = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
UpperCAmelCase_ : Any = ''' '''.join(str(_a ).split(" " )[:-1] )
UpperCAmelCase_ : Optional[int] = ''''''
UpperCAmelCase_ : Any = eval(str(_a ).split(" " )[-1] )
UpperCAmelCase_ : int = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(_a )
if len(_a ) > 0:
UpperCAmelCase_ : Optional[int] = full_error_msg + begin_error_msg + str(_a )
raise ValueError(_a )
benchmark.run()
if __name__ == "__main__":
main()
| 61 |
"""simple docstring"""
import numpy as np
def __lowercase ( _a ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A__(lowerCAmelCase__, unittest.TestCase ):
"""simple docstring"""
_A : str = KandinskyVaaImgaImgPipeline
_A : Dict = ["""image_embeds""", """negative_image_embeds""", """image"""]
_A : int = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
_A : str = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
_A : int = False
@property
def UpperCamelCase__ ( self ) -> Any:
return 32
@property
def UpperCamelCase__ ( self ) -> List[Any]:
return 32
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
return self.time_input_dim
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def UpperCamelCase__ ( self ) -> List[Any]:
return 100
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
torch.manual_seed(0 )
a_ : List[str] = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
a_ : Any = UNetaDConditionModel(**lowercase_ )
return model
@property
def UpperCamelCase__ ( self ) -> int:
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 ) -> str:
torch.manual_seed(0 )
a_ : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCamelCase__ ( self ) -> Optional[int]:
a_ : Any = self.dummy_unet
a_ : Optional[int] = self.dummy_movq
a_ : str = {
'''num_train_timesteps''': 1_000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_0_0_8_5,
'''beta_end''': 0.0_1_2,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
a_ : str = DDIMScheduler(**lowercase_ )
a_ : Dict = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def UpperCamelCase__ ( self , _lowercase , _lowercase=0 ) -> Any:
a_ : List[str] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
a_ : Optional[int] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
lowercase_ )
# create init_image
a_ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
a_ : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
a_ : int = Image.fromarray(np.uinta(lowercase_ ) ).convert("""RGB""" ).resize((256, 256) )
if str(lowercase_ ).startswith("""mps""" ):
a_ : List[str] = torch.manual_seed(lowercase_ )
else:
a_ : Tuple = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
a_ : Optional[int] = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase__ ( self ) -> Any:
a_ : Dict = '''cpu'''
a_ : str = self.get_dummy_components()
a_ : List[Any] = self.pipeline_class(**lowercase_ )
a_ : List[Any] = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
a_ : str = pipe(**self.get_dummy_inputs(lowercase_ ) )
a_ : Tuple = output.images
a_ : Union[str, Any] = pipe(
**self.get_dummy_inputs(lowercase_ ) , return_dict=lowercase_ , )[0]
a_ : str = image[0, -3:, -3:, -1]
a_ : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a_ : Dict = np.array(
[0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class A__(unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> int:
a_ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_img2img_frog.npy""" )
a_ : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
a_ : Any = '''A red cartoon frog, 4k'''
a_ : Any = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(lowercase_ )
a_ : Any = KandinskyVaaImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-decoder""" , torch_dtype=torch.floataa )
a_ : Any = pipeline.to(lowercase_ )
pipeline.set_progress_bar_config(disable=lowercase_ )
a_ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
a_ : List[Any] = pipe_prior(
lowercase_ , generator=lowercase_ , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
a_ : Dict = pipeline(
image=lowercase_ , image_embeds=lowercase_ , negative_image_embeds=lowercase_ , generator=lowercase_ , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="""np""" , )
a_ : Optional[Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_ )
| 248 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] , lowercase_ : str , lowercase_ : int ):
snake_case_ : Dict = params
snake_case_ : Union[str, Any] = np.array(lowercase_ )
snake_case_ : str = np.array([len(lowercase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : Dict , lowercase_ : Union[str, Any] ):
return (self.token_ids[index], self.lengths[index])
def __len__( self : List[Any] ):
return len(self.lengths )
def _snake_case ( self : Tuple ):
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def _snake_case ( self : Tuple ):
snake_case_ : str = self.params.max_model_input_size
snake_case_ : Dict = self.lengths > max_len
logger.info(f"Splitting {sum(lowercase_ )} too long sequences." )
def divide_chunks(lowercase_ : Tuple , lowercase_ : Optional[Any] ):
return [l[i : i + n] for i in range(0 , len(lowercase_ ) , lowercase_ )]
snake_case_ : Tuple = []
snake_case_ : Any = []
if self.params.mlm:
snake_case_, snake_case_ : Union[str, Any] = self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
else:
snake_case_, snake_case_ : Dict = self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
snake_case_ : Any = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
snake_case_ : Dict = np.insert(lowercase_ , 0 , lowercase_ )
if sub_s[-1] != sep_id:
snake_case_ : Tuple = np.insert(lowercase_ , len(lowercase_ ) , lowercase_ )
assert len(lowercase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowercase_ )
new_tok_ids.extend(lowercase_ )
new_lengths.extend([len(lowercase_ ) for l in sub_seqs] )
snake_case_ : List[str] = np.array(lowercase_ )
snake_case_ : Optional[Any] = np.array(lowercase_ )
def _snake_case ( self : Optional[int] ):
snake_case_ : List[Any] = len(self )
snake_case_ : List[str] = self.lengths > 11
snake_case_ : Dict = self.token_ids[indices]
snake_case_ : Dict = self.lengths[indices]
snake_case_ : str = len(self )
logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def _snake_case ( self : Tuple ):
if "unk_token" not in self.params.special_tok_ids:
return
else:
snake_case_ : str = self.params.special_tok_ids['''unk_token''']
snake_case_ : str = len(self )
snake_case_ : int = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
snake_case_ : str = (unk_occs / self.lengths) < 0.5
snake_case_ : Optional[Any] = self.token_ids[indices]
snake_case_ : Optional[int] = self.lengths[indices]
snake_case_ : Dict = len(self )
logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def _snake_case ( self : Dict ):
if not self.params.is_master:
return
logger.info(f"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def _snake_case ( self : List[str] , lowercase_ : Dict ):
snake_case_ : Optional[int] = [t[0] for t in batch]
snake_case_ : str = [t[1] for t in batch]
assert len(lowercase_ ) == len(lowercase_ )
# Max for paddings
snake_case_ : str = max(lowercase_ )
# Pad token ids
if self.params.mlm:
snake_case_ : Tuple = self.params.special_tok_ids['''pad_token''']
else:
snake_case_ : Dict = self.params.special_tok_ids['''unk_token''']
snake_case_ : Any = [list(t.astype(lowercase_ ) ) + [pad_idx] * (max_seq_len_ - len(lowercase_ )) for t in token_ids]
assert len(tk_ ) == len(lowercase_ )
assert all(len(lowercase_ ) == max_seq_len_ for t in tk_ )
snake_case_ : str = torch.tensor(tk_ ) # (bs, max_seq_len_)
snake_case_ : Optional[int] = torch.tensor(lowercase_ ) # (bs)
return tk_t, lg_t
| 264 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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 lowercase_ ( lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = KandinskyImgaImgPipeline
UpperCAmelCase_ : Dict = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
UpperCAmelCase_ : Optional[Any] = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
UpperCAmelCase_ : Union[str, Any] = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCAmelCase_ : Optional[int] = False
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
return 32
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[Any]:
return 32
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
return self.time_input_dim
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->Union[str, Any]:
return self.time_input_dim * 4
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
return 100
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
lowerCAmelCase = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
torch.manual_seed(0 )
lowerCAmelCase = 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 , )
lowerCAmelCase = MultilingualCLIP(lowercase_ )
lowerCAmelCase = text_encoder.eval()
return text_encoder
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
torch.manual_seed(0 )
lowerCAmelCase = {
'''in_channels''': 4,
# 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,
}
lowerCAmelCase = UNetaDConditionModel(**lowercase_ )
return model
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
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 SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
torch.manual_seed(0 )
lowerCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
lowerCAmelCase = self.dummy_text_encoder
lowerCAmelCase = self.dummy_tokenizer
lowerCAmelCase = self.dummy_unet
lowerCAmelCase = self.dummy_movq
lowerCAmelCase = {
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_0_0_8_5,
'''beta_end''': 0.0_1_2,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowerCAmelCase = DDIMScheduler(**lowercase_ )
lowerCAmelCase = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=0 ) ->Optional[Any]:
lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(lowercase_ )
# create init_image
lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase = Image.fromarray(np.uinta(lowercase_ ) ).convert('''RGB''' ).resize((256, 256) )
if str(lowercase_ ).startswith('''mps''' ):
lowerCAmelCase = torch.manual_seed(lowercase_ )
else:
lowerCAmelCase = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowerCAmelCase = {
'''prompt''': '''horse''',
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
lowerCAmelCase = '''cpu'''
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**lowercase_ )
lowerCAmelCase = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowerCAmelCase = pipe(**self.get_dummy_inputs(lowercase_ ) )
lowerCAmelCase = output.images
lowerCAmelCase = pipe(
**self.get_dummy_inputs(lowercase_ ) , return_dict=lowercase_ , )[0]
lowerCAmelCase = image[0, -3:, -3:, -1]
lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase = np.array(
[0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3] )
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 lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
lowerCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_img2img_frog.npy''' )
lowerCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowerCAmelCase = '''A red cartoon frog, 4k'''
lowerCAmelCase = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(lowercase_ )
lowerCAmelCase = KandinskyImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1''' , torch_dtype=torch.floataa )
lowerCAmelCase = pipeline.to(lowercase_ )
pipeline.set_progress_bar_config(disable=lowercase_ )
lowerCAmelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowerCAmelCase = pipe_prior(
lowercase_ , generator=lowercase_ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
lowerCAmelCase = pipeline(
lowercase_ , image=lowercase_ , image_embeds=lowercase_ , negative_image_embeds=lowercase_ , generator=lowercase_ , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
lowerCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_ )
| 338 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __lowercase ( _a , _a , _a = "x" , _a = 10**-10 , _a = 1 , ):
snake_case_ : Any = symbols(_a )
snake_case_ : int = lambdify(_a , _a )
snake_case_ : Optional[Any] = lambdify(_a , diff(_a , _a ) )
snake_case_ : Optional[Any] = starting_point
while True:
if diff_function(_a ) != 0:
snake_case_ : Optional[int] = prev_guess - multiplicity * func(_a ) / diff_function(
_a )
else:
raise ZeroDivisionError('''Could not find root''' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
snake_case_ : int = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
# Find root of polynomial
# Find fourth Root of 5
print(f'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')
# Find value of e
print(
'''The root of log(y) - 1 = 0 is ''',
f'{newton_raphson("log(y) - 1", 2, variable="y")}',
)
# Exponential Roots
print(
'''The root of exp(x) - 1 = 0 is''',
f'{newton_raphson("exp(x) - 1", 10, precision=0.005)}',
)
# Find root of cos(x)
print(f'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')
| 264 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self : Any , _A : TransformeraDModel , _A : AutoencoderKL , _A : KarrasDiffusionSchedulers , _A : Optional[Dict[int, str]] = None , ) -> Optional[Any]:
super().__init__()
self.register_modules(transformer=lowercase_ , vae=lowercase_ , scheduler=lowercase_ )
# create a imagenet -> id dictionary for easier use
__magic_name__ : Tuple = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(',' ):
__magic_name__ : str = int(lowercase_ )
__magic_name__ : Any = dict(sorted(self.labels.items() ) )
def __lowerCAmelCase ( self : List[Any] , _A : Union[str, List[str]] ) -> Optional[Any]:
if not isinstance(lowercase_ , lowercase_ ):
__magic_name__ : Tuple = list(lowercase_ )
for l in label:
if l not in self.labels:
raise ValueError(
F'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self : Optional[int] , _A : List[int] , _A : float = 4.0 , _A : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _A : int = 50 , _A : Optional[str] = "pil" , _A : bool = True , ) -> List[str]:
__magic_name__ : Any = len(lowercase_ )
__magic_name__ : List[str] = self.transformer.config.sample_size
__magic_name__ : Union[str, Any] = self.transformer.config.in_channels
__magic_name__ : str = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowercase_ , device=self.device , dtype=self.transformer.dtype , )
__magic_name__ : Optional[Any] = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
__magic_name__ : Optional[int] = torch.tensor(lowercase_ , device=self.device ).reshape(-1 )
__magic_name__ : Dict = torch.tensor([1000] * batch_size , device=self.device )
__magic_name__ : Tuple = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
__magic_name__ : List[Any] = latent_model_input[: len(lowercase_ ) // 2]
__magic_name__ : Union[str, Any] = torch.cat([half, half] , dim=0 )
__magic_name__ : Optional[Any] = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
__magic_name__ : int = t
if not torch.is_tensor(lowercase_ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
__magic_name__ : Tuple = latent_model_input.device.type == '''mps'''
if isinstance(lowercase_ , lowercase_ ):
__magic_name__ : List[str] = torch.floataa if is_mps else torch.floataa
else:
__magic_name__ : Optional[int] = torch.intaa if is_mps else torch.intaa
__magic_name__ : List[Any] = torch.tensor([timesteps] , dtype=lowercase_ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
__magic_name__ : str = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__magic_name__ : Tuple = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
__magic_name__ : List[Any] = self.transformer(
lowercase_ , timestep=lowercase_ , class_labels=lowercase_ ).sample
# perform guidance
if guidance_scale > 1:
__magic_name__ : Dict = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
__magic_name__ : Any = torch.split(lowercase_ , len(lowercase_ ) // 2 , dim=0 )
__magic_name__ : int = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
__magic_name__ : str = torch.cat([half_eps, half_eps] , dim=0 )
__magic_name__ : List[Any] = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
__magic_name__ : Optional[Any] = torch.split(lowercase_ , lowercase_ , dim=1 )
else:
__magic_name__ : List[str] = noise_pred
# compute previous image: x_t -> x_t-1
__magic_name__ : int = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
if guidance_scale > 1:
__magic_name__ : Optional[Any] = latent_model_input.chunk(2 , dim=0 )
else:
__magic_name__ : Dict = latent_model_input
__magic_name__ : Union[str, Any] = 1 / self.vae.config.scaling_factor * latents
__magic_name__ : Tuple = self.vae.decode(lowercase_ ).sample
__magic_name__ : str = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__magic_name__ : Union[str, Any] = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__magic_name__ : Union[str, Any] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowercase_ ) | 331 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_UpperCamelCase : str = logging.get_logger(__name__)
_UpperCamelCase : Optional[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_UpperCamelCase : List[str] = {
'''vocab_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-german-cased''': (
'''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
),
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
},
}
_UpperCamelCase : Optional[int] = {
'''distilbert-base-uncased''': 5_1_2,
'''distilbert-base-uncased-distilled-squad''': 5_1_2,
'''distilbert-base-cased''': 5_1_2,
'''distilbert-base-cased-distilled-squad''': 5_1_2,
'''distilbert-base-german-cased''': 5_1_2,
'''distilbert-base-multilingual-cased''': 5_1_2,
}
_UpperCamelCase : Tuple = {
'''distilbert-base-uncased''': {'''do_lower_case''': True},
'''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
'''distilbert-base-cased''': {'''do_lower_case''': False},
'''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
'''distilbert-base-german-cased''': {'''do_lower_case''': False},
'''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class a ( lowerCAmelCase__ ):
UpperCAmelCase_ : List[str] =VOCAB_FILES_NAMES
UpperCAmelCase_ : List[str] =PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ : str =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ : Optional[int] =PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase_ : List[str] =["""input_ids""", """attention_mask"""]
UpperCAmelCase_ : Dict =DistilBertTokenizer
def __init__( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=True , _lowerCamelCase="[UNK]" , _lowerCamelCase="[SEP]" , _lowerCamelCase="[PAD]" , _lowerCamelCase="[CLS]" , _lowerCamelCase="[MASK]" , _lowerCamelCase=True , _lowerCamelCase=None , **_lowerCamelCase , ):
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , do_lower_case=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , tokenize_chinese_chars=lowercase_ , strip_accents=lowercase_ , **lowercase_ , )
lowercase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , lowercase_ ) != do_lower_case
or normalizer_state.get('strip_accents' , lowercase_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , lowercase_ ) != tokenize_chinese_chars
):
lowercase = getattr(lowercase_ , normalizer_state.pop('type' ) )
lowercase = do_lower_case
lowercase = strip_accents
lowercase = tokenize_chinese_chars
lowercase = normalizer_class(**lowercase_ )
lowercase = do_lower_case
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase=None ):
lowercase = [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 UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ):
lowercase = [self.sep_token_id]
lowercase = [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 UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ):
lowercase = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
| 220 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def __lowercase ( _a ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
class snake_case__( lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ["""pixel_values"""]
def __init__( self , __lowercase = True , __lowercase = None , __lowercase = None , __lowercase = PILImageResampling.BILINEAR , __lowercase = True , __lowercase = 1 / 2_5_5 , __lowercase = True , __lowercase = None , __lowercase = None , **__lowercase , ) -> Dict:
super().__init__(**lowercase_ )
lowerCAmelCase_ : Optional[int] = size if size is not None else {'''shortest_edge''': 3_8_4}
lowerCAmelCase_ : Dict = get_size_dict(lowercase_ , default_to_square=lowercase_ )
lowerCAmelCase_ : List[Any] = do_resize
lowerCAmelCase_ : str = size
# Default value set here for backwards compatibility where the value in config is None
lowerCAmelCase_ : Optional[Any] = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
lowerCAmelCase_ : Optional[int] = resample
lowerCAmelCase_ : Any = do_rescale
lowerCAmelCase_ : Any = rescale_factor
lowerCAmelCase_ : Optional[Any] = do_normalize
lowerCAmelCase_ : Tuple = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase_ : Tuple = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase = PILImageResampling.BICUBIC , __lowercase = None , **__lowercase , ) -> List[str]:
lowerCAmelCase_ : int = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "shortest_edge" not in size:
raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""" )
lowerCAmelCase_ : Any = size['''shortest_edge''']
if shortest_edge < 3_8_4:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
lowerCAmelCase_ : int = int(shortest_edge / crop_pct )
lowerCAmelCase_ : Tuple = get_resize_output_image_size(lowercase_ , size=lowercase_ , default_to_square=lowercase_ )
lowerCAmelCase_ : str = resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=lowercase_ , size=(shortest_edge, shortest_edge) , data_format=lowercase_ , **lowercase_ )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
lowercase_ , size=(shortest_edge, shortest_edge) , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def lowercase_ ( self , __lowercase , __lowercase , __lowercase = None , **__lowercase , ) -> Optional[int]:
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase , ) -> int:
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def lowercase_ ( self , __lowercase , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , ) -> Tuple:
lowerCAmelCase_ : Dict = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase_ : Tuple = crop_pct if crop_pct is not None else self.crop_pct
lowerCAmelCase_ : Any = resample if resample is not None else self.resample
lowerCAmelCase_ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase_ : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase_ : List[str] = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase_ : Optional[int] = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase_ : List[Any] = image_std if image_std is not None else self.image_std
lowerCAmelCase_ : Optional[Any] = size if size is not None else self.size
lowerCAmelCase_ : List[str] = get_size_dict(lowercase_ , default_to_square=lowercase_ )
lowerCAmelCase_ : List[Any] = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
raise ValueError('''crop_pct must be specified if size < 384.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
lowerCAmelCase_ : str = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
lowerCAmelCase_ : Optional[Any] = [self.resize(image=lowercase_ , size=lowercase_ , crop_pct=lowercase_ , resample=lowercase_ ) for image in images]
if do_rescale:
lowerCAmelCase_ : Any = [self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
if do_normalize:
lowerCAmelCase_ : str = [self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ ) for image in images]
lowerCAmelCase_ : Optional[int] = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
lowerCAmelCase_ : Optional[Any] = {'''pixel_values''': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ ) | 262 |
"""simple docstring"""
import sys
lowercase__ : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __lowercase ( _a ):
snake_case_ : List[Any] = 1
for digit in s:
product *= int(_a )
return product
def __lowercase ( _a = N ):
snake_case_ : Optional[int] = -sys.maxsize - 1
snake_case_ : str = n[:13]
snake_case_ : List[Any] = 13
while cur_index < len(_a ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
snake_case_ : int = substr[1:] + n[cur_index]
cur_index += 1
else:
snake_case_ : Optional[Any] = max(_a , str_eval(_a ) )
snake_case_ : Any = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'{solution() = }')
| 264 | 0 |
"""simple docstring"""
A: Union[str, Any] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
A: Union[str, Any] = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
A: List[Any] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : List[Any] = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Any = ['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : int = [
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = [
'''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDistilBertForMaskedLM''',
'''TFDistilBertForMultipleChoice''',
'''TFDistilBertForQuestionAnswering''',
'''TFDistilBertForSequenceClassification''',
'''TFDistilBertForTokenClassification''',
'''TFDistilBertMainLayer''',
'''TFDistilBertModel''',
'''TFDistilBertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''FlaxDistilBertForMaskedLM''',
'''FlaxDistilBertForMultipleChoice''',
'''FlaxDistilBertForQuestionAnswering''',
'''FlaxDistilBertForSequenceClassification''',
'''FlaxDistilBertForTokenClassification''',
'''FlaxDistilBertModel''',
'''FlaxDistilBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
lowercase__ : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : int=13 ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : Any=True ,lowerCamelCase__ : Dict=True ,lowerCamelCase__ : Dict=True ,lowerCamelCase__ : Optional[Any]=99 ,lowerCamelCase__ : Union[str, Any]=32 ,lowerCamelCase__ : str=5 ,lowerCamelCase__ : Union[str, Any]=4 ,lowerCamelCase__ : Any=37 ,lowerCamelCase__ : Tuple="gelu" ,lowerCamelCase__ : Dict=0.1 ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : Optional[int]=512 ,lowerCamelCase__ : Optional[Any]=16 ,lowerCamelCase__ : Optional[Any]=2 ,lowerCamelCase__ : Optional[Any]=0.0_2 ,lowerCamelCase__ : List[Any]=3 ,lowerCamelCase__ : Union[str, Any]=4 ,lowerCamelCase__ : List[Any]=None ,):
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = seq_length
UpperCAmelCase__ = is_training
UpperCAmelCase__ = use_token_type_ids
UpperCAmelCase__ = use_labels
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = type_vocab_size
UpperCAmelCase__ = type_sequence_label_size
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = num_labels
UpperCAmelCase__ = num_choices
UpperCAmelCase__ = scope
UpperCAmelCase__ = self.vocab_size - 1
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ = None
if self.use_token_type_ids:
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
UpperCAmelCase__ = ids_tensor([self.batch_size] ,self.num_choices )
UpperCAmelCase__ = OpenAIGPTConfig(
vocab_size=self.vocab_size ,n_embd=self.hidden_size ,n_layer=self.num_hidden_layers ,n_head=self.num_attention_heads ,n_positions=self.max_position_embeddings ,pad_token_id=self.pad_token_id ,)
UpperCAmelCase__ = ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : Any ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Dict ,*lowerCamelCase__ : Dict ):
UpperCAmelCase__ = OpenAIGPTModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase__ = model(lowercase_ ,token_type_ids=lowercase_ ,head_mask=lowercase_ )
UpperCAmelCase__ = model(lowercase_ ,token_type_ids=lowercase_ )
UpperCAmelCase__ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : Dict ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : List[Any] ,*lowerCamelCase__ : Optional[Any] ):
UpperCAmelCase__ = OpenAIGPTLMHeadModel(lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase__ = model(lowercase_ ,token_type_ids=lowercase_ ,labels=lowercase_ )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self : List[str] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Dict ,*lowerCamelCase__ : Union[str, Any] ):
UpperCAmelCase__ = OpenAIGPTDoubleHeadsModel(lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase__ = model(lowercase_ ,token_type_ids=lowercase_ ,labels=lowercase_ )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self : Any ,lowerCamelCase__ : str ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Optional[Any] ,*lowerCamelCase__ : Any ):
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = OpenAIGPTForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
UpperCAmelCase__ = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase__ = model(lowercase_ ,token_type_ids=lowercase_ ,labels=lowercase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
UpperCAmelCase__
) = config_and_inputs
UpperCAmelCase__ = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class snake_case ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
snake_case__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
snake_case__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
snake_case__ = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : int ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Union[str, Any] ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def __lowerCAmelCase ( self : Optional[int] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[str]=False ):
UpperCAmelCase__ = super()._prepare_for_class(lowercase_ ,lowercase_ ,return_labels=lowercase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
UpperCAmelCase__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,dtype=torch.long ,device=lowercase_ ,)
UpperCAmelCase__ = inputs_dict['''labels''']
UpperCAmelCase__ = inputs_dict['''labels''']
UpperCAmelCase__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) ,dtype=torch.long ,device=lowercase_ ,)
UpperCAmelCase__ = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=lowercase_ )
return inputs_dict
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = OpenAIGPTModelTester(self )
UpperCAmelCase__ = ConfigTester(self ,config_class=lowercase_ ,n_embd=37 )
def __lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : Optional[Any] ):
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowercase_ )
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowercase_ )
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowercase_ )
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_ )
@slow
def __lowerCAmelCase ( self : Dict ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ = OpenAIGPTModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
class snake_case ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self : Optional[int] ):
UpperCAmelCase__ = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(lowercase_ )
UpperCAmelCase__ = torch.tensor([[481, 4_735, 544]] ,dtype=torch.long ,device=lowercase_ ) # the president is
UpperCAmelCase__ = [
481,
4_735,
544,
246,
963,
870,
762,
239,
244,
40_477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
UpperCAmelCase__ = model.generate(lowercase_ ,do_sample=lowercase_ )
self.assertListEqual(output_ids[0].tolist() ,lowercase_ )
| 98 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = logging.get_logger(__name__)
def __lowercase ( _a , _a=False ):
snake_case_ : List[str] = []
# fmt: off
# stem:
rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''') )
rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''') )
rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''') )
# backbone
rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case_ : Optional[int] = [(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'''),
] )
# fmt: on
return rename_keys
def __lowercase ( _a , _a , _a=False ):
for i in range(config.num_hidden_layers ):
if base_model:
snake_case_ : List[str] = ''''''
else:
snake_case_ : Dict = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ : List[str] = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
snake_case_ : Optional[int] = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Any = in_proj_weight[
: config.hidden_size, :
]
snake_case_ : Dict = in_proj_bias[: config.hidden_size]
snake_case_ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case_ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case_ : Dict = in_proj_weight[
-config.hidden_size :, :
]
snake_case_ : str = in_proj_bias[-config.hidden_size :]
def __lowercase ( _a ):
snake_case_ : Dict = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(_a , _a )
def __lowercase ( _a , _a , _a ):
snake_case_ : Union[str, Any] = dct.pop(_a )
snake_case_ : Union[str, Any] = val
def __lowercase ( ):
snake_case_ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ : Tuple = Image.open(requests.get(_a , stream=_a ).raw )
return im
@torch.no_grad()
def __lowercase ( _a , _a , _a=False ):
snake_case_ : str = BitConfig(
global_padding='''same''' , layer_type='''bottleneck''' , depths=(3, 4, 9) , out_features=['''stage3'''] , embedding_dynamic_padding=_a , )
snake_case_ : Tuple = ViTHybridConfig(backbone_config=_a , image_size=384 , num_labels=1_000 )
snake_case_ : int = False
# load original model from timm
snake_case_ : str = timm.create_model(_a , pretrained=_a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case_ : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_a )
snake_case_ : int = create_rename_keys(_a , _a )
for src, dest in rename_keys:
rename_key(_a , _a , _a )
read_in_q_k_v(_a , _a , _a )
snake_case_ : Optional[Any] = '''huggingface/label-files'''
snake_case_ : Any = '''imagenet-1k-id2label.json'''
snake_case_ : Dict = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
snake_case_ : Dict = {int(_a ): v for k, v in idalabel.items()}
snake_case_ : Optional[int] = idalabel
snake_case_ : Optional[Any] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
snake_case_ : Optional[Any] = ViTHybridModel(_a ).eval()
else:
snake_case_ : Any = ViTHybridForImageClassification(_a ).eval()
model.load_state_dict(_a )
# create image processor
snake_case_ : Optional[Any] = create_transform(**resolve_data_config({} , model=_a ) )
snake_case_ : List[Any] = transform.transforms
snake_case_ : Optional[Any] = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
snake_case_ : List[Any] = ViTHybridImageProcessor(
do_resize=_a , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_a , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=_a , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Optional[int] = transform(_a ).unsqueeze(0 )
snake_case_ : int = processor(_a , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(_a , _a )
# verify logits
with torch.no_grad():
snake_case_ : List[str] = model(_a )
snake_case_ : Any = outputs.logits
print('''Predicted class:''' , logits.argmax(-1 ).item() )
if base_model:
snake_case_ : Optional[Any] = timm_model.forward_features(_a )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_a , outputs.pooler_output , atol=1E-3 )
else:
snake_case_ : int = timm_model(_a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_a , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(f"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_a )
if push_to_hub:
print(f"Pushing model and processor to the hub {vit_name}" )
model.push_to_hub(f"ybelkada/{vit_name}" )
processor.push_to_hub(f"ybelkada/{vit_name}" )
if __name__ == "__main__":
lowercase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--vit_name''',
default='''vit_base_r50_s16_384''',
type=str,
help='''Name of the hybrid ViT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
lowercase__ : Any = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 264 | 0 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
a_ = logging.getLogger(__name__)
a_ = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _lowercase :
lowercase = field(
default=lowerCAmelCase__ , metadata={
'help': (
'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
)
} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowerCAmelCase__ )} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={
'help': (
'Override some existing default config settings when a model is trained from scratch. Example: '
'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
)
} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
lowercase = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
"""simple docstring"""
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
'--config_overrides can\'t be used in combination with --config_name or --model_name_or_path' )
@dataclass
class _lowercase :
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
lowercase = field(default=lowerCAmelCase__ , metadata={'help': 'The input training data file (a text file).'} )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
lowercase = field(
default=5 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated. Default to the max input length of the model.'
)
} , )
lowercase = field(
default=lowerCAmelCase__ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
lowercase = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
lowercase = field(
default=lowerCAmelCase__ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
"""simple docstring"""
if self.train_file is not None:
UpperCamelCase_ : Any = self.train_file.split('.' )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
UpperCamelCase_ : int = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def __lowercase ( lowerCamelCase : int , lowerCamelCase : List[str] ):
with open(_a , 'r' , encoding='utf-8' ) as f:
UpperCamelCase_ : Union[str, Any] = [json.loads(_a ) for line in f.read().splitlines() if (len(_a ) > 0 and not line.isspace())]
assert len(_a ) == len(_a )
UpperCamelCase_ : Dict = {c: dataset[c] for c in dataset.column_names}
UpperCamelCase_ : List[str] = refs
return Dataset.from_dict(_a )
def __lowercase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase_ : str = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase_ : Any = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase_ : Dict = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
UpperCamelCase_ : Optional[Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase_ : Dict = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , _a )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
UpperCamelCase_ : Optional[Any] = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
UpperCamelCase_ : int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"train[:{data_args.validation_split_percentage}%]" , )
UpperCamelCase_ : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"train[{data_args.validation_split_percentage}%:]" , )
else:
UpperCamelCase_ : Any = {}
if data_args.train_file is not None:
UpperCamelCase_ : List[Any] = data_args.train_file
if data_args.validation_file is not None:
UpperCamelCase_ : Optional[Any] = data_args.validation_file
UpperCamelCase_ : Optional[int] = data_args.train_file.split('.' )[-1]
if extension == "txt":
UpperCamelCase_ : Tuple = '''text'''
UpperCamelCase_ : Any = load_dataset(_a , data_files=_a )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase_ : Tuple = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase_ : Tuple = AutoConfig.from_pretrained(model_args.config_name , **_a )
elif model_args.model_name_or_path:
UpperCamelCase_ : int = AutoConfig.from_pretrained(model_args.model_name_or_path , **_a )
else:
UpperCamelCase_ : List[Any] = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
UpperCamelCase_ : List[str] = {
'''cache_dir''': model_args.cache_dir,
'''use_fast''': model_args.use_fast_tokenizer,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
UpperCamelCase_ : List[Any] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **_a )
elif model_args.model_name_or_path:
UpperCamelCase_ : str = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **_a )
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.' )
if model_args.model_name_or_path:
UpperCamelCase_ : Optional[int] = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_a , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
UpperCamelCase_ : str = AutoModelForMaskedLM.from_config(_a )
model.resize_token_embeddings(len(_a ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
UpperCamelCase_ : Optional[int] = datasets['''train'''].column_names
else:
UpperCamelCase_ : str = datasets['''validation'''].column_names
UpperCamelCase_ : str = '''text''' if '''text''' in column_names else column_names[0]
UpperCamelCase_ : int = '''max_length''' if data_args.pad_to_max_length else False
def tokenize_function(lowerCamelCase : List[Any] ):
# Remove empty lines
UpperCamelCase_ : Any = [line for line in examples['''text'''] if len(_a ) > 0 and not line.isspace()]
return tokenizer(examples['text'] , padding=_a , truncation=_a , max_length=data_args.max_seq_length )
UpperCamelCase_ : List[Any] = datasets.map(
_a , batched=_a , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
UpperCamelCase_ : List[Any] = add_chinese_references(tokenized_datasets['train'] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
UpperCamelCase_ : Optional[Any] = add_chinese_references(
tokenized_datasets['validation'] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
UpperCamelCase_ : List[str] = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
UpperCamelCase_ : List[Any] = False
# Data collator
# This one will take care of randomly masking the tokens.
UpperCamelCase_ : Optional[Any] = DataCollatorForWholeWordMask(tokenizer=_a , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
UpperCamelCase_ : int = Trainer(
model=_a , args=_a , train_dataset=tokenized_datasets['train'] if training_args.do_train else None , eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None , tokenizer=_a , data_collator=_a , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
UpperCamelCase_ : Dict = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
UpperCamelCase_ : Optional[int] = model_args.model_name_or_path
else:
UpperCamelCase_ : str = None
UpperCamelCase_ : Union[str, Any] = trainer.train(resume_from_checkpoint=_a )
trainer.save_model() # Saves the tokenizer too for easy upload
UpperCamelCase_ : Optional[Any] = os.path.join(training_args.output_dir , 'train_results.txt' )
if trainer.is_world_process_zero():
with open(_a , 'w' ) as writer:
logger.info('***** Train results *****' )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F" {key} = {value}" )
writer.write(F"{key} = {value}\n" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , 'trainer_state.json' ) )
# Evaluation
UpperCamelCase_ : Any = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
UpperCamelCase_ : Dict = trainer.evaluate()
UpperCamelCase_ : int = math.exp(eval_output['eval_loss'] )
UpperCamelCase_ : Any = perplexity
UpperCamelCase_ : Tuple = os.path.join(training_args.output_dir , 'eval_results_mlm_wwm.txt' )
if trainer.is_world_process_zero():
with open(_a , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in sorted(results.items() ):
logger.info(F" {key} = {value}" )
writer.write(F"{key} = {value}\n" )
return results
def __lowercase ( lowerCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 175 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
lowercase__ : str = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def __lowercase ( _a , _a ):
snake_case_ : Optional[int] = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
snake_case_ : List[Any] = int(re.match(r'''.*layer_(\d*).*''' , _a )[1] )
layer_number -= 3
return f"h.{layer_number}." + key
def __lowercase ( _a ):
if dtype == torch.bool:
return 1 / 8
snake_case_ : Dict = re.search(r'''[^\d](\d+)$''' , str(_a ) )
if bit_search is None:
raise ValueError(f"`dtype` is not a valid dtype: {dtype}." )
snake_case_ : Optional[int] = int(bit_search.groups()[0] )
return bit_size // 8
def __lowercase ( _a , _a , _a , _a , _a ):
# Construct model
if bloom_config_file == "":
snake_case_ : int = BloomConfig()
else:
snake_case_ : List[str] = BloomConfig.from_json_file(_a )
if shard_model:
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : int = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[str] = {'''weight_map''': {}, '''metadata''': {}}
snake_case_ : Any = 0
snake_case_ : Union[str, Any] = None
snake_case_ : List[str] = BloomConfig()
for j, file in enumerate(_a ):
print('''Processing file: {}'''.format(_a ) )
snake_case_ : Dict = None
for i in range(_a ):
# load all TP files
snake_case_ : Union[str, Any] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : List[str] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : Any = temp.pop(_a )
if tensors is None:
snake_case_ : Any = temp
else:
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : List[str] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Any = tensors[key] / pretraining_tp
torch.save(
_a , os.path.join(
_a , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
snake_case_ : List[str] = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
snake_case_ : List[str] = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) )
snake_case_ : int = BloomConfig()
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Dict = total_size
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(_a , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f:
snake_case_ : Tuple = json.dumps(_a , indent=2 , sort_keys=_a ) + '''\n'''
f.write(_a )
else:
snake_case_ : Union[str, Any] = BloomModel(_a )
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : Dict = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[Any] = None
for i, file in enumerate(_a ):
snake_case_ : Optional[Any] = None
for i in range(_a ):
# load all TP files
snake_case_ : List[str] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : Optional[Any] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : str = temp.pop(_a )
if tensors is None:
snake_case_ : int = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : Optional[Any] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Union[str, Any] = tensors[key] / pretraining_tp
snake_case_ : Any = model.load_state_dict(_a , strict=_a )
assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
if missing_keys is None:
snake_case_ : Optional[int] = set(other_keys.missing_keys )
else:
snake_case_ : Tuple = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f"The keys {missing_keys} are missing"
# Save pytorch-model
os.makedirs(_a , exist_ok=_a )
snake_case_ : List[str] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}" )
if config.torch_dtype is not None:
snake_case_ : Optional[Any] = model.to(config.torch_dtype )
torch.save(model.state_dict() , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
lowercase__ : List[Any] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 264 | 0 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , _a , _a , _a )
move_disk(_a , _a )
move_tower(height - 1 , _a , _a , _a )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
print("""moving disk from""" , _a , """to""" , _a )
def snake_case_ ( )-> Dict:
'''simple docstring'''
_UpperCAmelCase : List[Any] = int(input("""Height of hanoi: """ ).strip() )
move_tower(_a , """A""" , """B""" , """C""" )
if __name__ == "__main__":
main()
| 215 |
"""simple docstring"""
def __lowercase ( _a , _a , _a=False ):
if isinstance(_a , _a ) and isinstance(_a , _a ):
snake_case_ : Union[str, Any] = len(set_a.intersection(_a ) )
if alternative_union:
snake_case_ : Any = len(_a ) + len(_a )
else:
snake_case_ : str = len(set_a.union(_a ) )
return intersection / union
if isinstance(_a , (list, tuple) ) and isinstance(_a , (list, tuple) ):
snake_case_ : str = [element for element in set_a if element in set_b]
if alternative_union:
snake_case_ : Tuple = len(_a ) + len(_a )
return len(_a ) / union
else:
snake_case_ : List[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_a ) / len(_a )
return len(_a ) / len(_a )
return None
if __name__ == "__main__":
lowercase__ : Any = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowercase__ : Optional[Any] = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 264 | 0 |
'''simple docstring'''
import qiskit
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
_lowerCAmelCase = qiskit.QuantumCircuit(_a , _a )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
_lowerCAmelCase = qiskit.execute(_a , _a , shots=10_00 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(_a )
if __name__ == "__main__":
print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")
| 70 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowercase__ : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __lowercase ( ):
snake_case_ : Optional[Any] = Github(os.environ['''GITHUB_TOKEN'''] )
snake_case_ : Any = g.get_repo('''huggingface/diffusers''' )
snake_case_ : Any = repo.get_issues(state='''open''' )
for issue in open_issues:
snake_case_ : str = sorted(issue.get_comments() , key=lambda _a : i.created_at , reverse=_a )
snake_case_ : Dict = comments[0] if len(_a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 264 | 0 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __a ( ):
UpperCAmelCase_ : Optional[Any] = Github(os.environ["GITHUB_TOKEN"] )
UpperCAmelCase_ : Any = g.get_repo("huggingface/diffusers" )
UpperCAmelCase_ : Any = repo.get_issues(state="open" )
for issue in open_issues:
UpperCAmelCase_ : str = sorted(issue.get_comments(), key=lambda __lowerCamelCase : i.created_at, reverse=_a )
UpperCAmelCase_ : Dict = comments[0] if len(_a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="closed" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="open" )
issue.remove_from_labels("stale" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
issue.add_to_labels("stale" )
if __name__ == "__main__":
main()
| 61 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowercase ( _a , _a ):
# Load checkpoint
snake_case_ : Optional[Any] = torch.load(_a , map_location='''cpu''' )
snake_case_ : Union[str, Any] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
snake_case_ : Dict = {}
for k, v in state_dict.items():
if "pred_layer" in k:
snake_case_ : Union[str, Any] = v
else:
snake_case_ : Dict = v
snake_case_ : Union[str, Any] = chkpt['''params''']
snake_case_ : int = {n: v for n, v in config.items() if not isinstance(_a , (torch.FloatTensor, numpy.ndarray) )}
snake_case_ : int = chkpt['''dico_word2id''']
snake_case_ : str = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(f"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(_a , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
print(f"Save vocab file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
if __name__ == "__main__":
lowercase__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowercase__ : List[str] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 264 | 0 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class A__(lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self ) -> Optional[Any]:
a_ : str = []
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> int:
self.events.append("""on_init_end""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> Tuple:
self.events.append("""on_train_begin""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> Union[str, Any]:
self.events.append("""on_train_end""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> Optional[Any]:
self.events.append("""on_epoch_begin""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> List[Any]:
self.events.append("""on_epoch_end""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> int:
self.events.append("""on_step_begin""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> Any:
self.events.append("""on_step_end""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> str:
self.events.append("""on_evaluate""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> Optional[Any]:
self.events.append("""on_predict""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> Any:
self.events.append("""on_save""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> Optional[Any]:
self.events.append("""on_log""" )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> Any:
self.events.append("""on_prediction_step""" )
@require_torch
class A__(unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Tuple:
a_ : Tuple = tempfile.mkdtemp()
def UpperCamelCase__ ( self ) -> List[Any]:
shutil.rmtree(self.output_dir )
def UpperCamelCase__ ( self , _lowercase=0 , _lowercase=0 , _lowercase=64 , _lowercase=64 , _lowercase=None , _lowercase=False , **_lowercase ) -> List[Any]:
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
a_ : int = RegressionDataset(length=lowercase_ )
a_ : Any = RegressionDataset(length=lowercase_ )
a_ : int = RegressionModelConfig(a=lowercase_ , b=lowercase_ )
a_ : Tuple = RegressionPreTrainedModel(lowercase_ )
a_ : Any = TrainingArguments(self.output_dir , disable_tqdm=lowercase_ , report_to=[] , **lowercase_ )
return Trainer(
lowercase_ , lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , callbacks=lowercase_ , )
def UpperCamelCase__ ( self , _lowercase , _lowercase ) -> Any:
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
# Order doesn't matter
a_ : Any = sorted(lowercase_ , key=lambda _lowercase : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
a_ : List[str] = sorted(lowercase_ , key=lambda _lowercase : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
for cba, cba in zip(lowercase_ , lowercase_ ):
if isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , lowercase_ )
elif isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , cba.__class__ )
elif not isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(cba.__class__ , lowercase_ )
else:
self.assertEqual(lowercase_ , lowercase_ )
def UpperCamelCase__ ( self , _lowercase ) -> Optional[int]:
a_ : Tuple = ['''on_init_end''', '''on_train_begin''']
a_ : List[Any] = 0
a_ : Union[str, Any] = len(trainer.get_eval_dataloader() )
a_ : List[Any] = ['''on_prediction_step'''] * len(trainer.get_eval_dataloader() ) + ['''on_log''', '''on_evaluate''']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("""on_epoch_begin""" )
for _ in range(lowercase_ ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("""on_log""" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("""on_save""" )
expected_events.append("""on_epoch_end""" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def UpperCamelCase__ ( self ) -> Optional[Any]:
a_ : Union[str, Any] = self.get_trainer()
a_ : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# Callbacks passed at init are added to the default callbacks
a_ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
a_ : Optional[int] = self.get_trainer(disable_tqdm=lowercase_ )
a_ : List[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def UpperCamelCase__ ( self ) -> Any:
a_ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
a_ : List[Any] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
a_ : Dict = self.get_trainer()
a_ : Optional[int] = trainer.pop_callback(lowercase_ )
self.assertEqual(cb.__class__ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# We can also add, pop, or remove by instance
a_ : Optional[int] = self.get_trainer()
a_ : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
a_ : List[Any] = self.get_trainer()
a_ : Optional[int] = trainer.callback_handler.callbacks[0]
a_ : Optional[Any] = trainer.pop_callback(lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def UpperCamelCase__ ( self ) -> List[Any]:
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="""ignore""" , category=lowercase_ )
a_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
a_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# Independent log/save/eval
a_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
a_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
a_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
a_ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
a_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="""steps""" )
trainer.train()
a_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
a_ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="""epoch""" )
trainer.train()
a_ : Dict = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# A bit of everything
a_ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="""steps""" , )
trainer.train()
a_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# warning should be emitted for duplicated callbacks
with patch("""transformers.trainer_callback.logger.warning""" ) as warn_mock:
a_ : Dict = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(lowercase_ ) in warn_mock.call_args[0][0]
| 248 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 264 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__ : Dict = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowercase_ ( lowerCAmelCase__ ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = """ctrl"""
UpperCAmelCase_ : List[Any] = ["""past_key_values"""]
UpperCAmelCase_ : Optional[Any] = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __SCREAMING_SNAKE_CASE=246534 , __SCREAMING_SNAKE_CASE=256 , __SCREAMING_SNAKE_CASE=1280 , __SCREAMING_SNAKE_CASE=8192 , __SCREAMING_SNAKE_CASE=48 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=1e-6 , __SCREAMING_SNAKE_CASE=0.0_2 , __SCREAMING_SNAKE_CASE=True , **__SCREAMING_SNAKE_CASE , ) ->Dict:
lowerCAmelCase = vocab_size
lowerCAmelCase = n_positions
lowerCAmelCase = n_embd
lowerCAmelCase = n_layer
lowerCAmelCase = n_head
lowerCAmelCase = dff
lowerCAmelCase = resid_pdrop
lowerCAmelCase = embd_pdrop
lowerCAmelCase = layer_norm_epsilon
lowerCAmelCase = initializer_range
lowerCAmelCase = use_cache
super().__init__(**lowercase_ )
| 338 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __lowercase ( _a="" ):
snake_case_ : List[str] = tempfile.mkdtemp()
return os.path.join(_a , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : str ):
snake_case_ : int = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : Optional[int] = AgentAudio(lowercase_ )
snake_case_ : List[str] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowercase_ ) )
# Ensure that the file contains the same value as the original tensor
snake_case_, snake_case_ : int = sf.read(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , torch.tensor(lowercase_ ) , atol=1E-4 ) )
def _snake_case ( self : Optional[int] ):
snake_case_ : Any = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : List[str] = get_new_path(suffix='''.wav''' )
sf.write(lowercase_ , lowercase_ , 16000 )
snake_case_ : Tuple = AgentAudio(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
self.assertEqual(agent_type.to_string() , lowercase_ )
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Tuple ):
snake_case_ : List[Any] = torch.randint(0 , 256 , (64, 64, 3) )
snake_case_ : str = AgentImage(lowercase_ )
snake_case_ : Union[str, Any] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type._tensor , atol=1E-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : Any = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Optional[int] = Image.open(lowercase_ )
snake_case_ : Tuple = AgentImage(lowercase_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : int = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Dict = Image.open(lowercase_ )
snake_case_ : List[str] = AgentImage(lowercase_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Any ):
snake_case_ : Tuple = '''Hey!'''
snake_case_ : Optional[Any] = AgentText(lowercase_ )
self.assertEqual(lowercase_ , agent_type.to_string() )
self.assertEqual(lowercase_ , agent_type.to_raw() )
self.assertEqual(lowercase_ , lowercase_ )
| 264 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( lowerCAmelCase : List[str] ):
"""simple docstring"""
create_state_space_tree(_a , [] , 0 )
def lowerCamelCase ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Dict ):
"""simple docstring"""
if index == len(_a ):
print(_a )
return
create_state_space_tree(_a , _a , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(_a , _a , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
lowerCAmelCase :list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(['''A''', '''B''', '''C'''])
generate_all_subsequences(seq) | 331 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : str = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
lowercase__ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[Any] = 10_00 ):
'''simple docstring'''
return sum(e for e in range(3 , _a ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 220 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : str = XLMRobertaTokenizer
_lowerCAmelCase : int = XLMRobertaTokenizerFast
_lowerCAmelCase : str = True
_lowerCAmelCase : Dict = True
def _snake_case ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ : List[str] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def _snake_case ( self : str ):
snake_case_ : List[Any] = '''<pad>'''
snake_case_ : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def _snake_case ( self : Union[str, Any] ):
snake_case_ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowercase_ ) , 1002 )
def _snake_case ( self : Union[str, Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def _snake_case ( self : Dict ):
snake_case_ : Optional[Any] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
snake_case_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowercase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
snake_case_ : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
snake_case_ : List[Any] = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _snake_case ( self : List[str] ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
snake_case_ : int = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : int = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : Tuple = tokenizer_r.save_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
snake_case_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : Union[str, Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[str] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : List[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[Any] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : Tuple = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
snake_case_ : Optional[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : Dict = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@cached_property
def _snake_case ( self : List[str] ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _snake_case ( self : Optional[Any] ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowercase_ , f.name )
snake_case_ : Any = XLMRobertaTokenizer(f.name , keep_accents=lowercase_ )
snake_case_ : List[Any] = pickle.dumps(lowercase_ )
pickle.loads(lowercase_ )
def _snake_case ( self : Tuple ):
if not self.test_rust_tokenizer:
return
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ : Optional[int] = self.get_rust_tokenizer()
snake_case_ : Dict = '''I was born in 92000, and this is falsé.'''
snake_case_ : Optional[int] = tokenizer.tokenize(lowercase_ )
snake_case_ : Tuple = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : List[str] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
snake_case_ : str = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ : Any = tokenizer.encode(lowercase_ )
snake_case_ : int = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
@slow
def _snake_case ( self : Tuple ):
snake_case_ : int = '''Hello World!'''
snake_case_ : int = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : List[Any] ):
snake_case_ : Any = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
snake_case_ : Optional[int] = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : Dict ):
# fmt: off
snake_case_ : int = {'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 264 | 0 |
from __future__ import annotations
import os
from collections.abc import Mapping
_UpperCAmelCase : Optional[int] =tuple[int, int]
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase , __lowercase ) -> str:
lowerCAmelCase_ : set[int] = vertices
lowerCAmelCase_ : dict[EdgeT, int] = {
(min(lowercase_ ), max(lowercase_ )): weight for edge, weight in edges.items()
}
def lowercase_ ( self , __lowercase , __lowercase ) -> List[Any]:
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
lowerCAmelCase_ : Tuple = weight
def lowercase_ ( self ) -> Optional[Any]:
lowerCAmelCase_ : Graph = Graph({min(self.vertices )} , {} )
lowerCAmelCase_ : EdgeT
lowerCAmelCase_ : int
lowerCAmelCase_ : EdgeT
lowerCAmelCase_ : int
while len(subgraph.vertices ) < len(self.vertices ):
lowerCAmelCase_ : Tuple = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
lowerCAmelCase_ : Dict = edge
lowerCAmelCase_ : Union[str, Any] = weight
subgraph.add_edge(lowercase_ , lowercase_ )
return subgraph
def lowerCAmelCase ( lowerCAmelCase_ = "p107_network.txt" )-> Tuple:
lowerCAmelCase_ : str = os.path.abspath(os.path.dirname(_a ) )
lowerCAmelCase_ : str = os.path.join(_a , _a )
lowerCAmelCase_ : dict[EdgeT, int] = {}
lowerCAmelCase_ : list[str]
lowerCAmelCase_ : int
lowerCAmelCase_ : int
with open(_a ) as f:
lowerCAmelCase_ : List[str] = f.read().strip().split('''\n''' )
lowerCAmelCase_ : List[str] = [line.split(''',''' ) for line in data]
for edgea in range(1 , len(_a ) ):
for edgea in range(_a ):
if adjaceny_matrix[edgea][edgea] != "-":
lowerCAmelCase_ : str = int(adjaceny_matrix[edgea][edgea] )
lowerCAmelCase_ : Graph = Graph(set(range(len(_a ) ) ) , _a )
lowerCAmelCase_ : Graph = graph.prims_algorithm()
lowerCAmelCase_ : int = sum(graph.edges.values() )
lowerCAmelCase_ : int = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f"""{solution() = }""") | 262 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[Any] = """gpt_neox"""
def __init__( self : List[str] , lowercase_ : str=50432 , lowercase_ : List[Any]=6144 , lowercase_ : List[Any]=44 , lowercase_ : Union[str, Any]=64 , lowercase_ : List[str]=24576 , lowercase_ : List[Any]="gelu" , lowercase_ : str=0.25 , lowercase_ : Optional[int]=10000 , lowercase_ : Optional[int]=0.0 , lowercase_ : Optional[int]=0.0 , lowercase_ : int=0.1 , lowercase_ : Tuple=2048 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : List[str]=1E-5 , lowercase_ : str=True , lowercase_ : str=0 , lowercase_ : Union[str, Any]=2 , lowercase_ : List[str]=False , lowercase_ : Optional[int]=True , lowercase_ : List[Any]=None , **lowercase_ : Optional[int] , ):
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : Dict = num_attention_heads
snake_case_ : List[Any] = intermediate_size
snake_case_ : List[Any] = hidden_act
snake_case_ : str = rotary_pct
snake_case_ : Dict = rotary_emb_base
snake_case_ : Optional[int] = attention_dropout
snake_case_ : Tuple = hidden_dropout
snake_case_ : Tuple = classifier_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Any = use_cache
snake_case_ : Optional[int] = tie_word_embeddings
snake_case_ : Any = use_parallel_residual
snake_case_ : Union[str, Any] = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def _snake_case ( self : Optional[int] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , lowercase_ ) 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}" )
snake_case_ : Any = self.rope_scaling.get('''type''' , lowercase_ )
snake_case_ : Union[str, Any] = self.rope_scaling.get('''factor''' , lowercase_ )
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(lowercase_ , lowercase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 264 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase__ ):
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[Any] = tempfile.mkdtemp()
UpperCAmelCase : Tuple = 8
# DPR tok
UpperCAmelCase : Tuple = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : Tuple = os.path.join(self.tmpdirname , """dpr_tokenizer""" )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
UpperCAmelCase : List[str] = os.path.join(lowercase_ , DPR_VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
# BART tok
UpperCAmelCase : Tuple = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
UpperCAmelCase : Union[str, Any] = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
UpperCAmelCase : Union[str, Any] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
UpperCAmelCase : List[str] = {'''unk_token''': '''<unk>'''}
UpperCAmelCase : Any = os.path.join(self.tmpdirname , """bart_tokenizer""" )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
UpperCAmelCase : Optional[int] = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase : int = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowercase_ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowercase_ ) )
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , """bart_tokenizer""" ) )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Optional[int] = os.path.join(self.tmpdirname , """rag_tokenizer""" )
UpperCAmelCase : List[str] = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
UpperCAmelCase : Tuple = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(lowercase_ )
rag_tokenizer.save_pretrained(lowercase_ )
UpperCAmelCase : Optional[Any] = RagTokenizer.from_pretrained(lowercase_ , config=lowercase_ )
self.assertIsInstance(new_rag_tokenizer.question_encoder , lowercase_ )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , lowercase_ )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple = RagTokenizer.from_pretrained("""facebook/rag-token-nq""" )
UpperCAmelCase : List[str] = [
'''who got the first nobel prize in physics''',
'''when is the next deadpool movie being released''',
'''which mode is used for short wave broadcast service''',
'''who is the owner of reading football club''',
'''when is the next scandal episode coming out''',
'''when is the last time the philadelphia won the superbowl''',
'''what is the most current adobe flash player version''',
'''how many episodes are there in dragon ball z''',
'''what is the first step in the evolution of the eye''',
'''where is gall bladder situated in human body''',
'''what is the main mineral in lithium batteries''',
'''who is the president of usa right now''',
'''where do the greasers live in the outsiders''',
'''panda is a national animal of which country''',
'''what is the name of manchester united stadium''',
]
UpperCAmelCase : Optional[Any] = tokenizer(lowercase_ )
self.assertIsNotNone(lowercase_ )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int = RagTokenizer.from_pretrained("""facebook/rag-sequence-nq""" )
UpperCAmelCase : Any = [
'''who got the first nobel prize in physics''',
'''when is the next deadpool movie being released''',
'''which mode is used for short wave broadcast service''',
'''who is the owner of reading football club''',
'''when is the next scandal episode coming out''',
'''when is the last time the philadelphia won the superbowl''',
'''what is the most current adobe flash player version''',
'''how many episodes are there in dragon ball z''',
'''what is the first step in the evolution of the eye''',
'''where is gall bladder situated in human body''',
'''what is the main mineral in lithium batteries''',
'''who is the president of usa right now''',
'''where do the greasers live in the outsiders''',
'''panda is a national animal of which country''',
'''what is the name of manchester united stadium''',
]
UpperCAmelCase : Optional[int] = tokenizer(lowercase_ )
self.assertIsNotNone(lowercase_ )
| 109 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowercase__ : int = None
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : List[str] = '''▁'''
lowercase__ : Optional[int] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
lowercase__ : str = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
'''tokenizer_file''': {
'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
},
}
lowercase__ : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
_lowerCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Tuple = PegasusTokenizer
_lowerCAmelCase : str = ["""input_ids""", """attention_mask"""]
def __init__( self : Any , lowercase_ : Optional[Any]=None , lowercase_ : int=None , lowercase_ : Tuple="<pad>" , lowercase_ : int="</s>" , lowercase_ : Tuple="<unk>" , lowercase_ : str="<mask_2>" , lowercase_ : Optional[Any]="<mask_1>" , lowercase_ : str=None , lowercase_ : List[str]=103 , **lowercase_ : List[Any] , ):
snake_case_ : Dict = offset
if additional_special_tokens is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError(
f"additional_special_tokens should be of type {type(lowercase_ )}, but is"
f" {type(lowercase_ )}" )
snake_case_ : str = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f"<unk_{i}>" for i in range(len(lowercase_ ) , self.offset - 1 )
]
if len(set(lowercase_ ) ) != len(lowercase_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}." )
snake_case_ : Union[str, Any] = additional_special_tokens_extended
else:
snake_case_ : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f"<unk_{i}>" for i in range(2 , self.offset )]
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , pad_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , mask_token=lowercase_ , mask_token_sent=lowercase_ , offset=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
snake_case_ : List[Any] = vocab_file
snake_case_ : List[Any] = False if not self.vocab_file else True
def _snake_case ( self : str , lowercase_ : Union[str, Any] ):
snake_case_ : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
f" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}" )
return [1 if x in all_special_ids else 0 for x in seq]
def _snake_case ( self : int , lowercase_ : List , lowercase_ : Optional[List] = None , lowercase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(lowercase_ )
elif token_ids_a is None:
return self._special_token_mask(lowercase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _snake_case ( self : List[Any] , lowercase_ : Optional[int] , lowercase_ : str=None ):
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 _snake_case ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ : Dict = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
return (out_vocab_file,)
| 264 | 0 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCAmelCase__ : Optional[int] = {
# 1536-bit
5: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 2048-bit
14: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 3072-bit
15: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 4096-bit
16: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
+ '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
+ '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
+ '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
+ '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
+ '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
+ 'FFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 6144-bit
17: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
+ '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
+ '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
+ 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
+ '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
+ 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
+ '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
+ '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
+ '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
+ 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
+ '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
+ 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
+ '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
+ '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
+ '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
+ 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
+ '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
+ 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
+ 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
+ '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
+ 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
+ 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
+ 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
+ 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
+ '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
+ '6DCC4024FFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
# 8192-bit
18: {
'''prime''': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
+ '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
+ '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
+ '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
+ '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
+ '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
+ '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
+ 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
+ '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
+ 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
+ '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
+ 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
+ '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
+ 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
+ '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
+ 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
+ '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
+ '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
+ '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
+ '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
+ '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
+ '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
+ '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
+ '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
+ 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
+ '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
+ '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
+ '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
base=16,
),
'''generator''': 2,
},
}
class snake_case :
"""simple docstring"""
def __init__( self : str ,lowerCamelCase__ : int = 14 ):
if group not in primes:
raise ValueError('Unsupported Group' )
UpperCAmelCase__ = primes[group]['''prime''']
UpperCAmelCase__ = primes[group]['''generator''']
UpperCAmelCase__ = int(hexlify(urandom(32 ) ) ,base=16 )
def __lowerCAmelCase ( self : Optional[Any] ):
return hex(self.__private_key )[2:]
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = pow(self.generator ,self.__private_key ,self.prime )
return hex(lowercase_ )[2:]
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(lowercase_ ,(self.prime - 1) // 2 ,self.prime ) == 1
)
def __lowerCAmelCase ( self : int ,lowerCamelCase__ : str ):
UpperCAmelCase__ = int(lowercase_ ,base=16 )
if not self.is_valid_public_key(lowercase_ ):
raise ValueError('Invalid public key' )
UpperCAmelCase__ = pow(lowercase_ ,self.__private_key ,self.prime )
return shaaaa(str(lowercase_ ).encode() ).hexdigest()
@staticmethod
def __lowerCAmelCase ( lowerCamelCase__ : int ,lowerCamelCase__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(lowercase_ ,(prime - 1) // 2 ,lowercase_ ) == 1
)
@staticmethod
def __lowerCAmelCase ( lowerCamelCase__ : str ,lowerCamelCase__ : str ,lowerCamelCase__ : int = 14 ):
UpperCAmelCase__ = int(lowercase_ ,base=16 )
UpperCAmelCase__ = int(lowercase_ ,base=16 )
UpperCAmelCase__ = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(lowercase_ ,lowercase_ ):
raise ValueError('Invalid public key' )
UpperCAmelCase__ = pow(lowercase_ ,lowercase_ ,lowercase_ )
return shaaaa(str(lowercase_ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 98 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int=13 , lowercase_ : Optional[int]=7 , lowercase_ : Any=True , lowercase_ : Dict=True , lowercase_ : Dict=True , lowercase_ : Optional[Any]=99 , lowercase_ : Union[str, Any]=32 , lowercase_ : str=5 , lowercase_ : Union[str, Any]=4 , lowercase_ : Any=37 , lowercase_ : Tuple="gelu" , lowercase_ : Dict=0.1 , lowercase_ : Tuple=0.1 , lowercase_ : Optional[int]=512 , lowercase_ : Optional[Any]=16 , lowercase_ : Optional[Any]=2 , lowercase_ : Optional[Any]=0.02 , lowercase_ : List[Any]=3 , lowercase_ : Union[str, Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ : Any = parent
snake_case_ : List[str] = batch_size
snake_case_ : List[Any] = seq_length
snake_case_ : Optional[int] = is_training
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Optional[Any] = use_labels
snake_case_ : Union[str, Any] = vocab_size
snake_case_ : Any = hidden_size
snake_case_ : List[Any] = num_hidden_layers
snake_case_ : Any = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : Tuple = max_position_embeddings
snake_case_ : int = type_vocab_size
snake_case_ : Tuple = type_sequence_label_size
snake_case_ : str = initializer_range
snake_case_ : Tuple = num_labels
snake_case_ : str = num_choices
snake_case_ : Any = scope
snake_case_ : Dict = self.vocab_size - 1
def _snake_case ( self : int ):
snake_case_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Optional[Any] = None
if self.use_token_type_ids:
snake_case_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : str = None
snake_case_ : Dict = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ : int = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
snake_case_ : Any = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _snake_case ( self : Tuple , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , *lowercase_ : Dict ):
snake_case_ : List[Any] = OpenAIGPTModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Any = model(lowercase_ , token_type_ids=lowercase_ , head_mask=lowercase_ )
snake_case_ : Optional[Any] = model(lowercase_ , token_type_ids=lowercase_ )
snake_case_ : Optional[Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : List[Any] , *lowercase_ : Optional[Any] ):
snake_case_ : Union[str, Any] = OpenAIGPTLMHeadModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Union[str, Any] = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : List[str] , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Dict , *lowercase_ : Union[str, Any] ):
snake_case_ : Tuple = OpenAIGPTDoubleHeadsModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Dict = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : Any , lowercase_ : str , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , *lowercase_ : Any ):
snake_case_ : int = self.num_labels
snake_case_ : Any = OpenAIGPTForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Optional[Any] = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : int ):
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : str = config_and_inputs
snake_case_ : str = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : Dict = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_lowerCAmelCase : int = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_lowerCAmelCase : Union[str, Any] = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _snake_case ( self : Tuple , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _snake_case ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str]=False ):
snake_case_ : Dict = super()._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowercase_ , )
snake_case_ : int = inputs_dict['''labels''']
snake_case_ : Optional[Any] = inputs_dict['''labels''']
snake_case_ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowercase_ , )
snake_case_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase_ )
return inputs_dict
def _snake_case ( self : Any ):
snake_case_ : List[str] = OpenAIGPTModelTester(self )
snake_case_ : Dict = ConfigTester(self , config_class=lowercase_ , n_embd=37 )
def _snake_case ( self : List[str] ):
self.config_tester.run_common_tests()
def _snake_case ( self : Optional[Any] ):
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowercase_ )
def _snake_case ( self : List[str] ):
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowercase_ )
def _snake_case ( self : int ):
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowercase_ )
def _snake_case ( self : List[str] ):
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_ )
@slow
def _snake_case ( self : Dict ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Optional[Any] = OpenAIGPTModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
@slow
def _snake_case ( self : Optional[int] ):
snake_case_ : Optional[Any] = OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
model.to(lowercase_ )
snake_case_ : List[str] = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=lowercase_ ) # the president is
snake_case_ : List[Any] = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
snake_case_ : Optional[Any] = model.generate(lowercase_ , do_sample=lowercase_ )
self.assertListEqual(output_ids[0].tolist() , lowercase_ )
| 264 | 0 |
import argparse
import os
import re
import packaging.version
a_ = '''examples/'''
a_ = {
'''examples''': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'''\1version="VERSION",'''),
'''doc''': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), '''release = "VERSION"\n'''),
}
a_ = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
a_ = '''README.md'''
def __lowercase ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Union[str, Any] ):
with open(_a , 'r' , encoding='utf-8' , newline='\n' ) as f:
UpperCamelCase_ : Union[str, Any] = f.read()
UpperCamelCase_ : List[Any] = REPLACE_PATTERNS[pattern]
UpperCamelCase_ : Tuple = replace.replace('VERSION' , _a )
UpperCamelCase_ : List[Any] = re_pattern.sub(_a , _a )
with open(_a , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(_a )
def __lowercase ( lowerCamelCase : List[str] ):
for folder, directories, fnames in os.walk(_a ):
# 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(_a , _a ) , _a , pattern='examples' )
def __lowercase ( lowerCamelCase : Union[str, Any] , lowerCamelCase : List[str]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_a , _a , _a )
if not patch:
update_version_in_examples(_a )
def __lowercase ( ):
UpperCamelCase_ : Optional[Any] = '''🤗 Transformers currently provides the following architectures'''
UpperCamelCase_ : int = '''1. Want to contribute a new model?'''
with open(_a , 'r' , encoding='utf-8' , newline='\n' ) as f:
UpperCamelCase_ : List[Any] = f.readlines()
# Find the start of the list.
UpperCamelCase_ : Optional[Any] = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
UpperCamelCase_ : Union[str, Any] = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
UpperCamelCase_ : Optional[int] = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(_a , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(_a )
def __lowercase ( ):
with open(REPLACE_FILES['init'] , 'r' ) as f:
UpperCamelCase_ : Dict = f.read()
UpperCamelCase_ : List[Any] = REPLACE_PATTERNS['''init'''][0].search(_a ).groups()[0]
return packaging.version.parse(_a )
def __lowercase ( lowerCamelCase : int=False ):
UpperCamelCase_ : Optional[int] = 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:
UpperCamelCase_ : Dict = default_version.base_version
elif patch:
UpperCamelCase_ : Optional[Any] = F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
else:
UpperCamelCase_ : Dict = F"{default_version.major}.{default_version.minor + 1}.0"
# Now let's ask nicely if that's the right one.
UpperCamelCase_ : Tuple = input(F"Which version are you releasing? [{default_version}]" )
if len(_a ) == 0:
UpperCamelCase_ : Tuple = default_version
print(F"Updating version to {version}." )
global_version_update(_a , patch=_a )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def __lowercase ( ):
UpperCamelCase_ : Union[str, Any] = get_version()
UpperCamelCase_ : Optional[Any] = F"{current_version.major}.{current_version.minor + 1}.0.dev0"
UpperCamelCase_ : str = current_version.base_version
# Check with the user we got that right.
UpperCamelCase_ : Optional[Any] = input(F"Which version are we developing now? [{dev_version}]" )
if len(_a ) == 0:
UpperCamelCase_ : Union[str, Any] = dev_version
print(F"Updating version to {version}." )
global_version_update(_a )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
a_ = 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()
| 175 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Any , lowercase_ : TransformeraDModel , lowercase_ : AutoencoderKL , lowercase_ : KarrasDiffusionSchedulers , lowercase_ : Optional[Dict[int, str]] = None , ):
super().__init__()
self.register_modules(transformer=lowercase_ , vae=lowercase_ , scheduler=lowercase_ )
# create a imagenet -> id dictionary for easier use
snake_case_ : Tuple = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''',''' ):
snake_case_ : str = int(lowercase_ )
snake_case_ : Any = dict(sorted(self.labels.items() ) )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, List[str]] ):
if not isinstance(lowercase_ , lowercase_ ):
snake_case_ : Tuple = list(lowercase_ )
for l in label:
if l not in self.labels:
raise ValueError(
f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}." )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self : Optional[int] , lowercase_ : List[int] , lowercase_ : float = 4.0 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : int = 50 , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , ):
snake_case_ : Any = len(lowercase_ )
snake_case_ : List[str] = self.transformer.config.sample_size
snake_case_ : Union[str, Any] = self.transformer.config.in_channels
snake_case_ : str = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowercase_ , device=self.device , dtype=self.transformer.dtype , )
snake_case_ : Optional[Any] = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
snake_case_ : Optional[int] = torch.tensor(lowercase_ , device=self.device ).reshape(-1 )
snake_case_ : Dict = torch.tensor([1000] * batch_size , device=self.device )
snake_case_ : Tuple = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
snake_case_ : List[Any] = latent_model_input[: len(lowercase_ ) // 2]
snake_case_ : Union[str, Any] = torch.cat([half, half] , dim=0 )
snake_case_ : Optional[Any] = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
snake_case_ : int = t
if not torch.is_tensor(lowercase_ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
snake_case_ : Tuple = latent_model_input.device.type == '''mps'''
if isinstance(lowercase_ , lowercase_ ):
snake_case_ : List[str] = torch.floataa if is_mps else torch.floataa
else:
snake_case_ : Optional[int] = torch.intaa if is_mps else torch.intaa
snake_case_ : List[Any] = torch.tensor([timesteps] , dtype=lowercase_ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
snake_case_ : str = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
snake_case_ : Tuple = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
snake_case_ : List[Any] = self.transformer(
lowercase_ , timestep=lowercase_ , class_labels=lowercase_ ).sample
# perform guidance
if guidance_scale > 1:
snake_case_, snake_case_ : Dict = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
snake_case_, snake_case_ : Any = torch.split(lowercase_ , len(lowercase_ ) // 2 , dim=0 )
snake_case_ : int = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
snake_case_ : str = torch.cat([half_eps, half_eps] , dim=0 )
snake_case_ : List[Any] = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
snake_case_, snake_case_ : Optional[Any] = torch.split(lowercase_ , lowercase_ , dim=1 )
else:
snake_case_ : List[str] = noise_pred
# compute previous image: x_t -> x_t-1
snake_case_ : int = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
if guidance_scale > 1:
snake_case_, snake_case_ : Optional[Any] = latent_model_input.chunk(2 , dim=0 )
else:
snake_case_ : Dict = latent_model_input
snake_case_ : Union[str, Any] = 1 / self.vae.config.scaling_factor * latents
snake_case_ : Tuple = self.vae.decode(lowercase_ ).sample
snake_case_ : str = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ : Union[str, Any] = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case_ : Union[str, Any] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowercase_ )
| 264 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def snake_case_ ( )-> Dict:
'''simple docstring'''
_UpperCAmelCase : List[str] = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
_UpperCAmelCase : int = Dataset.from_dict(_a )
return dataset
class lowercase ( lowerCAmelCase__ ):
"""simple docstring"""
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = get_dataset()
_UpperCAmelCase : List[Any] = make_duplicate_clusters(lowercase_ ,0.85 )
self.assertEqual(len(duplicate_clusters[0] ) ,2 )
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = get_dataset()
_UpperCAmelCase : List[str] = deduplicate_dataset(lowercase_ )
self.assertEqual(len(lowercase_ ) ,2 )
print(lowercase_ )
self.assertEqual(duplicate_clusters[0][0]["""copies"""] ,2 )
self.assertEqual(duplicate_clusters[0][0]["""is_extreme"""] ,lowercase_ )
| 215 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self : List[Any] ):
snake_case_ : List[str] = ''''''
snake_case_ : Tuple = ''''''
snake_case_ : int = []
snake_case_ : Optional[int] = 0
snake_case_ : Optional[Any] = 256
snake_case_ : Tuple = 0
snake_case_ : Tuple = 0
snake_case_ : Optional[Any] = 0
snake_case_ : Any = 0
def _snake_case ( self : Optional[Any] , lowercase_ : List[Any] ):
snake_case_ : List[Any] = cva.imread(lowercase_ , 0 )
snake_case_ : Tuple = copy.deepcopy(self.img )
snake_case_, snake_case_, snake_case_ : List[Any] = plt.hist(self.img.ravel() , 256 , [0, 256] , label='''x''' )
snake_case_ : str = np.sum(lowercase_ )
for i in range(len(lowercase_ ) ):
snake_case_ : Optional[Any] = x[i] / self.k
self.sk += prk
snake_case_ : Any = (self.L - 1) * self.sk
if self.rem != 0:
snake_case_ : Dict = int(last % last )
snake_case_ : Union[str, Any] = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowercase_ )
snake_case_ : int = int(np.ma.count(self.img ) / self.img[1].size )
snake_case_ : Tuple = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
snake_case_ : Union[str, Any] = self.img[j][i]
if num != self.last_list[num]:
snake_case_ : List[str] = self.last_list[num]
cva.imwrite('''output_data/output.jpg''' , self.img )
def _snake_case ( self : Tuple ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _snake_case ( self : int ):
cva.imshow('''Output-Image''' , self.img )
cva.imshow('''Input-Image''' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowercase__ : Any = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
lowercase__ : Any = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 264 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ : List[Any] ={
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any =['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : int =[
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict =[
'''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDistilBertForMaskedLM''',
'''TFDistilBertForMultipleChoice''',
'''TFDistilBertForQuestionAnswering''',
'''TFDistilBertForSequenceClassification''',
'''TFDistilBertForTokenClassification''',
'''TFDistilBertMainLayer''',
'''TFDistilBertModel''',
'''TFDistilBertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple =[
'''FlaxDistilBertForMaskedLM''',
'''FlaxDistilBertForMultipleChoice''',
'''FlaxDistilBertForQuestionAnswering''',
'''FlaxDistilBertForSequenceClassification''',
'''FlaxDistilBertForTokenClassification''',
'''FlaxDistilBertModel''',
'''FlaxDistilBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
A__ : Union[str, Any] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 70 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] ):
snake_case_ : str = []
def _snake_case ( self : List[Any] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : List[str] , **lowercase_ : Tuple ):
self.events.append('''on_init_end''' )
def _snake_case ( self : List[Any] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_train_begin''' )
def _snake_case ( self : Any , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_train_end''' )
def _snake_case ( self : str , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any] , **lowercase_ : List[Any] ):
self.events.append('''on_epoch_begin''' )
def _snake_case ( self : Tuple , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ):
self.events.append('''on_epoch_end''' )
def _snake_case ( self : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , **lowercase_ : Optional[Any] ):
self.events.append('''on_step_begin''' )
def _snake_case ( self : int , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , **lowercase_ : List[str] ):
self.events.append('''on_step_end''' )
def _snake_case ( self : str , lowercase_ : int , lowercase_ : Dict , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_evaluate''' )
def _snake_case ( self : Dict , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : List[Any] , **lowercase_ : str ):
self.events.append('''on_predict''' )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int , **lowercase_ : Union[str, Any] ):
self.events.append('''on_save''' )
def _snake_case ( self : str , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : Any ):
self.events.append('''on_log''' )
def _snake_case ( self : Dict , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Union[str, Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_prediction_step''' )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : List[str] ):
snake_case_ : Tuple = tempfile.mkdtemp()
def _snake_case ( self : Tuple ):
shutil.rmtree(self.output_dir )
def _snake_case ( self : int , lowercase_ : Union[str, Any]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=64 , lowercase_ : Union[str, Any]=64 , lowercase_ : Union[str, Any]=None , lowercase_ : Any=False , **lowercase_ : List[Any] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
snake_case_ : int = RegressionDataset(length=lowercase_ )
snake_case_ : Any = RegressionDataset(length=lowercase_ )
snake_case_ : int = RegressionModelConfig(a=lowercase_ , b=lowercase_ )
snake_case_ : Tuple = RegressionPreTrainedModel(lowercase_ )
snake_case_ : Any = TrainingArguments(self.output_dir , disable_tqdm=lowercase_ , report_to=[] , **lowercase_ )
return Trainer(
lowercase_ , lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , callbacks=lowercase_ , )
def _snake_case ( self : Optional[int] , lowercase_ : Any , lowercase_ : List[Any] ):
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
# Order doesn't matter
snake_case_ : Any = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
snake_case_ : List[str] = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
for cba, cba in zip(lowercase_ , lowercase_ ):
if isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , lowercase_ )
elif isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , cba.__class__ )
elif not isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(cba.__class__ , lowercase_ )
else:
self.assertEqual(lowercase_ , lowercase_ )
def _snake_case ( self : Optional[Any] , lowercase_ : Tuple ):
snake_case_ : Tuple = ['''on_init_end''', '''on_train_begin''']
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = len(trainer.get_eval_dataloader() )
snake_case_ : List[Any] = ['''on_prediction_step'''] * len(trainer.get_eval_dataloader() ) + ['''on_log''', '''on_evaluate''']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('''on_epoch_begin''' )
for _ in range(lowercase_ ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('''on_log''' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('''on_save''' )
expected_events.append('''on_epoch_end''' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _snake_case ( self : List[str] ):
snake_case_ : Union[str, Any] = self.get_trainer()
snake_case_ : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# Callbacks passed at init are added to the default callbacks
snake_case_ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
snake_case_ : Optional[int] = self.get_trainer(disable_tqdm=lowercase_ )
snake_case_ : List[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : int ):
snake_case_ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
snake_case_ : List[Any] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : Dict = self.get_trainer()
snake_case_ : Optional[int] = trainer.pop_callback(lowercase_ )
self.assertEqual(cb.__class__ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# We can also add, pop, or remove by instance
snake_case_ : Optional[int] = self.get_trainer()
snake_case_ : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : List[Any] = self.get_trainer()
snake_case_ : Optional[int] = trainer.callback_handler.callbacks[0]
snake_case_ : Optional[Any] = trainer.pop_callback(lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : List[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='''ignore''' , category=lowercase_ )
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# Independent log/save/eval
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
snake_case_ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='''steps''' )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='''epoch''' )
trainer.train()
snake_case_ : Dict = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# A bit of everything
snake_case_ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy='''steps''' , )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# warning should be emitted for duplicated callbacks
with patch('''transformers.trainer_callback.logger.warning''' ) as warn_mock:
snake_case_ : Dict = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(lowercase_ ) in warn_mock.call_args[0][0]
| 264 | 0 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
_a = '''us-east-1''' # defaults region
@dataclass
class A_ :
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str
SCREAMING_SNAKE_CASE__ : List[Any] = """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
SCREAMING_SNAKE_CASE__ : str = {
"""task_name""": """mnli""",
"""per_device_train_batch_size""": 16,
"""per_device_eval_batch_size""": 16,
"""do_train""": True,
"""do_eval""": True,
"""do_predict""": True,
"""output_dir""": """/opt/ml/model""",
"""overwrite_output_dir""": True,
"""max_steps""": 500,
"""save_steps""": 5500,
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = {**hyperparameters, """max_steps""": 1000}
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return F"""{self.framework}-transfromers-test"""
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return F"""./tests/sagemaker/scripts/{self.framework}"""
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="class" )
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = SageMakerTestEnvironment(framework=request.cls.framework )
| 61 |
"""simple docstring"""
import numpy as np
def __lowercase ( _a ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Tuple = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class A__(lowerCAmelCase__ ):
"""simple docstring"""
_A : Optional[Any] = """vit_mae"""
def __init__( self , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.0_2 , _lowercase=1e-12 , _lowercase=224 , _lowercase=16 , _lowercase=3 , _lowercase=True , _lowercase=16 , _lowercase=512 , _lowercase=8 , _lowercase=2_048 , _lowercase=0.7_5 , _lowercase=False , **_lowercase , ) -> str:
super().__init__(**lowercase_ )
a_ : Tuple = hidden_size
a_ : str = num_hidden_layers
a_ : Tuple = num_attention_heads
a_ : int = intermediate_size
a_ : Dict = hidden_act
a_ : Optional[Any] = hidden_dropout_prob
a_ : List[Any] = attention_probs_dropout_prob
a_ : Union[str, Any] = initializer_range
a_ : Any = layer_norm_eps
a_ : List[Any] = image_size
a_ : str = patch_size
a_ : Optional[Any] = num_channels
a_ : List[str] = qkv_bias
a_ : Optional[int] = decoder_num_attention_heads
a_ : Union[str, Any] = decoder_hidden_size
a_ : Optional[Any] = decoder_num_hidden_layers
a_ : Union[str, Any] = decoder_intermediate_size
a_ : Optional[Any] = mask_ratio
a_ : Tuple = norm_pix_loss
| 248 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] , lowercase_ : str , lowercase_ : int ):
snake_case_ : Dict = params
snake_case_ : Union[str, Any] = np.array(lowercase_ )
snake_case_ : str = np.array([len(lowercase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : Dict , lowercase_ : Union[str, Any] ):
return (self.token_ids[index], self.lengths[index])
def __len__( self : List[Any] ):
return len(self.lengths )
def _snake_case ( self : Tuple ):
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def _snake_case ( self : Tuple ):
snake_case_ : str = self.params.max_model_input_size
snake_case_ : Dict = self.lengths > max_len
logger.info(f"Splitting {sum(lowercase_ )} too long sequences." )
def divide_chunks(lowercase_ : Tuple , lowercase_ : Optional[Any] ):
return [l[i : i + n] for i in range(0 , len(lowercase_ ) , lowercase_ )]
snake_case_ : Tuple = []
snake_case_ : Any = []
if self.params.mlm:
snake_case_, snake_case_ : Union[str, Any] = self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
else:
snake_case_, snake_case_ : Dict = self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
snake_case_ : Any = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
snake_case_ : Dict = np.insert(lowercase_ , 0 , lowercase_ )
if sub_s[-1] != sep_id:
snake_case_ : Tuple = np.insert(lowercase_ , len(lowercase_ ) , lowercase_ )
assert len(lowercase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowercase_ )
new_tok_ids.extend(lowercase_ )
new_lengths.extend([len(lowercase_ ) for l in sub_seqs] )
snake_case_ : List[str] = np.array(lowercase_ )
snake_case_ : Optional[Any] = np.array(lowercase_ )
def _snake_case ( self : Optional[int] ):
snake_case_ : List[Any] = len(self )
snake_case_ : List[str] = self.lengths > 11
snake_case_ : Dict = self.token_ids[indices]
snake_case_ : Dict = self.lengths[indices]
snake_case_ : str = len(self )
logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def _snake_case ( self : Tuple ):
if "unk_token" not in self.params.special_tok_ids:
return
else:
snake_case_ : str = self.params.special_tok_ids['''unk_token''']
snake_case_ : str = len(self )
snake_case_ : int = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
snake_case_ : str = (unk_occs / self.lengths) < 0.5
snake_case_ : Optional[Any] = self.token_ids[indices]
snake_case_ : Optional[int] = self.lengths[indices]
snake_case_ : Dict = len(self )
logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def _snake_case ( self : Dict ):
if not self.params.is_master:
return
logger.info(f"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def _snake_case ( self : List[str] , lowercase_ : Dict ):
snake_case_ : Optional[int] = [t[0] for t in batch]
snake_case_ : str = [t[1] for t in batch]
assert len(lowercase_ ) == len(lowercase_ )
# Max for paddings
snake_case_ : str = max(lowercase_ )
# Pad token ids
if self.params.mlm:
snake_case_ : Tuple = self.params.special_tok_ids['''pad_token''']
else:
snake_case_ : Dict = self.params.special_tok_ids['''unk_token''']
snake_case_ : Any = [list(t.astype(lowercase_ ) ) + [pad_idx] * (max_seq_len_ - len(lowercase_ )) for t in token_ids]
assert len(tk_ ) == len(lowercase_ )
assert all(len(lowercase_ ) == max_seq_len_ for t in tk_ )
snake_case_ : str = torch.tensor(tk_ ) # (bs, max_seq_len_)
snake_case_ : Optional[int] = torch.tensor(lowercase_ ) # (bs)
return tk_t, lg_t
| 264 | 0 |
import math
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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class lowercase_ ( lowerCAmelCase__ ):
"""simple docstring"""
UpperCAmelCase_ : torch.FloatTensor
UpperCAmelCase_ : Optional[torch.FloatTensor] = None
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__=0.9_99 , snake_case__="cosine" , ) -> Dict:
if alpha_transform_type == "cosine":
def alpha_bar_fn(snake_case__ ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(snake_case__ ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )
lowerCAmelCase = []
for i in range(_a ):
lowerCAmelCase = i / num_diffusion_timesteps
lowerCAmelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_a ) / alpha_bar_fn(_a ) , _a ) )
return torch.tensor(_a , dtype=torch.floataa )
class lowercase_ ( lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
@register_to_config
def __init__( self , __SCREAMING_SNAKE_CASE = 1000 , __SCREAMING_SNAKE_CASE = "fixed_small_log" , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE = 1.0 , __SCREAMING_SNAKE_CASE = "epsilon" , __SCREAMING_SNAKE_CASE = "squaredcos_cap_v2" , ) ->Union[str, Any]:
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
lowerCAmelCase = betas_for_alpha_bar(lowercase_ )
lowerCAmelCase = 1.0 - self.betas
lowerCAmelCase = torch.cumprod(self.alphas , dim=0 )
lowerCAmelCase = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
lowerCAmelCase = 1.0
# setable values
lowerCAmelCase = None
lowerCAmelCase = torch.from_numpy(np.arange(0 , lowercase_ )[::-1].copy() )
lowerCAmelCase = variance_type
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ) ->List[Any]:
return sample
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ) ->int:
lowerCAmelCase = num_inference_steps
lowerCAmelCase = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
lowerCAmelCase = (np.arange(0 , lowercase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
lowerCAmelCase = torch.from_numpy(lowercase_ ).to(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ) ->Tuple:
if prev_timestep is None:
lowerCAmelCase = t - 1
lowerCAmelCase = self.alphas_cumprod[t]
lowerCAmelCase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
lowerCAmelCase = 1 - alpha_prod_t
lowerCAmelCase = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
lowerCAmelCase = self.betas[t]
else:
lowerCAmelCase = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowerCAmelCase = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
lowerCAmelCase = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
lowerCAmelCase = torch.log(torch.clamp(lowercase_ , min=1e-20 ) )
lowerCAmelCase = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
lowerCAmelCase = variance.log()
lowerCAmelCase = beta.log()
lowerCAmelCase = (predicted_variance + 1) / 2
lowerCAmelCase = frac * max_log + (1 - frac) * min_log
return variance
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE = True , ) ->Dict:
lowerCAmelCase = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
lowerCAmelCase = torch.split(lowercase_ , sample.shape[1] , dim=1 )
else:
lowerCAmelCase = None
# 1. compute alphas, betas
if prev_timestep is None:
lowerCAmelCase = t - 1
lowerCAmelCase = self.alphas_cumprod[t]
lowerCAmelCase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
lowerCAmelCase = 1 - alpha_prod_t
lowerCAmelCase = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
lowerCAmelCase = self.betas[t]
lowerCAmelCase = self.alphas[t]
else:
lowerCAmelCase = 1 - alpha_prod_t / alpha_prod_t_prev
lowerCAmelCase = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowerCAmelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowerCAmelCase = model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
''' for the UnCLIPScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowerCAmelCase = torch.clamp(
lowercase_ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowerCAmelCase = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
lowerCAmelCase = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowerCAmelCase = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
lowerCAmelCase = 0
if t > 0:
lowerCAmelCase = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=lowercase_ , device=model_output.device )
lowerCAmelCase = self._get_variance(
lowercase_ , predicted_variance=lowercase_ , prev_timestep=lowercase_ , )
if self.variance_type == "fixed_small_log":
lowerCAmelCase = variance
elif self.variance_type == "learned_range":
lowerCAmelCase = (0.5 * variance).exp()
else:
raise ValueError(
F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
''' for the UnCLIPScheduler.''' )
lowerCAmelCase = variance * variance_noise
lowerCAmelCase = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=lowercase_ , pred_original_sample=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ) ->Tuple:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
lowerCAmelCase = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
lowerCAmelCase = timesteps.to(original_samples.device )
lowerCAmelCase = alphas_cumprod[timesteps] ** 0.5
lowerCAmelCase = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
lowerCAmelCase = sqrt_alpha_prod.unsqueeze(-1 )
lowerCAmelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
lowerCAmelCase = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
lowerCAmelCase = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
lowerCAmelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 338 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __lowercase ( _a , _a , _a = "x" , _a = 10**-10 , _a = 1 , ):
snake_case_ : Any = symbols(_a )
snake_case_ : int = lambdify(_a , _a )
snake_case_ : Optional[Any] = lambdify(_a , diff(_a , _a ) )
snake_case_ : Optional[Any] = starting_point
while True:
if diff_function(_a ) != 0:
snake_case_ : Optional[int] = prev_guess - multiplicity * func(_a ) / diff_function(
_a )
else:
raise ZeroDivisionError('''Could not find root''' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
snake_case_ : int = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
# Find root of polynomial
# Find fourth Root of 5
print(f'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')
# Find value of e
print(
'''The root of log(y) - 1 = 0 is ''',
f'{newton_raphson("log(y) - 1", 2, variable="y")}',
)
# Exponential Roots
print(
'''The root of exp(x) - 1 = 0 is''',
f'{newton_raphson("exp(x) - 1", 10, precision=0.005)}',
)
# Find root of cos(x)
print(f'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')
| 264 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase :List[str] = 1_6
lowerCAmelCase :Any = 3_2
def lowerCamelCase ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : Any = 16 ):
"""simple docstring"""
__magic_name__ : Optional[int] = AutoTokenizer.from_pretrained('bert-base-cased' )
__magic_name__ : str = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowerCAmelCase : List[str] ):
# max_length=None => use the model max length (it's actually the default)
__magic_name__ : Optional[Any] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_a , max_length=_a )
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():
__magic_name__ : int = datasets.map(
_a , batched=_a , 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
__magic_name__ : Optional[int] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowerCAmelCase : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__magic_name__ : List[Any] = 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":
__magic_name__ : Dict = 16
elif accelerator.mixed_precision != "no":
__magic_name__ : int = 8
else:
__magic_name__ : Dict = None
return tokenizer.pad(
_a , padding='longest' , max_length=_a , pad_to_multiple_of=_a , return_tensors='pt' , )
# Instantiate dataloaders.
__magic_name__ : Tuple = DataLoader(
tokenized_datasets['train'] , shuffle=_a , collate_fn=_a , batch_size=_a )
__magic_name__ : Dict = DataLoader(
tokenized_datasets['validation'] , shuffle=_a , collate_fn=_a , batch_size=_a )
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
lowerCAmelCase :Optional[int] = mocked_dataloaders # noqa: F811
def lowerCamelCase ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[str] ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS' , _a ) == "1":
__magic_name__ : Optional[int] = 2
# New Code #
__magic_name__ : Union[str, Any] = int(args.gradient_accumulation_steps )
__magic_name__ : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
__magic_name__ : Any = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_a )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__magic_name__ : Optional[int] = config['''lr''']
__magic_name__ : Dict = int(config['num_epochs'] )
__magic_name__ : Optional[int] = int(config['seed'] )
__magic_name__ : Optional[int] = int(config['batch_size'] )
__magic_name__ : Optional[int] = evaluate.load('glue' , 'mrpc' )
set_seed(_a )
__magic_name__ : Optional[int] = get_dataloaders(_a , _a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__magic_name__ : Tuple = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=_a )
# 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).
__magic_name__ : Tuple = model.to(accelerator.device )
# Instantiate optimizer
__magic_name__ : List[str] = AdamW(params=model.parameters() , lr=_a )
# Instantiate scheduler
__magic_name__ : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=_a , num_warmup_steps=100 , num_training_steps=(len(_a ) * 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.
__magic_name__ : Union[str, Any] = accelerator.prepare(
_a , _a , _a , _a , _a )
# Now we train the model
for epoch in range(_a ):
model.train()
with LocalSGD(
accelerator=_a , model=_a , local_sgd_steps=_a , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(_a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(_a ):
__magic_name__ : int = model(**_a )
__magic_name__ : List[str] = output.loss
accelerator.backward(_a )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(_a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__magic_name__ : int = model(**_a )
__magic_name__ : Optional[int] = outputs.logits.argmax(dim=-1 )
__magic_name__ : List[Any] = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=_a , references=_a , )
__magic_name__ : Union[str, Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , _a )
def lowerCamelCase ( ):
"""simple docstring"""
__magic_name__ : List[str] = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=_a , default=_a , 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.' , )
# New Code #
parser.add_argument(
'--gradient_accumulation_steps' , type=_a , default=1 , help='The number of minibatches to be ran before gradients are accumulated.' , )
parser.add_argument(
'--local_sgd_steps' , type=_a , default=8 , help='Number of local SGD steps or None to disable local SGD' )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
__magic_name__ : Dict = parser.parse_args()
__magic_name__ : Optional[Any] = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_a , _a )
if __name__ == "__main__":
main() | 331 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCamelCase : Dict = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class a ( lowerCAmelCase__, unittest.TestCase ):
UpperCAmelCase_ : str =XLMRobertaTokenizer
UpperCAmelCase_ : int =XLMRobertaTokenizerFast
UpperCAmelCase_ : str =True
UpperCAmelCase_ : Dict =True
def UpperCamelCase_ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowercase = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ):
lowercase = '''<pad>'''
lowercase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def UpperCamelCase_ ( self ):
lowercase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(lowercase_ ) , 1_0_0_2 )
def UpperCamelCase_ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def UpperCamelCase_ ( self ):
lowercase = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
lowercase = tokenizer.tokenize('This is a test' )
self.assertListEqual(lowercase_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowercase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowercase = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowercase = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def UpperCamelCase_ ( self ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowercase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
lowercase = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase = tempfile.mkdtemp()
lowercase = tokenizer_r.save_pretrained(lowercase_ )
lowercase = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
lowercase = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
lowercase = tokenizer_r.from_pretrained(lowercase_ )
lowercase = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
lowercase = tempfile.mkdtemp()
lowercase = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
lowercase = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
lowercase = tokenizer_r.from_pretrained(lowercase_ )
lowercase = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
lowercase = tempfile.mkdtemp()
lowercase = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
lowercase = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowercase = tokenizer_r.from_pretrained(lowercase_ )
lowercase = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@cached_property
def UpperCamelCase_ ( self ):
return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base' )
def UpperCamelCase_ ( self ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowercase_ , f.name )
lowercase = XLMRobertaTokenizer(f.name , keep_accents=lowercase_ )
lowercase = pickle.dumps(lowercase_ )
pickle.loads(lowercase_ )
def UpperCamelCase_ ( self ):
if not self.test_rust_tokenizer:
return
lowercase = self.get_tokenizer()
lowercase = self.get_rust_tokenizer()
lowercase = '''I was born in 92000, and this is falsé.'''
lowercase = tokenizer.tokenize(lowercase_ )
lowercase = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
lowercase = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
lowercase = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
lowercase = self.get_rust_tokenizer()
lowercase = tokenizer.encode(lowercase_ )
lowercase = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
@slow
def UpperCamelCase_ ( self ):
lowercase = '''Hello World!'''
lowercase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def UpperCamelCase_ ( self ):
lowercase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
lowercase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def UpperCamelCase_ ( self ):
# fmt: off
lowercase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='xlm-roberta-base' , revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' , )
| 220 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def __lowercase ( _a ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class snake_case__( lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : torch.FloatTensor
class snake_case__( lowerCAmelCase__, lowerCAmelCase__ ):
'''simple docstring'''
@register_to_config
def __init__( self , __lowercase = 1_6 , __lowercase = 8_8 , __lowercase = None , __lowercase = None , __lowercase = 1 , __lowercase = 0.0 , __lowercase = 3_2 , __lowercase = None , __lowercase = False , __lowercase = None , __lowercase = "geglu" , __lowercase = True , __lowercase = True , ) -> Any:
super().__init__()
lowerCAmelCase_ : Tuple = num_attention_heads
lowerCAmelCase_ : int = attention_head_dim
lowerCAmelCase_ : List[Any] = num_attention_heads * attention_head_dim
lowerCAmelCase_ : Tuple = in_channels
lowerCAmelCase_ : List[Any] = torch.nn.GroupNorm(num_groups=lowercase_ , num_channels=lowercase_ , eps=1e-6 , affine=lowercase_ )
lowerCAmelCase_ : int = nn.Linear(lowercase_ , lowercase_ )
# 3. Define transformers blocks
lowerCAmelCase_ : Optional[int] = nn.ModuleList(
[
BasicTransformerBlock(
lowercase_ , lowercase_ , lowercase_ , dropout=lowercase_ , cross_attention_dim=lowercase_ , activation_fn=lowercase_ , attention_bias=lowercase_ , double_self_attention=lowercase_ , norm_elementwise_affine=lowercase_ , )
for d in range(lowercase_ )
] )
lowerCAmelCase_ : Tuple = nn.Linear(lowercase_ , lowercase_ )
def lowercase_ ( self , __lowercase , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=1 , __lowercase=None , __lowercase = True , ) -> List[str]:
lowerCAmelCase_ : List[Any] = hidden_states.shape
lowerCAmelCase_ : Optional[int] = batch_frames // num_frames
lowerCAmelCase_ : Dict = hidden_states
lowerCAmelCase_ : List[Any] = hidden_states[None, :].reshape(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowerCAmelCase_ : Union[str, Any] = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
lowerCAmelCase_ : Optional[int] = self.norm(lowercase_ )
lowerCAmelCase_ : Optional[int] = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , lowercase_ , lowercase_ )
lowerCAmelCase_ : Any = self.proj_in(lowercase_ )
# 2. Blocks
for block in self.transformer_blocks:
lowerCAmelCase_ : Union[str, Any] = block(
lowercase_ , encoder_hidden_states=lowercase_ , timestep=lowercase_ , cross_attention_kwargs=lowercase_ , class_labels=lowercase_ , )
# 3. Output
lowerCAmelCase_ : Dict = self.proj_out(lowercase_ )
lowerCAmelCase_ : Dict = (
hidden_states[None, None, :]
.reshape(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
lowerCAmelCase_ : Union[str, Any] = hidden_states.reshape(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowerCAmelCase_ : Optional[int] = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=lowercase_ ) | 262 |
"""simple docstring"""
import sys
lowercase__ : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __lowercase ( _a ):
snake_case_ : List[Any] = 1
for digit in s:
product *= int(_a )
return product
def __lowercase ( _a = N ):
snake_case_ : Optional[int] = -sys.maxsize - 1
snake_case_ : str = n[:13]
snake_case_ : List[Any] = 13
while cur_index < len(_a ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
snake_case_ : int = substr[1:] + n[cur_index]
cur_index += 1
else:
snake_case_ : Optional[Any] = max(_a , str_eval(_a ) )
snake_case_ : Any = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'{solution() = }')
| 264 | 0 |
"""simple docstring"""
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
A: List[str] = logging.get_logger(__name__)
A: str = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A: Union[str, Any] = {
'''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'''
),
},
}
A: int = {
'''squeezebert/squeezebert-uncased''': 5_1_2,
'''squeezebert/squeezebert-mnli''': 5_1_2,
'''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}
A: Tuple = {
'''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase__ ):
__lowerCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
__lowerCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : str = SqueezeBertTokenizer
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="[UNK]" , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="[PAD]" , _SCREAMING_SNAKE_CASE="[CLS]" , _SCREAMING_SNAKE_CASE="[MASK]" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ) -> Any:
'''simple docstring'''
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , do_lower_case=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , tokenize_chinese_chars=lowercase_ , strip_accents=lowercase_ , **lowercase_ , )
UpperCAmelCase : int = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowercase_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowercase_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowercase_ ) != tokenize_chinese_chars
):
UpperCAmelCase : Optional[int] = getattr(lowercase_ , normalizer_state.pop("""type""" ) )
UpperCAmelCase : List[Any] = do_lower_case
UpperCAmelCase : Any = strip_accents
UpperCAmelCase : Union[str, Any] = tokenize_chinese_chars
UpperCAmelCase : str = normalizer_class(**lowercase_ )
UpperCAmelCase : str = do_lower_case
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[str] = [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 SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Optional[int] = [self.sep_token_id]
UpperCAmelCase : Union[str, Any] = [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 SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[str] = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
| 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : List[Any] = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Any = ['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : int = [
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = [
'''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDistilBertForMaskedLM''',
'''TFDistilBertForMultipleChoice''',
'''TFDistilBertForQuestionAnswering''',
'''TFDistilBertForSequenceClassification''',
'''TFDistilBertForTokenClassification''',
'''TFDistilBertMainLayer''',
'''TFDistilBertModel''',
'''TFDistilBertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''FlaxDistilBertForMaskedLM''',
'''FlaxDistilBertForMultipleChoice''',
'''FlaxDistilBertForQuestionAnswering''',
'''FlaxDistilBertForSequenceClassification''',
'''FlaxDistilBertForTokenClassification''',
'''FlaxDistilBertModel''',
'''FlaxDistilBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
lowercase__ : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
from PIL import Image
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(lowerCamelCase ) -> int:
return int(1_2_8 + factor * (c - 1_2_8) )
return img.point(_a )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change contrast to 170
lowerCAmelCase__ : Optional[int] = change_contrast(img, 170)
cont_img.save('image_data/lena_high_contrast.png', format='png')
| 98 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = logging.get_logger(__name__)
def __lowercase ( _a , _a=False ):
snake_case_ : List[str] = []
# fmt: off
# stem:
rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''') )
rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''') )
rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''') )
# backbone
rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case_ : Optional[int] = [(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'''),
] )
# fmt: on
return rename_keys
def __lowercase ( _a , _a , _a=False ):
for i in range(config.num_hidden_layers ):
if base_model:
snake_case_ : List[str] = ''''''
else:
snake_case_ : Dict = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ : List[str] = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
snake_case_ : Optional[int] = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Any = in_proj_weight[
: config.hidden_size, :
]
snake_case_ : Dict = in_proj_bias[: config.hidden_size]
snake_case_ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case_ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case_ : Dict = in_proj_weight[
-config.hidden_size :, :
]
snake_case_ : str = in_proj_bias[-config.hidden_size :]
def __lowercase ( _a ):
snake_case_ : Dict = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(_a , _a )
def __lowercase ( _a , _a , _a ):
snake_case_ : Union[str, Any] = dct.pop(_a )
snake_case_ : Union[str, Any] = val
def __lowercase ( ):
snake_case_ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ : Tuple = Image.open(requests.get(_a , stream=_a ).raw )
return im
@torch.no_grad()
def __lowercase ( _a , _a , _a=False ):
snake_case_ : str = BitConfig(
global_padding='''same''' , layer_type='''bottleneck''' , depths=(3, 4, 9) , out_features=['''stage3'''] , embedding_dynamic_padding=_a , )
snake_case_ : Tuple = ViTHybridConfig(backbone_config=_a , image_size=384 , num_labels=1_000 )
snake_case_ : int = False
# load original model from timm
snake_case_ : str = timm.create_model(_a , pretrained=_a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case_ : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_a )
snake_case_ : int = create_rename_keys(_a , _a )
for src, dest in rename_keys:
rename_key(_a , _a , _a )
read_in_q_k_v(_a , _a , _a )
snake_case_ : Optional[Any] = '''huggingface/label-files'''
snake_case_ : Any = '''imagenet-1k-id2label.json'''
snake_case_ : Dict = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
snake_case_ : Dict = {int(_a ): v for k, v in idalabel.items()}
snake_case_ : Optional[int] = idalabel
snake_case_ : Optional[Any] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
snake_case_ : Optional[Any] = ViTHybridModel(_a ).eval()
else:
snake_case_ : Any = ViTHybridForImageClassification(_a ).eval()
model.load_state_dict(_a )
# create image processor
snake_case_ : Optional[Any] = create_transform(**resolve_data_config({} , model=_a ) )
snake_case_ : List[Any] = transform.transforms
snake_case_ : Optional[Any] = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
snake_case_ : List[Any] = ViTHybridImageProcessor(
do_resize=_a , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_a , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=_a , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Optional[int] = transform(_a ).unsqueeze(0 )
snake_case_ : int = processor(_a , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(_a , _a )
# verify logits
with torch.no_grad():
snake_case_ : List[str] = model(_a )
snake_case_ : Any = outputs.logits
print('''Predicted class:''' , logits.argmax(-1 ).item() )
if base_model:
snake_case_ : Optional[Any] = timm_model.forward_features(_a )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_a , outputs.pooler_output , atol=1E-3 )
else:
snake_case_ : int = timm_model(_a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_a , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(f"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_a )
if push_to_hub:
print(f"Pushing model and processor to the hub {vit_name}" )
model.push_to_hub(f"ybelkada/{vit_name}" )
processor.push_to_hub(f"ybelkada/{vit_name}" )
if __name__ == "__main__":
lowercase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--vit_name''',
default='''vit_base_r50_s16_384''',
type=str,
help='''Name of the hybrid ViT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
lowercase__ : Any = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 264 | 0 |
def __lowercase ( lowerCamelCase : Tuple ):
UpperCamelCase_ : str = []
UpperCamelCase_ : List[str] = set({'(', '[', '{'} )
UpperCamelCase_ : Tuple = set({')', ']', '}'} )
UpperCamelCase_ : Union[str, Any] = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(_a ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(_a ) == 0 or (len(_a ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(_a ) == 0
def __lowercase ( ):
UpperCamelCase_ : Optional[Any] = input('Enter sequence of brackets: ' )
if is_balanced(_a ):
print(_a , 'is balanced' )
else:
print(_a , 'is not balanced' )
if __name__ == "__main__":
main()
| 175 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
lowercase__ : str = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def __lowercase ( _a , _a ):
snake_case_ : Optional[int] = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
snake_case_ : List[Any] = int(re.match(r'''.*layer_(\d*).*''' , _a )[1] )
layer_number -= 3
return f"h.{layer_number}." + key
def __lowercase ( _a ):
if dtype == torch.bool:
return 1 / 8
snake_case_ : Dict = re.search(r'''[^\d](\d+)$''' , str(_a ) )
if bit_search is None:
raise ValueError(f"`dtype` is not a valid dtype: {dtype}." )
snake_case_ : Optional[int] = int(bit_search.groups()[0] )
return bit_size // 8
def __lowercase ( _a , _a , _a , _a , _a ):
# Construct model
if bloom_config_file == "":
snake_case_ : int = BloomConfig()
else:
snake_case_ : List[str] = BloomConfig.from_json_file(_a )
if shard_model:
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : int = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[str] = {'''weight_map''': {}, '''metadata''': {}}
snake_case_ : Any = 0
snake_case_ : Union[str, Any] = None
snake_case_ : List[str] = BloomConfig()
for j, file in enumerate(_a ):
print('''Processing file: {}'''.format(_a ) )
snake_case_ : Dict = None
for i in range(_a ):
# load all TP files
snake_case_ : Union[str, Any] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : List[str] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : Any = temp.pop(_a )
if tensors is None:
snake_case_ : Any = temp
else:
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : List[str] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Any = tensors[key] / pretraining_tp
torch.save(
_a , os.path.join(
_a , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
snake_case_ : List[str] = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
snake_case_ : List[str] = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) )
snake_case_ : int = BloomConfig()
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Dict = total_size
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(_a , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f:
snake_case_ : Tuple = json.dumps(_a , indent=2 , sort_keys=_a ) + '''\n'''
f.write(_a )
else:
snake_case_ : Union[str, Any] = BloomModel(_a )
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : Dict = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[Any] = None
for i, file in enumerate(_a ):
snake_case_ : Optional[Any] = None
for i in range(_a ):
# load all TP files
snake_case_ : List[str] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : Optional[Any] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : str = temp.pop(_a )
if tensors is None:
snake_case_ : int = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : Optional[Any] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Union[str, Any] = tensors[key] / pretraining_tp
snake_case_ : Any = model.load_state_dict(_a , strict=_a )
assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
if missing_keys is None:
snake_case_ : Optional[int] = set(other_keys.missing_keys )
else:
snake_case_ : Tuple = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f"The keys {missing_keys} are missing"
# Save pytorch-model
os.makedirs(_a , exist_ok=_a )
snake_case_ : List[str] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}" )
if config.torch_dtype is not None:
snake_case_ : Optional[Any] = model.to(config.torch_dtype )
torch.save(model.state_dict() , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
lowercase__ : List[Any] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 264 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class lowercase ( lowerCAmelCase__ ):
"""simple docstring"""
def _snake_case ( self ) -> str:
_UpperCAmelCase : str = tempfile.mkdtemp()
_UpperCAmelCase : List[Any] = 5
# Realm tok
_UpperCAmelCase : List[str] = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''test''',
'''question''',
'''this''',
'''is''',
'''the''',
'''first''',
'''second''',
'''third''',
'''fourth''',
'''fifth''',
'''record''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_UpperCAmelCase : Union[str, Any] = os.path.join(self.tmpdirname ,"""realm_tokenizer""" )
os.makedirs(lowercase_ ,exist_ok=lowercase_ )
_UpperCAmelCase : Tuple = os.path.join(lowercase_ ,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] ) )
_UpperCAmelCase : Tuple = os.path.join(self.tmpdirname ,"""realm_block_records""" )
os.makedirs(lowercase_ ,exist_ok=lowercase_ )
def _snake_case ( self ) -> Union[str, Any]:
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"""realm_tokenizer""" ) )
def _snake_case ( self ) -> str:
shutil.rmtree(self.tmpdirname )
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : str = RealmConfig(num_block_records=self.num_block_records )
return config
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : Any = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""question""": ["""foo""", """bar"""],
"""answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
} )
return dataset
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Union[str, Any] = np.array(
[
B"""This is the first record""",
B"""This is the second record""",
B"""This is the third record""",
B"""This is the fourth record""",
B"""This is the fifth record""",
B"""This is a longer longer longer record""",
] ,dtype=lowercase_ ,)
return block_records
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : List[Any] = RealmRetriever(
block_records=self.get_dummy_block_records() ,tokenizer=self.get_tokenizer() ,)
return retriever
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Dict = self.get_config()
_UpperCAmelCase : int = self.get_dummy_retriever()
_UpperCAmelCase : Dict = retriever.tokenizer
_UpperCAmelCase : List[Any] = np.array([0, 3] ,dtype="""long""" )
_UpperCAmelCase : Any = tokenizer(["""Test question"""] ).input_ids
_UpperCAmelCase : Any = tokenizer(
["""the fourth"""] ,add_special_tokens=lowercase_ ,return_token_type_ids=lowercase_ ,return_attention_mask=lowercase_ ,).input_ids
_UpperCAmelCase : List[Any] = config.reader_seq_len
_UpperCAmelCase : Optional[Any] = retriever(
lowercase_ ,lowercase_ ,answer_ids=lowercase_ ,max_length=lowercase_ ,return_tensors="""np""" )
self.assertEqual(len(lowercase_ ) ,2 )
self.assertEqual(len(lowercase_ ) ,2 )
self.assertEqual(len(lowercase_ ) ,2 )
self.assertEqual(concat_inputs.input_ids.shape ,(2, 10) )
self.assertEqual(concat_inputs.attention_mask.shape ,(2, 10) )
self.assertEqual(concat_inputs.token_type_ids.shape ,(2, 10) )
self.assertEqual(concat_inputs.special_tokens_mask.shape ,(2, 10) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) ,["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """first""", """record""", """[SEP]"""] ,)
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) ,["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """fourth""", """record""", """[SEP]"""] ,)
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Any = self.get_config()
_UpperCAmelCase : Tuple = self.get_dummy_retriever()
_UpperCAmelCase : int = retriever.tokenizer
_UpperCAmelCase : Any = np.array([0, 3, 5] ,dtype="""long""" )
_UpperCAmelCase : Tuple = tokenizer(["""Test question"""] ).input_ids
_UpperCAmelCase : List[Any] = tokenizer(
["""the fourth""", """longer longer"""] ,add_special_tokens=lowercase_ ,return_token_type_ids=lowercase_ ,return_attention_mask=lowercase_ ,).input_ids
_UpperCAmelCase : Dict = config.reader_seq_len
_UpperCAmelCase : List[str] = retriever(
lowercase_ ,lowercase_ ,answer_ids=lowercase_ ,max_length=lowercase_ ,return_tensors="""np""" )
self.assertEqual([False, True, True] ,lowercase_ )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] ,lowercase_ )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] ,lowercase_ )
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : int = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname ,"""realm_block_records""" ) )
# Test local path
_UpperCAmelCase : Optional[int] = retriever.from_pretrained(os.path.join(self.tmpdirname ,"""realm_block_records""" ) )
self.assertEqual(retriever.block_records[0] ,B"""This is the first record""" )
# Test mocked remote path
with patch("""transformers.models.realm.retrieval_realm.hf_hub_download""" ) as mock_hf_hub_download:
_UpperCAmelCase : Optional[Any] = os.path.join(
os.path.join(self.tmpdirname ,"""realm_block_records""" ) ,_REALM_BLOCK_RECORDS_FILENAME )
_UpperCAmelCase : List[Any] = RealmRetriever.from_pretrained("""google/realm-cc-news-pretrained-openqa""" )
self.assertEqual(retriever.block_records[0] ,B"""This is the first record""" )
| 215 |
"""simple docstring"""
def __lowercase ( _a , _a , _a=False ):
if isinstance(_a , _a ) and isinstance(_a , _a ):
snake_case_ : Union[str, Any] = len(set_a.intersection(_a ) )
if alternative_union:
snake_case_ : Any = len(_a ) + len(_a )
else:
snake_case_ : str = len(set_a.union(_a ) )
return intersection / union
if isinstance(_a , (list, tuple) ) and isinstance(_a , (list, tuple) ):
snake_case_ : str = [element for element in set_a if element in set_b]
if alternative_union:
snake_case_ : Tuple = len(_a ) + len(_a )
return len(_a ) / union
else:
snake_case_ : List[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_a ) / len(_a )
return len(_a ) / len(_a )
return None
if __name__ == "__main__":
lowercase__ : Any = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowercase__ : Optional[Any] = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 264 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class UpperCAmelCase ( unittest.TestCase ):
def lowercase__ ( self : str ) -> Union[str, Any]:
_lowerCAmelCase = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
_lowerCAmelCase = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
_lowerCAmelCase = {
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
_lowerCAmelCase = {
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 1_60_00,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
_lowerCAmelCase = tempfile.mkdtemp()
_lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
_lowerCAmelCase = os.path.join(self.tmpdirname , lowercase_ )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowercase_ ) + """\n""" )
with open(self.feature_extraction_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowercase_ ) + """\n""" )
# load decoder from hub
_lowerCAmelCase = '''hf-internal-testing/ngram-beam-search-decoder'''
def lowercase__ ( self : Any , **__snake_case : Optional[Any] ) -> Tuple:
_lowerCAmelCase = self.add_kwargs_tokens_map.copy()
kwargs.update(lowercase_ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def lowercase__ ( self : str , **__snake_case : Dict ) -> Union[str, Any]:
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **lowercase_ )
def lowercase__ ( self : Union[str, Any] , **__snake_case : int ) -> int:
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **lowercase_ )
def lowercase__ ( self : Tuple ) -> Dict:
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self : Optional[int] ) -> List[str]:
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_feature_extractor()
_lowerCAmelCase = self.get_decoder()
_lowerCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
processor.save_pretrained(self.tmpdirname )
_lowerCAmelCase = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowercase_ )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , lowercase_ )
def lowercase__ ( self : int ) -> Dict:
_lowerCAmelCase = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
_lowerCAmelCase = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def lowercase__ ( self : Dict ) -> Any:
_lowerCAmelCase = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(lowercase_ , """include""" ):
WavaVecaProcessorWithLM(
tokenizer=lowercase_ , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def lowercase__ ( self : int ) -> Union[str, Any]:
_lowerCAmelCase = self.get_feature_extractor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_decoder()
_lowerCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
_lowerCAmelCase = floats_list((3, 10_00) )
_lowerCAmelCase = feature_extractor(lowercase_ , return_tensors="""np""" )
_lowerCAmelCase = processor(lowercase_ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowercase__ ( self : Optional[int] ) -> str:
_lowerCAmelCase = self.get_feature_extractor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_decoder()
_lowerCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
_lowerCAmelCase = '''This is a test string'''
_lowerCAmelCase = processor(text=lowercase_ )
_lowerCAmelCase = tokenizer(lowercase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase__ ( self : List[str] , __snake_case : Any=(2, 10, 16) , __snake_case : str=77 ) -> List[str]:
np.random.seed(lowercase_ )
return np.random.rand(*lowercase_ )
def lowercase__ ( self : Optional[Any] ) -> Dict:
_lowerCAmelCase = self.get_feature_extractor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_decoder()
_lowerCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
_lowerCAmelCase = self._get_dummy_logits(shape=(10, 16) , seed=13 )
_lowerCAmelCase = processor.decode(lowercase_ )
_lowerCAmelCase = decoder.decode_beams(lowercase_ )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def lowercase__ ( self : Optional[Any] , __snake_case : Union[str, Any] ) -> Any:
_lowerCAmelCase = self.get_feature_extractor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_decoder()
_lowerCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
_lowerCAmelCase = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
_lowerCAmelCase = processor.batch_decode(lowercase_ )
else:
with get_context(lowercase_ ).Pool() as pool:
_lowerCAmelCase = processor.batch_decode(lowercase_ , lowercase_ )
_lowerCAmelCase = list(lowercase_ )
with get_context("""fork""" ).Pool() as p:
_lowerCAmelCase = decoder.decode_beams_batch(lowercase_ , lowercase_ )
_lowerCAmelCase = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowercase_ , decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] , decoded_processor.text )
self.assertListEqual(lowercase_ , decoded_processor.logit_score )
self.assertListEqual(lowercase_ , decoded_processor.lm_score )
def lowercase__ ( self : List[str] ) -> List[str]:
_lowerCAmelCase = self.get_feature_extractor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_decoder()
_lowerCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
_lowerCAmelCase = self._get_dummy_logits()
_lowerCAmelCase = 15
_lowerCAmelCase = -20.0
_lowerCAmelCase = -4.0
_lowerCAmelCase = processor.batch_decode(
lowercase_ , beam_width=lowercase_ , beam_prune_logp=lowercase_ , token_min_logp=lowercase_ , )
_lowerCAmelCase = decoded_processor_out.text
_lowerCAmelCase = list(lowercase_ )
with get_context("""fork""" ).Pool() as pool:
_lowerCAmelCase = decoder.decode_beams_batch(
lowercase_ , lowercase_ , beam_width=lowercase_ , beam_prune_logp=lowercase_ , token_min_logp=lowercase_ , )
_lowerCAmelCase = [d[0][0] for d in decoded_decoder_out]
_lowerCAmelCase = [d[0][2] for d in decoded_decoder_out]
_lowerCAmelCase = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] , lowercase_ )
self.assertTrue(np.array_equal(lowercase_ , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.0_54, -18.4_47] , lowercase_ , atol=1E-3 ) )
self.assertTrue(np.array_equal(lowercase_ , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.5_54, -13.94_74] , lowercase_ , atol=1E-3 ) )
def lowercase__ ( self : List[Any] ) -> List[str]:
_lowerCAmelCase = self.get_feature_extractor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_decoder()
_lowerCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
_lowerCAmelCase = self._get_dummy_logits()
_lowerCAmelCase = 2.0
_lowerCAmelCase = 5.0
_lowerCAmelCase = -20.0
_lowerCAmelCase = True
_lowerCAmelCase = processor.batch_decode(
lowercase_ , alpha=lowercase_ , beta=lowercase_ , unk_score_offset=lowercase_ , lm_score_boundary=lowercase_ , )
_lowerCAmelCase = decoded_processor_out.text
_lowerCAmelCase = list(lowercase_ )
decoder.reset_params(
alpha=lowercase_ , beta=lowercase_ , unk_score_offset=lowercase_ , lm_score_boundary=lowercase_ , )
with get_context("""fork""" ).Pool() as pool:
_lowerCAmelCase = decoder.decode_beams_batch(
lowercase_ , lowercase_ , )
_lowerCAmelCase = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] , lowercase_ )
_lowerCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , lowercase_ )
def lowercase__ ( self : Dict ) -> int:
_lowerCAmelCase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_lowerCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
_lowerCAmelCase = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
_lowerCAmelCase = os.listdir(lowercase_ )
_lowerCAmelCase = ['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowercase_ , lowercase_ )
def lowercase__ ( self : Union[str, Any] ) -> Dict:
_lowerCAmelCase = snapshot_download("""hf-internal-testing/processor_with_lm""" )
_lowerCAmelCase = WavaVecaProcessorWithLM.from_pretrained(lowercase_ )
_lowerCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
_lowerCAmelCase = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
_lowerCAmelCase = os.listdir(lowercase_ )
_lowerCAmelCase = os.listdir(lowercase_ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowercase_ , lowercase_ )
def lowercase__ ( self : str ) -> Any:
_lowerCAmelCase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_lowerCAmelCase = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_lowerCAmelCase = floats_list((3, 10_00) )
_lowerCAmelCase = processor_wavaveca(lowercase_ , return_tensors="""np""" )
_lowerCAmelCase = processor_auto(lowercase_ , return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
_lowerCAmelCase = self._get_dummy_logits()
_lowerCAmelCase = processor_wavaveca.batch_decode(lowercase_ )
_lowerCAmelCase = processor_auto.batch_decode(lowercase_ )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def lowercase__ ( self : Union[str, Any] ) -> List[str]:
_lowerCAmelCase = self.get_feature_extractor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_decoder()
_lowerCAmelCase = WavaVecaProcessorWithLM(tokenizer=lowercase_ , feature_extractor=lowercase_ , decoder=lowercase_ )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
@staticmethod
def lowercase__ ( __snake_case : Tuple , __snake_case : str ) -> int:
_lowerCAmelCase = [d[key] for d in offsets]
return retrieved_list
def lowercase__ ( self : str ) -> List[Any]:
_lowerCAmelCase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_lowerCAmelCase = self._get_dummy_logits()[0]
_lowerCAmelCase = processor.decode(lowercase_ , output_word_offsets=lowercase_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(lowercase_ , lowercase_ ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] , """word""" ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """word""" ) , ["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """start_offset""" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] , """end_offset""" ) , [1, 3, 5] )
def lowercase__ ( self : Optional[int] ) -> Any:
_lowerCAmelCase = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
_lowerCAmelCase = self._get_dummy_logits()
_lowerCAmelCase = processor.batch_decode(lowercase_ , output_word_offsets=lowercase_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(lowercase_ , lowercase_ ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(lowercase_ , """word""" ) ) for o in outputs["""word_offsets"""]] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """word""" ) , ["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """start_offset""" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] , """end_offset""" ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def lowercase__ ( self : int ) -> Union[str, Any]:
import torch
_lowerCAmelCase = load_dataset("""common_voice""" , """en""" , split="""train""" , streaming=lowercase_ )
_lowerCAmelCase = ds.cast_column("""audio""" , datasets.Audio(sampling_rate=1_60_00 ) )
_lowerCAmelCase = iter(lowercase_ )
_lowerCAmelCase = next(lowercase_ )
_lowerCAmelCase = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
_lowerCAmelCase = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
_lowerCAmelCase = processor(sample["""audio"""]["""array"""] , return_tensors="""pt""" ).input_values
with torch.no_grad():
_lowerCAmelCase = model(lowercase_ ).logits.cpu().numpy()
_lowerCAmelCase = processor.decode(logits[0] , output_word_offsets=lowercase_ )
_lowerCAmelCase = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
_lowerCAmelCase = [
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
_lowerCAmelCase = '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(""" """.join(self.get_from_offsets(lowercase_ , """word""" ) ) , lowercase_ )
self.assertEqual(""" """.join(self.get_from_offsets(lowercase_ , """word""" ) ) , output.text )
# output times
_lowerCAmelCase = torch.tensor(self.get_from_offsets(lowercase_ , """start_time""" ) )
_lowerCAmelCase = torch.tensor(self.get_from_offsets(lowercase_ , """end_time""" ) )
# fmt: off
_lowerCAmelCase = torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99] )
_lowerCAmelCase = torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=0.01 ) )
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=0.01 ) )
| 70 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowercase__ : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __lowercase ( ):
snake_case_ : Optional[Any] = Github(os.environ['''GITHUB_TOKEN'''] )
snake_case_ : Any = g.get_repo('''huggingface/diffusers''' )
snake_case_ : Any = repo.get_issues(state='''open''' )
for issue in open_issues:
snake_case_ : str = sorted(issue.get_comments() , key=lambda _a : i.created_at , reverse=_a )
snake_case_ : Dict = comments[0] if len(_a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 264 | 0 |
"""simple docstring"""
import numpy as np
def __a ( __lowerCamelCase ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowercase ( _a , _a ):
# Load checkpoint
snake_case_ : Optional[Any] = torch.load(_a , map_location='''cpu''' )
snake_case_ : Union[str, Any] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
snake_case_ : Dict = {}
for k, v in state_dict.items():
if "pred_layer" in k:
snake_case_ : Union[str, Any] = v
else:
snake_case_ : Dict = v
snake_case_ : Union[str, Any] = chkpt['''params''']
snake_case_ : int = {n: v for n, v in config.items() if not isinstance(_a , (torch.FloatTensor, numpy.ndarray) )}
snake_case_ : int = chkpt['''dico_word2id''']
snake_case_ : str = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(f"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(_a , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
print(f"Save vocab file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
if __name__ == "__main__":
lowercase__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowercase__ : List[str] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 264 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _UpperCAmelCase ( a__ = ""):
'''simple docstring'''
a_ : str = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
a_ : List[Any] = BeautifulSoup(requests.get(_a).text , """html.parser""")
a_ : Optional[int] = soup.find_all("""td""" , attrs="""titleColumn""")
a_ : Dict = soup.find_all("""td""" , class_="""ratingColumn imdbRating""")
return {
title.a.text: float(rating.strong.text)
for title, rating in zip(_a , _a)
}
def _UpperCAmelCase ( a__ = "IMDb_Top_250_Movies.csv"):
'''simple docstring'''
a_ : int = get_imdb_top_aaa_movies()
with open(_a , """w""" , newline="""""") as out_file:
a_ : int = csv.writer(_a)
writer.writerow(["""Movie title""", """IMDb rating"""])
for title, rating in movies.items():
writer.writerow([title, rating])
if __name__ == "__main__":
write_movies()
| 248 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 264 | 0 |
import cmath
import math
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[str]:
lowerCAmelCase = math.radians(_a )
lowerCAmelCase = math.radians(_a )
# Convert voltage and current to rectangular form
lowerCAmelCase = cmath.rect(_a , _a )
lowerCAmelCase = cmath.rect(_a , _a )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __lowercase ( _a="" ):
snake_case_ : List[str] = tempfile.mkdtemp()
return os.path.join(_a , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : str ):
snake_case_ : int = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : Optional[int] = AgentAudio(lowercase_ )
snake_case_ : List[str] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowercase_ ) )
# Ensure that the file contains the same value as the original tensor
snake_case_, snake_case_ : int = sf.read(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , torch.tensor(lowercase_ ) , atol=1E-4 ) )
def _snake_case ( self : Optional[int] ):
snake_case_ : Any = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : List[str] = get_new_path(suffix='''.wav''' )
sf.write(lowercase_ , lowercase_ , 16000 )
snake_case_ : Tuple = AgentAudio(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
self.assertEqual(agent_type.to_string() , lowercase_ )
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Tuple ):
snake_case_ : List[Any] = torch.randint(0 , 256 , (64, 64, 3) )
snake_case_ : str = AgentImage(lowercase_ )
snake_case_ : Union[str, Any] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type._tensor , atol=1E-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : Any = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Optional[int] = Image.open(lowercase_ )
snake_case_ : Tuple = AgentImage(lowercase_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : int = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Dict = Image.open(lowercase_ )
snake_case_ : List[str] = AgentImage(lowercase_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Any ):
snake_case_ : Tuple = '''Hey!'''
snake_case_ : Optional[Any] = AgentText(lowercase_ )
self.assertEqual(lowercase_ , agent_type.to_string() )
self.assertEqual(lowercase_ , agent_type.to_raw() )
self.assertEqual(lowercase_ , lowercase_ )
| 264 | 0 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : int ):
"""simple docstring"""
assert isinstance(_a , _a )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def lowerCamelCase ( lowerCAmelCase : int , lowerCAmelCase : List[Any] , lowerCAmelCase : Any ):
"""simple docstring"""
__magic_name__ : Tuple = tmp_path / '''cache'''
__magic_name__ : Union[str, Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__magic_name__ : Optional[int] = JsonDatasetReader(_a , cache_dir=_a , keep_in_memory=_a ).read()
_check_json_dataset(_a , _a )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def lowerCamelCase ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : List[str] , lowerCAmelCase : List[str] ):
"""simple docstring"""
__magic_name__ : Optional[Any] = tmp_path / '''cache'''
__magic_name__ : Optional[int] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__magic_name__ : Optional[int] = features.copy() if features else default_expected_features
__magic_name__ : str = (
Features({feature: Value(_a ) for feature, dtype in features.items()} ) if features is not None else None
)
__magic_name__ : Optional[Any] = JsonDatasetReader(_a , features=_a , cache_dir=_a ).read()
_check_json_dataset(_a , _a )
@pytest.mark.parametrize(
'features' , [
None,
{'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
] , )
def lowerCamelCase ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : int ):
"""simple docstring"""
__magic_name__ : List[str] = tmp_path / '''cache'''
__magic_name__ : Tuple = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
__magic_name__ : str = features.copy() if features else default_expected_features
__magic_name__ : Dict = (
Features({feature: Value(_a ) for feature, dtype in features.items()} ) if features is not None else None
)
__magic_name__ : Any = JsonDatasetReader(_a , features=_a , cache_dir=_a ).read()
assert isinstance(_a , _a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def lowerCamelCase ( lowerCAmelCase : List[str] , lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
__magic_name__ : Tuple = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
__magic_name__ : List[str] = features.copy()
__magic_name__ : str = (
Features({feature: Value(_a ) for feature, dtype in features.items()} ) if features is not None else None
)
__magic_name__ : int = tmp_path / '''cache'''
__magic_name__ : Optional[int] = JsonDatasetReader(_a , features=_a , cache_dir=_a ).read()
assert isinstance(_a , _a )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def lowerCamelCase ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : str , lowerCAmelCase : List[str] ):
"""simple docstring"""
__magic_name__ : Optional[int] = tmp_path / '''cache'''
__magic_name__ : List[str] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__magic_name__ : Optional[int] = JsonDatasetReader(_a , cache_dir=_a , split=_a ).read()
_check_json_dataset(_a , _a )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' , [str, list] )
def lowerCamelCase ( lowerCAmelCase : Dict , lowerCAmelCase : Tuple , lowerCAmelCase : int ):
"""simple docstring"""
if issubclass(_a , _a ):
__magic_name__ : Optional[int] = jsonl_path
elif issubclass(_a , _a ):
__magic_name__ : Optional[Any] = [jsonl_path]
__magic_name__ : int = tmp_path / '''cache'''
__magic_name__ : int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__magic_name__ : Tuple = JsonDatasetReader(_a , cache_dir=_a ).read()
_check_json_dataset(_a , _a )
def lowerCamelCase ( lowerCAmelCase : Optional[int] , lowerCAmelCase : int , lowerCAmelCase : Union[str, Any]=("train",) ):
"""simple docstring"""
assert isinstance(_a , _a )
for split in splits:
__magic_name__ : str = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def lowerCamelCase ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : List[str] , lowerCAmelCase : Optional[int] ):
"""simple docstring"""
__magic_name__ : List[str] = tmp_path / '''cache'''
__magic_name__ : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__magic_name__ : Optional[Any] = JsonDatasetReader({'train': jsonl_path} , cache_dir=_a , keep_in_memory=_a ).read()
_check_json_datasetdict(_a , _a )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def lowerCamelCase ( lowerCAmelCase : Dict , lowerCAmelCase : List[Any] , lowerCAmelCase : Optional[int] ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = tmp_path / '''cache'''
__magic_name__ : str = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__magic_name__ : List[Any] = features.copy() if features else default_expected_features
__magic_name__ : Dict = (
Features({feature: Value(_a ) for feature, dtype in features.items()} ) if features is not None else None
)
__magic_name__ : str = JsonDatasetReader({'train': jsonl_path} , features=_a , cache_dir=_a ).read()
_check_json_datasetdict(_a , _a )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def lowerCamelCase ( lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : List[Any] ):
"""simple docstring"""
if split:
__magic_name__ : List[str] = {split: jsonl_path}
else:
__magic_name__ : List[str] = '''train'''
__magic_name__ : Optional[int] = {'''train''': jsonl_path, '''test''': jsonl_path}
__magic_name__ : List[Any] = tmp_path / '''cache'''
__magic_name__ : Any = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
__magic_name__ : int = JsonDatasetReader(_a , cache_dir=_a ).read()
_check_json_datasetdict(_a , _a , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCamelCase ( lowerCAmelCase : Any ):
"""simple docstring"""
return json.load(_a )
def lowerCamelCase ( lowerCAmelCase : Any ):
"""simple docstring"""
return [json.loads(_a ) for line in buffer]
class _lowerCamelCase :
'''simple docstring'''
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def __lowerCAmelCase ( self : List[str] , _A : Any , _A : List[Any] , _A : Optional[int] ) -> List[str]:
with io.BytesIO() as buffer:
JsonDatasetWriter(lowercase_ , lowercase_ , lines=lowercase_ ).write()
buffer.seek(0 )
__magic_name__ : List[str] = load_json_function(lowercase_ )
assert isinstance(lowercase_ , lowercase_ )
assert isinstance(exported_content[0] , lowercase_ )
assert len(lowercase_ ) == 10
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def __lowerCAmelCase ( self : str , _A : Dict , _A : Union[str, Any] , _A : Union[str, Any] , _A : Optional[int] , _A : Dict ) -> int:
with io.BytesIO() as buffer:
JsonDatasetWriter(lowercase_ , lowercase_ , lines=lowercase_ , orient=lowercase_ ).write()
buffer.seek(0 )
__magic_name__ : str = load_json(lowercase_ )
assert isinstance(lowercase_ , lowercase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowercase_ , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(lowercase_ ) == 10
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def __lowerCAmelCase ( self : str , _A : Tuple , _A : Optional[Any] , _A : Union[str, Any] ) -> Optional[Any]:
with io.BytesIO() as buffer:
JsonDatasetWriter(lowercase_ , lowercase_ , lines=lowercase_ , num_proc=2 ).write()
buffer.seek(0 )
__magic_name__ : Optional[Any] = load_json_function(lowercase_ )
assert isinstance(lowercase_ , lowercase_ )
assert isinstance(exported_content[0] , lowercase_ )
assert len(lowercase_ ) == 10
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def __lowerCAmelCase ( self : List[Any] , _A : Union[str, Any] , _A : List[str] , _A : List[Any] , _A : List[str] , _A : str ) -> List[str]:
with io.BytesIO() as buffer:
JsonDatasetWriter(lowercase_ , lowercase_ , lines=lowercase_ , orient=lowercase_ , num_proc=2 ).write()
buffer.seek(0 )
__magic_name__ : str = load_json(lowercase_ )
assert isinstance(lowercase_ , lowercase_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowercase_ , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(lowercase_ ) == 10
def __lowerCAmelCase ( self : str , _A : Optional[Any] ) -> int:
with pytest.raises(lowercase_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowercase_ , lowercase_ , num_proc=0 )
@pytest.mark.parametrize('compression, extension' , [('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')] )
def __lowerCAmelCase ( self : Optional[Any] , _A : int , _A : List[str] , _A : Tuple , _A : Union[str, Any] , _A : Optional[int] ) -> int:
__magic_name__ : Any = tmp_path_factory.mktemp('data' ) / F'test.json.{extension}'
__magic_name__ : Tuple = str(shared_datadir / F'test_file.json.{extension}' )
JsonDatasetWriter(lowercase_ , lowercase_ , compression=lowercase_ ).write()
with fsspec.open(lowercase_ , 'rb' , compression='infer' ) as f:
__magic_name__ : Optional[Any] = f.read()
with fsspec.open(lowercase_ , 'rb' , compression='infer' ) as f:
__magic_name__ : Optional[Any] = f.read()
assert exported_content == original_content | 331 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : str = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
lowercase__ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : int = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class a ( lowerCAmelCase__ ):
UpperCAmelCase_ : List[Any] ="""gpt_neox"""
def __init__( self , _lowerCamelCase=5_0_4_3_2 , _lowerCamelCase=6_1_4_4 , _lowerCamelCase=4_4 , _lowerCamelCase=6_4 , _lowerCamelCase=2_4_5_7_6 , _lowerCamelCase="gelu" , _lowerCamelCase=0.2_5 , _lowerCamelCase=1_0_0_0_0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.1 , _lowerCamelCase=2_0_4_8 , _lowerCamelCase=0.0_2 , _lowerCamelCase=1e-5 , _lowerCamelCase=True , _lowerCamelCase=0 , _lowerCamelCase=2 , _lowerCamelCase=False , _lowerCamelCase=True , _lowerCamelCase=None , **_lowerCamelCase , ):
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
lowercase = vocab_size
lowercase = max_position_embeddings
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = rotary_pct
lowercase = rotary_emb_base
lowercase = attention_dropout
lowercase = hidden_dropout
lowercase = classifier_dropout
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = use_cache
lowercase = tie_word_embeddings
lowercase = use_parallel_residual
lowercase = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'The hidden size is not divisble by the number of attention heads! Make sure to update them!' )
def UpperCamelCase_ ( self ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , lowercase_ ) 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}' )
lowercase = self.rope_scaling.get('type' , lowercase_ )
lowercase = self.rope_scaling.get('factor' , lowercase_ )
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(lowercase_ , lowercase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 220 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : str = XLMRobertaTokenizer
_lowerCAmelCase : int = XLMRobertaTokenizerFast
_lowerCAmelCase : str = True
_lowerCAmelCase : Dict = True
def _snake_case ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ : List[str] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def _snake_case ( self : str ):
snake_case_ : List[Any] = '''<pad>'''
snake_case_ : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def _snake_case ( self : Union[str, Any] ):
snake_case_ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowercase_ ) , 1002 )
def _snake_case ( self : Union[str, Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def _snake_case ( self : Dict ):
snake_case_ : Optional[Any] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
snake_case_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowercase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
snake_case_ : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
snake_case_ : List[Any] = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _snake_case ( self : List[str] ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
snake_case_ : int = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : int = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : Tuple = tokenizer_r.save_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
snake_case_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : Union[str, Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[str] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : List[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[Any] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : Tuple = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
snake_case_ : Optional[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : Dict = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@cached_property
def _snake_case ( self : List[str] ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _snake_case ( self : Optional[Any] ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowercase_ , f.name )
snake_case_ : Any = XLMRobertaTokenizer(f.name , keep_accents=lowercase_ )
snake_case_ : List[Any] = pickle.dumps(lowercase_ )
pickle.loads(lowercase_ )
def _snake_case ( self : Tuple ):
if not self.test_rust_tokenizer:
return
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ : Optional[int] = self.get_rust_tokenizer()
snake_case_ : Dict = '''I was born in 92000, and this is falsé.'''
snake_case_ : Optional[int] = tokenizer.tokenize(lowercase_ )
snake_case_ : Tuple = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : List[str] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
snake_case_ : str = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ : Any = tokenizer.encode(lowercase_ )
snake_case_ : int = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
@slow
def _snake_case ( self : Tuple ):
snake_case_ : int = '''Hello World!'''
snake_case_ : int = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : List[Any] ):
snake_case_ : Any = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
snake_case_ : Optional[int] = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : Dict ):
# fmt: off
snake_case_ : int = {'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 264 | 0 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ )-> Any:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_a ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(_a ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod() | 262 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[Any] = """gpt_neox"""
def __init__( self : List[str] , lowercase_ : str=50432 , lowercase_ : List[Any]=6144 , lowercase_ : List[Any]=44 , lowercase_ : Union[str, Any]=64 , lowercase_ : List[str]=24576 , lowercase_ : List[Any]="gelu" , lowercase_ : str=0.25 , lowercase_ : Optional[int]=10000 , lowercase_ : Optional[int]=0.0 , lowercase_ : Optional[int]=0.0 , lowercase_ : int=0.1 , lowercase_ : Tuple=2048 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : List[str]=1E-5 , lowercase_ : str=True , lowercase_ : str=0 , lowercase_ : Union[str, Any]=2 , lowercase_ : List[str]=False , lowercase_ : Optional[int]=True , lowercase_ : List[Any]=None , **lowercase_ : Optional[int] , ):
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : Dict = num_attention_heads
snake_case_ : List[Any] = intermediate_size
snake_case_ : List[Any] = hidden_act
snake_case_ : str = rotary_pct
snake_case_ : Dict = rotary_emb_base
snake_case_ : Optional[int] = attention_dropout
snake_case_ : Tuple = hidden_dropout
snake_case_ : Tuple = classifier_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Any = use_cache
snake_case_ : Optional[int] = tie_word_embeddings
snake_case_ : Any = use_parallel_residual
snake_case_ : Union[str, Any] = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def _snake_case ( self : Optional[int] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , lowercase_ ) 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}" )
snake_case_ : Any = self.rope_scaling.get('''type''' , lowercase_ )
snake_case_ : Union[str, Any] = self.rope_scaling.get('''factor''' , lowercase_ )
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(lowercase_ , lowercase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 264 | 0 |
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : str = name
UpperCAmelCase : List[Any] = value
UpperCAmelCase : Union[str, Any] = weight
def __repr__( self ) -> int:
'''simple docstring'''
return F"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
return self.value
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
return self.name
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
return self.weight
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
return self.value / self.weight
def _snake_case ( UpperCamelCase : Union[str, Any] , UpperCamelCase : List[str] , UpperCamelCase : List[Any] ):
UpperCAmelCase : Union[str, Any] = []
for i in range(len(_a ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _snake_case ( UpperCamelCase : Union[str, Any] , UpperCamelCase : Any , UpperCamelCase : Dict ):
UpperCAmelCase : Union[str, Any] = sorted(_a , key=_a , reverse=_a )
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Any = 0.0, 0.0
for i in range(len(_a ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _snake_case ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 109 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowercase__ : int = None
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : List[str] = '''▁'''
lowercase__ : Optional[int] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
lowercase__ : str = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
'''tokenizer_file''': {
'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
},
}
lowercase__ : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
_lowerCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Tuple = PegasusTokenizer
_lowerCAmelCase : str = ["""input_ids""", """attention_mask"""]
def __init__( self : Any , lowercase_ : Optional[Any]=None , lowercase_ : int=None , lowercase_ : Tuple="<pad>" , lowercase_ : int="</s>" , lowercase_ : Tuple="<unk>" , lowercase_ : str="<mask_2>" , lowercase_ : Optional[Any]="<mask_1>" , lowercase_ : str=None , lowercase_ : List[str]=103 , **lowercase_ : List[Any] , ):
snake_case_ : Dict = offset
if additional_special_tokens is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError(
f"additional_special_tokens should be of type {type(lowercase_ )}, but is"
f" {type(lowercase_ )}" )
snake_case_ : str = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f"<unk_{i}>" for i in range(len(lowercase_ ) , self.offset - 1 )
]
if len(set(lowercase_ ) ) != len(lowercase_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}." )
snake_case_ : Union[str, Any] = additional_special_tokens_extended
else:
snake_case_ : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f"<unk_{i}>" for i in range(2 , self.offset )]
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , pad_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , mask_token=lowercase_ , mask_token_sent=lowercase_ , offset=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
snake_case_ : List[Any] = vocab_file
snake_case_ : List[Any] = False if not self.vocab_file else True
def _snake_case ( self : str , lowercase_ : Union[str, Any] ):
snake_case_ : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
f" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}" )
return [1 if x in all_special_ids else 0 for x in seq]
def _snake_case ( self : int , lowercase_ : List , lowercase_ : Optional[List] = None , lowercase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(lowercase_ )
elif token_ids_a is None:
return self._special_token_mask(lowercase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _snake_case ( self : List[Any] , lowercase_ : Optional[int] , lowercase_ : str=None ):
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 _snake_case ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ : Dict = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
return (out_vocab_file,)
| 264 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class snake_case ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCamelCase__ : str ,lowerCamelCase__ : int ):
UpperCAmelCase__ = params
UpperCAmelCase__ = np.array(lowercase_ )
UpperCAmelCase__ = np.array([len(lowercase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : Dict ,lowerCamelCase__ : Union[str, Any] ):
return (self.token_ids[index], self.lengths[index])
def __len__( self : List[Any] ):
return len(self.lengths )
def __lowerCAmelCase ( self : Tuple ):
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def __lowerCAmelCase ( self : Tuple ):
UpperCAmelCase__ = self.params.max_model_input_size
UpperCAmelCase__ = self.lengths > max_len
logger.info(f'''Splitting {sum(lowercase_ )} too long sequences.''' )
def divide_chunks(lowerCamelCase__ : Tuple ,lowerCamelCase__ : Optional[Any] ):
return [l[i : i + n] for i in range(0 ,len(lowercase_ ) ,lowercase_ )]
UpperCAmelCase__ = []
UpperCAmelCase__ = []
if self.params.mlm:
UpperCAmelCase__ = self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
else:
UpperCAmelCase__ = self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']
for seq_, len_ in zip(self.token_ids ,self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
UpperCAmelCase__ = []
for sub_s in divide_chunks(seq_ ,max_len - 2 ):
if sub_s[0] != cls_id:
UpperCAmelCase__ = np.insert(lowercase_ ,0 ,lowercase_ )
if sub_s[-1] != sep_id:
UpperCAmelCase__ = np.insert(lowercase_ ,len(lowercase_ ) ,lowercase_ )
assert len(lowercase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowercase_ )
new_tok_ids.extend(lowercase_ )
new_lengths.extend([len(lowercase_ ) for l in sub_seqs] )
UpperCAmelCase__ = np.array(lowercase_ )
UpperCAmelCase__ = np.array(lowercase_ )
def __lowerCAmelCase ( self : Optional[int] ):
UpperCAmelCase__ = len(self )
UpperCAmelCase__ = self.lengths > 11
UpperCAmelCase__ = self.token_ids[indices]
UpperCAmelCase__ = self.lengths[indices]
UpperCAmelCase__ = len(self )
logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def __lowerCAmelCase ( self : Tuple ):
if "unk_token" not in self.params.special_tok_ids:
return
else:
UpperCAmelCase__ = self.params.special_tok_ids['''unk_token''']
UpperCAmelCase__ = len(self )
UpperCAmelCase__ = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
UpperCAmelCase__ = (unk_occs / self.lengths) < 0.5
UpperCAmelCase__ = self.token_ids[indices]
UpperCAmelCase__ = self.lengths[indices]
UpperCAmelCase__ = len(self )
logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def __lowerCAmelCase ( self : Dict ):
if not self.params.is_master:
return
logger.info(f'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def __lowerCAmelCase ( self : List[str] ,lowerCamelCase__ : Dict ):
UpperCAmelCase__ = [t[0] for t in batch]
UpperCAmelCase__ = [t[1] for t in batch]
assert len(lowercase_ ) == len(lowercase_ )
# Max for paddings
UpperCAmelCase__ = max(lowercase_ )
# Pad token ids
if self.params.mlm:
UpperCAmelCase__ = self.params.special_tok_ids['''pad_token''']
else:
UpperCAmelCase__ = self.params.special_tok_ids['''unk_token''']
UpperCAmelCase__ = [list(t.astype(lowercase_ ) ) + [pad_idx] * (max_seq_len_ - len(lowercase_ )) for t in token_ids]
assert len(tk_ ) == len(lowercase_ )
assert all(len(lowercase_ ) == max_seq_len_ for t in tk_ )
UpperCAmelCase__ = torch.tensor(tk_ ) # (bs, max_seq_len_)
UpperCAmelCase__ = torch.tensor(lowercase_ ) # (bs)
return tk_t, lg_t
| 98 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int=13 , lowercase_ : Optional[int]=7 , lowercase_ : Any=True , lowercase_ : Dict=True , lowercase_ : Dict=True , lowercase_ : Optional[Any]=99 , lowercase_ : Union[str, Any]=32 , lowercase_ : str=5 , lowercase_ : Union[str, Any]=4 , lowercase_ : Any=37 , lowercase_ : Tuple="gelu" , lowercase_ : Dict=0.1 , lowercase_ : Tuple=0.1 , lowercase_ : Optional[int]=512 , lowercase_ : Optional[Any]=16 , lowercase_ : Optional[Any]=2 , lowercase_ : Optional[Any]=0.02 , lowercase_ : List[Any]=3 , lowercase_ : Union[str, Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ : Any = parent
snake_case_ : List[str] = batch_size
snake_case_ : List[Any] = seq_length
snake_case_ : Optional[int] = is_training
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Optional[Any] = use_labels
snake_case_ : Union[str, Any] = vocab_size
snake_case_ : Any = hidden_size
snake_case_ : List[Any] = num_hidden_layers
snake_case_ : Any = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : Tuple = max_position_embeddings
snake_case_ : int = type_vocab_size
snake_case_ : Tuple = type_sequence_label_size
snake_case_ : str = initializer_range
snake_case_ : Tuple = num_labels
snake_case_ : str = num_choices
snake_case_ : Any = scope
snake_case_ : Dict = self.vocab_size - 1
def _snake_case ( self : int ):
snake_case_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Optional[Any] = None
if self.use_token_type_ids:
snake_case_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : str = None
snake_case_ : Dict = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ : int = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
snake_case_ : Any = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _snake_case ( self : Tuple , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , *lowercase_ : Dict ):
snake_case_ : List[Any] = OpenAIGPTModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Any = model(lowercase_ , token_type_ids=lowercase_ , head_mask=lowercase_ )
snake_case_ : Optional[Any] = model(lowercase_ , token_type_ids=lowercase_ )
snake_case_ : Optional[Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : List[Any] , *lowercase_ : Optional[Any] ):
snake_case_ : Union[str, Any] = OpenAIGPTLMHeadModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Union[str, Any] = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : List[str] , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Dict , *lowercase_ : Union[str, Any] ):
snake_case_ : Tuple = OpenAIGPTDoubleHeadsModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Dict = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : Any , lowercase_ : str , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , *lowercase_ : Any ):
snake_case_ : int = self.num_labels
snake_case_ : Any = OpenAIGPTForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Optional[Any] = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : int ):
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : str = config_and_inputs
snake_case_ : str = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : Dict = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_lowerCAmelCase : int = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_lowerCAmelCase : Union[str, Any] = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _snake_case ( self : Tuple , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _snake_case ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str]=False ):
snake_case_ : Dict = super()._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowercase_ , )
snake_case_ : int = inputs_dict['''labels''']
snake_case_ : Optional[Any] = inputs_dict['''labels''']
snake_case_ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowercase_ , )
snake_case_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase_ )
return inputs_dict
def _snake_case ( self : Any ):
snake_case_ : List[str] = OpenAIGPTModelTester(self )
snake_case_ : Dict = ConfigTester(self , config_class=lowercase_ , n_embd=37 )
def _snake_case ( self : List[str] ):
self.config_tester.run_common_tests()
def _snake_case ( self : Optional[Any] ):
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowercase_ )
def _snake_case ( self : List[str] ):
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowercase_ )
def _snake_case ( self : int ):
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowercase_ )
def _snake_case ( self : List[str] ):
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_ )
@slow
def _snake_case ( self : Dict ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Optional[Any] = OpenAIGPTModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
@slow
def _snake_case ( self : Optional[int] ):
snake_case_ : Optional[Any] = OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
model.to(lowercase_ )
snake_case_ : List[str] = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=lowercase_ ) # the president is
snake_case_ : List[Any] = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
snake_case_ : Optional[Any] = model.generate(lowercase_ , do_sample=lowercase_ )
self.assertListEqual(output_ids[0].tolist() , lowercase_ )
| 264 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase__ ):
lowercase = 42
lowercase = 42
def __init__( self : Dict , snake_case : UNetaDModel , snake_case : ScoreSdeVeScheduler ) -> Optional[int]:
"""simple docstring"""
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : Tuple , snake_case : int = 1 , snake_case : int = 2_0_0_0 , snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case : Optional[str] = "pil" , snake_case : bool = True , **snake_case : int , ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : List[str] = self.unet.config.sample_size
UpperCamelCase_ : Union[str, Any] = (batch_size, 3, img_size, img_size)
UpperCamelCase_ : List[Any] = self.unet
UpperCamelCase_ : List[Any] = randn_tensor(lowercase_ , generator=lowercase_ ) * self.scheduler.init_noise_sigma
UpperCamelCase_ : List[Any] = sample.to(self.device )
self.scheduler.set_timesteps(lowercase_ )
self.scheduler.set_sigmas(lowercase_ )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase_ : List[str] = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
UpperCamelCase_ : Tuple = self.unet(lowercase_ , lowercase_ ).sample
UpperCamelCase_ : Tuple = self.scheduler.step_correct(lowercase_ , lowercase_ , generator=lowercase_ ).prev_sample
# prediction step
UpperCamelCase_ : Union[str, Any] = model(lowercase_ , lowercase_ ).sample
UpperCamelCase_ : List[Any] = self.scheduler.step_pred(lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ )
UpperCamelCase_ : List[str] = output.prev_sample, output.prev_sample_mean
UpperCamelCase_ : int = sample_mean.clamp(0 , 1 )
UpperCamelCase_ : List[Any] = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCamelCase_ : Any = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=lowercase_ )
| 175 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Any , lowercase_ : TransformeraDModel , lowercase_ : AutoencoderKL , lowercase_ : KarrasDiffusionSchedulers , lowercase_ : Optional[Dict[int, str]] = None , ):
super().__init__()
self.register_modules(transformer=lowercase_ , vae=lowercase_ , scheduler=lowercase_ )
# create a imagenet -> id dictionary for easier use
snake_case_ : Tuple = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''',''' ):
snake_case_ : str = int(lowercase_ )
snake_case_ : Any = dict(sorted(self.labels.items() ) )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, List[str]] ):
if not isinstance(lowercase_ , lowercase_ ):
snake_case_ : Tuple = list(lowercase_ )
for l in label:
if l not in self.labels:
raise ValueError(
f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}." )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self : Optional[int] , lowercase_ : List[int] , lowercase_ : float = 4.0 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : int = 50 , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , ):
snake_case_ : Any = len(lowercase_ )
snake_case_ : List[str] = self.transformer.config.sample_size
snake_case_ : Union[str, Any] = self.transformer.config.in_channels
snake_case_ : str = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowercase_ , device=self.device , dtype=self.transformer.dtype , )
snake_case_ : Optional[Any] = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
snake_case_ : Optional[int] = torch.tensor(lowercase_ , device=self.device ).reshape(-1 )
snake_case_ : Dict = torch.tensor([1000] * batch_size , device=self.device )
snake_case_ : Tuple = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
snake_case_ : List[Any] = latent_model_input[: len(lowercase_ ) // 2]
snake_case_ : Union[str, Any] = torch.cat([half, half] , dim=0 )
snake_case_ : Optional[Any] = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
snake_case_ : int = t
if not torch.is_tensor(lowercase_ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
snake_case_ : Tuple = latent_model_input.device.type == '''mps'''
if isinstance(lowercase_ , lowercase_ ):
snake_case_ : List[str] = torch.floataa if is_mps else torch.floataa
else:
snake_case_ : Optional[int] = torch.intaa if is_mps else torch.intaa
snake_case_ : List[Any] = torch.tensor([timesteps] , dtype=lowercase_ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
snake_case_ : str = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
snake_case_ : Tuple = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
snake_case_ : List[Any] = self.transformer(
lowercase_ , timestep=lowercase_ , class_labels=lowercase_ ).sample
# perform guidance
if guidance_scale > 1:
snake_case_, snake_case_ : Dict = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
snake_case_, snake_case_ : Any = torch.split(lowercase_ , len(lowercase_ ) // 2 , dim=0 )
snake_case_ : int = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
snake_case_ : str = torch.cat([half_eps, half_eps] , dim=0 )
snake_case_ : List[Any] = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
snake_case_, snake_case_ : Optional[Any] = torch.split(lowercase_ , lowercase_ , dim=1 )
else:
snake_case_ : List[str] = noise_pred
# compute previous image: x_t -> x_t-1
snake_case_ : int = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
if guidance_scale > 1:
snake_case_, snake_case_ : Optional[Any] = latent_model_input.chunk(2 , dim=0 )
else:
snake_case_ : Dict = latent_model_input
snake_case_ : Union[str, Any] = 1 / self.vae.config.scaling_factor * latents
snake_case_ : Tuple = self.vae.decode(lowercase_ ).sample
snake_case_ : str = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ : Union[str, Any] = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case_ : Union[str, Any] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowercase_ )
| 264 | 0 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 215 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self : List[Any] ):
snake_case_ : List[str] = ''''''
snake_case_ : Tuple = ''''''
snake_case_ : int = []
snake_case_ : Optional[int] = 0
snake_case_ : Optional[Any] = 256
snake_case_ : Tuple = 0
snake_case_ : Tuple = 0
snake_case_ : Optional[Any] = 0
snake_case_ : Any = 0
def _snake_case ( self : Optional[Any] , lowercase_ : List[Any] ):
snake_case_ : List[Any] = cva.imread(lowercase_ , 0 )
snake_case_ : Tuple = copy.deepcopy(self.img )
snake_case_, snake_case_, snake_case_ : List[Any] = plt.hist(self.img.ravel() , 256 , [0, 256] , label='''x''' )
snake_case_ : str = np.sum(lowercase_ )
for i in range(len(lowercase_ ) ):
snake_case_ : Optional[Any] = x[i] / self.k
self.sk += prk
snake_case_ : Any = (self.L - 1) * self.sk
if self.rem != 0:
snake_case_ : Dict = int(last % last )
snake_case_ : Union[str, Any] = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowercase_ )
snake_case_ : int = int(np.ma.count(self.img ) / self.img[1].size )
snake_case_ : Tuple = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
snake_case_ : Union[str, Any] = self.img[j][i]
if num != self.last_list[num]:
snake_case_ : List[str] = self.last_list[num]
cva.imwrite('''output_data/output.jpg''' , self.img )
def _snake_case ( self : Tuple ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _snake_case ( self : int ):
cva.imshow('''Output-Image''' , self.img )
cva.imshow('''Input-Image''' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowercase__ : Any = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
lowercase__ : Any = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 264 | 0 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = []
for part_id in partition_order:
_lowerCAmelCase = df.where(f"SPARK_PARTITION_ID() = {part_id}" ).collect()
for row_idx, row in enumerate(_a ):
expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(1_00 ).repartition(1 )
_lowerCAmelCase = Spark(_a )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(10 ).repartition(2 )
_lowerCAmelCase = [1, 0]
_lowerCAmelCase = _generate_iterable_examples(_a , _a ) # Reverse the partitions.
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , _a )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
_lowerCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(10 ).repartition(1 )
_lowerCAmelCase = SparkExamplesIterable(_a )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_a ):
assert row_id == f"0_{i}"
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
_lowerCAmelCase = lambda lowerCAmelCase : x.reverse()
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [2, 1, 0] )
_lowerCAmelCase = SparkExamplesIterable(_a ).shuffle_data_sources(_a )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_a ):
_lowerCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
_lowerCAmelCase = SparkExamplesIterable(_a ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [0, 2] )
for i, (row_id, row_dict) in enumerate(_a ):
_lowerCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
_lowerCAmelCase = SparkExamplesIterable(_a ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [1, 3] )
for i, (row_id, row_dict) in enumerate(_a ):
_lowerCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(1_00 ).repartition(1 )
_lowerCAmelCase = Spark(_a )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_00
| 70 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] ):
snake_case_ : str = []
def _snake_case ( self : List[Any] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : List[str] , **lowercase_ : Tuple ):
self.events.append('''on_init_end''' )
def _snake_case ( self : List[Any] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_train_begin''' )
def _snake_case ( self : Any , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_train_end''' )
def _snake_case ( self : str , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any] , **lowercase_ : List[Any] ):
self.events.append('''on_epoch_begin''' )
def _snake_case ( self : Tuple , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ):
self.events.append('''on_epoch_end''' )
def _snake_case ( self : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , **lowercase_ : Optional[Any] ):
self.events.append('''on_step_begin''' )
def _snake_case ( self : int , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , **lowercase_ : List[str] ):
self.events.append('''on_step_end''' )
def _snake_case ( self : str , lowercase_ : int , lowercase_ : Dict , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_evaluate''' )
def _snake_case ( self : Dict , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : List[Any] , **lowercase_ : str ):
self.events.append('''on_predict''' )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int , **lowercase_ : Union[str, Any] ):
self.events.append('''on_save''' )
def _snake_case ( self : str , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : Any ):
self.events.append('''on_log''' )
def _snake_case ( self : Dict , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Union[str, Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_prediction_step''' )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : List[str] ):
snake_case_ : Tuple = tempfile.mkdtemp()
def _snake_case ( self : Tuple ):
shutil.rmtree(self.output_dir )
def _snake_case ( self : int , lowercase_ : Union[str, Any]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=64 , lowercase_ : Union[str, Any]=64 , lowercase_ : Union[str, Any]=None , lowercase_ : Any=False , **lowercase_ : List[Any] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
snake_case_ : int = RegressionDataset(length=lowercase_ )
snake_case_ : Any = RegressionDataset(length=lowercase_ )
snake_case_ : int = RegressionModelConfig(a=lowercase_ , b=lowercase_ )
snake_case_ : Tuple = RegressionPreTrainedModel(lowercase_ )
snake_case_ : Any = TrainingArguments(self.output_dir , disable_tqdm=lowercase_ , report_to=[] , **lowercase_ )
return Trainer(
lowercase_ , lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , callbacks=lowercase_ , )
def _snake_case ( self : Optional[int] , lowercase_ : Any , lowercase_ : List[Any] ):
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
# Order doesn't matter
snake_case_ : Any = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
snake_case_ : List[str] = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
for cba, cba in zip(lowercase_ , lowercase_ ):
if isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , lowercase_ )
elif isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , cba.__class__ )
elif not isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(cba.__class__ , lowercase_ )
else:
self.assertEqual(lowercase_ , lowercase_ )
def _snake_case ( self : Optional[Any] , lowercase_ : Tuple ):
snake_case_ : Tuple = ['''on_init_end''', '''on_train_begin''']
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = len(trainer.get_eval_dataloader() )
snake_case_ : List[Any] = ['''on_prediction_step'''] * len(trainer.get_eval_dataloader() ) + ['''on_log''', '''on_evaluate''']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('''on_epoch_begin''' )
for _ in range(lowercase_ ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('''on_log''' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('''on_save''' )
expected_events.append('''on_epoch_end''' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _snake_case ( self : List[str] ):
snake_case_ : Union[str, Any] = self.get_trainer()
snake_case_ : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# Callbacks passed at init are added to the default callbacks
snake_case_ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
snake_case_ : Optional[int] = self.get_trainer(disable_tqdm=lowercase_ )
snake_case_ : List[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : int ):
snake_case_ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
snake_case_ : List[Any] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : Dict = self.get_trainer()
snake_case_ : Optional[int] = trainer.pop_callback(lowercase_ )
self.assertEqual(cb.__class__ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# We can also add, pop, or remove by instance
snake_case_ : Optional[int] = self.get_trainer()
snake_case_ : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : List[Any] = self.get_trainer()
snake_case_ : Optional[int] = trainer.callback_handler.callbacks[0]
snake_case_ : Optional[Any] = trainer.pop_callback(lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : List[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='''ignore''' , category=lowercase_ )
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# Independent log/save/eval
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
snake_case_ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='''steps''' )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='''epoch''' )
trainer.train()
snake_case_ : Dict = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# A bit of everything
snake_case_ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy='''steps''' , )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# warning should be emitted for duplicated callbacks
with patch('''transformers.trainer_callback.logger.warning''' ) as warn_mock:
snake_case_ : Dict = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(lowercase_ ) in warn_mock.call_args[0][0]
| 264 | 0 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : List[str] = prime_factors(_a )
if is_square_free(_a ):
return -1 if len(_a ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 |
"""simple docstring"""
import numpy as np
def __lowercase ( _a ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
__snake_case : Tuple = logging.get_logger(__name__)
class A__(lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self , *_lowercase , **_lowercase ) -> Optional[int]:
warnings.warn(
"""The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DonutImageProcessor instead.""" , lowercase_ , )
super().__init__(*lowercase_ , **lowercase_ )
| 248 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] , lowercase_ : str , lowercase_ : int ):
snake_case_ : Dict = params
snake_case_ : Union[str, Any] = np.array(lowercase_ )
snake_case_ : str = np.array([len(lowercase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : Dict , lowercase_ : Union[str, Any] ):
return (self.token_ids[index], self.lengths[index])
def __len__( self : List[Any] ):
return len(self.lengths )
def _snake_case ( self : Tuple ):
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def _snake_case ( self : Tuple ):
snake_case_ : str = self.params.max_model_input_size
snake_case_ : Dict = self.lengths > max_len
logger.info(f"Splitting {sum(lowercase_ )} too long sequences." )
def divide_chunks(lowercase_ : Tuple , lowercase_ : Optional[Any] ):
return [l[i : i + n] for i in range(0 , len(lowercase_ ) , lowercase_ )]
snake_case_ : Tuple = []
snake_case_ : Any = []
if self.params.mlm:
snake_case_, snake_case_ : Union[str, Any] = self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
else:
snake_case_, snake_case_ : Dict = self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
snake_case_ : Any = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
snake_case_ : Dict = np.insert(lowercase_ , 0 , lowercase_ )
if sub_s[-1] != sep_id:
snake_case_ : Tuple = np.insert(lowercase_ , len(lowercase_ ) , lowercase_ )
assert len(lowercase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowercase_ )
new_tok_ids.extend(lowercase_ )
new_lengths.extend([len(lowercase_ ) for l in sub_seqs] )
snake_case_ : List[str] = np.array(lowercase_ )
snake_case_ : Optional[Any] = np.array(lowercase_ )
def _snake_case ( self : Optional[int] ):
snake_case_ : List[Any] = len(self )
snake_case_ : List[str] = self.lengths > 11
snake_case_ : Dict = self.token_ids[indices]
snake_case_ : Dict = self.lengths[indices]
snake_case_ : str = len(self )
logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def _snake_case ( self : Tuple ):
if "unk_token" not in self.params.special_tok_ids:
return
else:
snake_case_ : str = self.params.special_tok_ids['''unk_token''']
snake_case_ : str = len(self )
snake_case_ : int = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
snake_case_ : str = (unk_occs / self.lengths) < 0.5
snake_case_ : Optional[Any] = self.token_ids[indices]
snake_case_ : Optional[int] = self.lengths[indices]
snake_case_ : Dict = len(self )
logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def _snake_case ( self : Dict ):
if not self.params.is_master:
return
logger.info(f"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def _snake_case ( self : List[str] , lowercase_ : Dict ):
snake_case_ : Optional[int] = [t[0] for t in batch]
snake_case_ : str = [t[1] for t in batch]
assert len(lowercase_ ) == len(lowercase_ )
# Max for paddings
snake_case_ : str = max(lowercase_ )
# Pad token ids
if self.params.mlm:
snake_case_ : Tuple = self.params.special_tok_ids['''pad_token''']
else:
snake_case_ : Dict = self.params.special_tok_ids['''unk_token''']
snake_case_ : Any = [list(t.astype(lowercase_ ) ) + [pad_idx] * (max_seq_len_ - len(lowercase_ )) for t in token_ids]
assert len(tk_ ) == len(lowercase_ )
assert all(len(lowercase_ ) == max_seq_len_ for t in tk_ )
snake_case_ : str = torch.tensor(tk_ ) # (bs, max_seq_len_)
snake_case_ : Optional[int] = torch.tensor(lowercase_ ) # (bs)
return tk_t, lg_t
| 264 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowercase_ ( unittest.TestCase , lowerCAmelCase__ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
lowerCAmelCase = load_tool('''text-to-speech''' )
self.tool.setup()
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
lowerCAmelCase = self.tool('''hey''' )
lowerCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
lowerCAmelCase = self.tool('''hey''' )
lowerCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ) , ) )
| 338 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __lowercase ( _a , _a , _a = "x" , _a = 10**-10 , _a = 1 , ):
snake_case_ : Any = symbols(_a )
snake_case_ : int = lambdify(_a , _a )
snake_case_ : Optional[Any] = lambdify(_a , diff(_a , _a ) )
snake_case_ : Optional[Any] = starting_point
while True:
if diff_function(_a ) != 0:
snake_case_ : Optional[int] = prev_guess - multiplicity * func(_a ) / diff_function(
_a )
else:
raise ZeroDivisionError('''Could not find root''' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
snake_case_ : int = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
# Find root of polynomial
# Find fourth Root of 5
print(f'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')
# Find value of e
print(
'''The root of log(y) - 1 = 0 is ''',
f'{newton_raphson("log(y) - 1", 2, variable="y")}',
)
# Exponential Roots
print(
'''The root of exp(x) - 1 = 0 is''',
f'{newton_raphson("exp(x) - 1", 10, precision=0.005)}',
)
# Find root of cos(x)
print(f'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')
| 264 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _lowerCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A_ : Optional[int] = AudioLDMPipeline
A_ : List[Any] = TEXT_TO_AUDIO_PARAMS
A_ : int = TEXT_TO_AUDIO_BATCH_PARAMS
A_ : Optional[Any] = frozenset(
[
"""num_inference_steps""",
"""num_waveforms_per_prompt""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def __lowerCAmelCase ( self : List[Any] ) -> Any:
torch.manual_seed(0 )
__magic_name__ : Dict = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=(32, 64) , class_embed_type='simple_projection' , projection_class_embeddings_input_dim=32 , class_embeddings_concat=lowercase_ , )
__magic_name__ : List[Any] = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
torch.manual_seed(0 )
__magic_name__ : List[Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
__magic_name__ : Dict = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
__magic_name__ : Union[str, Any] = ClapTextModelWithProjection(lowercase_ )
__magic_name__ : Dict = RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta' , model_max_length=77 )
__magic_name__ : int = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=lowercase_ , )
__magic_name__ : List[str] = SpeechTaHifiGan(lowercase_ )
__magic_name__ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''vocoder''': vocoder,
}
return components
def __lowerCAmelCase ( self : Any , _A : List[str] , _A : List[str]=0 ) -> Any:
if str(lowercase_ ).startswith('mps' ):
__magic_name__ : int = torch.manual_seed(lowercase_ )
else:
__magic_name__ : Any = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
__magic_name__ : Optional[Any] = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
}
return inputs
def __lowerCAmelCase ( self : Tuple ) -> List[str]:
__magic_name__ : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__magic_name__ : Tuple = self.get_dummy_components()
__magic_name__ : Tuple = AudioLDMPipeline(**lowercase_ )
__magic_name__ : Any = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : List[Any] = self.get_dummy_inputs(lowercase_ )
__magic_name__ : List[str] = audioldm_pipe(**lowercase_ )
__magic_name__ : Any = output.audios[0]
assert audio.ndim == 1
assert len(lowercase_ ) == 256
__magic_name__ : List[Any] = audio[:10]
__magic_name__ : Tuple = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
__magic_name__ : Tuple = self.get_dummy_components()
__magic_name__ : str = AudioLDMPipeline(**lowercase_ )
__magic_name__ : Optional[int] = audioldm_pipe.to(lowercase_ )
__magic_name__ : Optional[int] = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : str = self.get_dummy_inputs(lowercase_ )
__magic_name__ : Optional[int] = 3 * [inputs['''prompt''']]
# forward
__magic_name__ : List[Any] = audioldm_pipe(**lowercase_ )
__magic_name__ : Optional[int] = output.audios[0]
__magic_name__ : Tuple = self.get_dummy_inputs(lowercase_ )
__magic_name__ : str = 3 * [inputs.pop('prompt' )]
__magic_name__ : str = audioldm_pipe.tokenizer(
lowercase_ , padding='max_length' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=lowercase_ , return_tensors='pt' , )
__magic_name__ : int = text_inputs['''input_ids'''].to(lowercase_ )
__magic_name__ : Tuple = audioldm_pipe.text_encoder(
lowercase_ , )
__magic_name__ : Optional[int] = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
__magic_name__ : int = F.normalize(lowercase_ , dim=-1 )
__magic_name__ : Tuple = prompt_embeds
# forward
__magic_name__ : Dict = audioldm_pipe(**lowercase_ )
__magic_name__ : List[Any] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def __lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
__magic_name__ : Dict = self.get_dummy_components()
__magic_name__ : int = AudioLDMPipeline(**lowercase_ )
__magic_name__ : Tuple = audioldm_pipe.to(lowercase_ )
__magic_name__ : Union[str, Any] = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : Tuple = self.get_dummy_inputs(lowercase_ )
__magic_name__ : int = 3 * ['''this is a negative prompt''']
__magic_name__ : int = negative_prompt
__magic_name__ : Union[str, Any] = 3 * [inputs['''prompt''']]
# forward
__magic_name__ : Union[str, Any] = audioldm_pipe(**lowercase_ )
__magic_name__ : Any = output.audios[0]
__magic_name__ : Optional[int] = self.get_dummy_inputs(lowercase_ )
__magic_name__ : Any = 3 * [inputs.pop('prompt' )]
__magic_name__ : List[Any] = []
for p in [prompt, negative_prompt]:
__magic_name__ : Union[str, Any] = audioldm_pipe.tokenizer(
lowercase_ , padding='max_length' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=lowercase_ , return_tensors='pt' , )
__magic_name__ : str = text_inputs['''input_ids'''].to(lowercase_ )
__magic_name__ : Optional[Any] = audioldm_pipe.text_encoder(
lowercase_ , )
__magic_name__ : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
__magic_name__ : List[str] = F.normalize(lowercase_ , dim=-1 )
embeds.append(lowercase_ )
__magic_name__ : str = embeds
# forward
__magic_name__ : Optional[Any] = audioldm_pipe(**lowercase_ )
__magic_name__ : int = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def __lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
__magic_name__ : Any = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__magic_name__ : Tuple = self.get_dummy_components()
__magic_name__ : Any = PNDMScheduler(skip_prk_steps=lowercase_ )
__magic_name__ : Tuple = AudioLDMPipeline(**lowercase_ )
__magic_name__ : str = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : Union[str, Any] = self.get_dummy_inputs(lowercase_ )
__magic_name__ : Tuple = '''egg cracking'''
__magic_name__ : Optional[int] = audioldm_pipe(**lowercase_ , negative_prompt=lowercase_ )
__magic_name__ : Any = output.audios[0]
assert audio.ndim == 1
assert len(lowercase_ ) == 256
__magic_name__ : int = audio[:10]
__magic_name__ : Union[str, Any] = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self : List[str] ) -> Tuple:
__magic_name__ : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__magic_name__ : Union[str, Any] = self.get_dummy_components()
__magic_name__ : Dict = PNDMScheduler(skip_prk_steps=lowercase_ )
__magic_name__ : Any = AudioLDMPipeline(**lowercase_ )
__magic_name__ : Any = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : int = '''A hammer hitting a wooden surface'''
# test num_waveforms_per_prompt=1 (default)
__magic_name__ : Optional[Any] = audioldm_pipe(lowercase_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
__magic_name__ : int = 2
__magic_name__ : Any = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
__magic_name__ : List[str] = 2
__magic_name__ : Union[str, Any] = audioldm_pipe(lowercase_ , num_inference_steps=2 , num_waveforms_per_prompt=lowercase_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
__magic_name__ : List[str] = 2
__magic_name__ : Dict = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=lowercase_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def __lowerCAmelCase ( self : int ) -> Tuple:
__magic_name__ : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__magic_name__ : Dict = self.get_dummy_components()
__magic_name__ : Optional[Any] = AudioLDMPipeline(**lowercase_ )
__magic_name__ : str = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : Optional[Any] = audioldm_pipe.vocoder.config.sampling_rate
__magic_name__ : Any = self.get_dummy_inputs(lowercase_ )
__magic_name__ : Tuple = audioldm_pipe(audio_length_in_s=0.016 , **lowercase_ )
__magic_name__ : Optional[Any] = output.audios[0]
assert audio.ndim == 1
assert len(lowercase_ ) / vocoder_sampling_rate == 0.016
__magic_name__ : List[str] = audioldm_pipe(audio_length_in_s=0.032 , **lowercase_ )
__magic_name__ : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(lowercase_ ) / vocoder_sampling_rate == 0.032
def __lowerCAmelCase ( self : Any ) -> int:
__magic_name__ : List[str] = self.get_dummy_components()
__magic_name__ : str = AudioLDMPipeline(**lowercase_ )
__magic_name__ : Optional[int] = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : int = ['''hey''']
__magic_name__ : int = audioldm_pipe(lowercase_ , num_inference_steps=1 )
__magic_name__ : Optional[Any] = output.audios.shape
assert audio_shape == (1, 256)
__magic_name__ : List[str] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
__magic_name__ : str = SpeechTaHifiGan(lowercase_ ).to(lowercase_ )
__magic_name__ : List[str] = audioldm_pipe(lowercase_ , num_inference_steps=1 )
__magic_name__ : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def __lowerCAmelCase ( self : List[Any] ) -> Tuple:
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase_ )
def __lowerCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
self._test_inference_batch_single_identical(test_mean_pixel_difference=lowercase_ )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase_ )
@slow
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Any ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : List[str] , _A : List[str] , _A : Optional[Any]="cpu" , _A : Optional[Any]=torch.floataa , _A : Any=0 ) -> Tuple:
__magic_name__ : str = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
__magic_name__ : List[Any] = np.random.RandomState(lowercase_ ).standard_normal((1, 8, 128, 16) )
__magic_name__ : Dict = torch.from_numpy(lowercase_ ).to(device=lowercase_ , dtype=lowercase_ )
__magic_name__ : Union[str, Any] = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 2.5,
}
return inputs
def __lowerCAmelCase ( self : List[Any] ) -> Tuple:
__magic_name__ : Optional[int] = AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
__magic_name__ : List[str] = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : Optional[Any] = self.get_inputs(lowercase_ )
__magic_name__ : Tuple = 25
__magic_name__ : int = audioldm_pipe(**lowercase_ ).audios[0]
assert audio.ndim == 1
assert len(lowercase_ ) == 81920
__magic_name__ : Tuple = audio[77230:77240]
__magic_name__ : Any = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
__magic_name__ : Union[str, Any] = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1E-2
def __lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
__magic_name__ : Optional[int] = AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
__magic_name__ : Dict = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
__magic_name__ : List[Any] = audioldm_pipe.to(lowercase_ )
audioldm_pipe.set_progress_bar_config(disable=lowercase_ )
__magic_name__ : str = self.get_inputs(lowercase_ )
__magic_name__ : Tuple = audioldm_pipe(**lowercase_ ).audios[0]
assert audio.ndim == 1
assert len(lowercase_ ) == 81920
__magic_name__ : Dict = audio[27780:27790]
__magic_name__ : Optional[Any] = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
__magic_name__ : int = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3E-2 | 331 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCamelCase : Optional[int] = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
_UpperCamelCase : List[Any] = direct_transformers_import(PATH_TO_TRANSFORMERS)
_UpperCamelCase : Optional[int] = transformers.models.auto.configuration_auto.CONFIG_MAPPING
_UpperCamelCase : Union[str, Any] = {
# used to compute the property `self.chunk_length`
'''EncodecConfig''': ['''overlap'''],
# used as `self.bert_model = BertModel(config, ...)`
'''DPRConfig''': True,
# not used in modeling files, but it's an important information
'''FSMTConfig''': ['''langs'''],
# used internally in the configuration class file
'''GPTNeoConfig''': ['''attention_types'''],
# used internally in the configuration class file
'''EsmConfig''': ['''is_folding_model'''],
# used during training (despite we don't have training script for these models yet)
'''Mask2FormerConfig''': ['''ignore_value'''],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
'''OneFormerConfig''': ['''ignore_value''', '''norm'''],
# used during preprocessing and collation, see `collating_graphormer.py`
'''GraphormerConfig''': ['''spatial_pos_max'''],
# used internally in the configuration class file
'''T5Config''': ['''feed_forward_proj'''],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
'''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
'''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
# used internally in the configuration class file
'''LongT5Config''': ['''feed_forward_proj'''],
# used internally in the configuration class file
'''SwitchTransformersConfig''': ['''feed_forward_proj'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''BioGptConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''GLPNConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''SegformerConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''CvtConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''PerceiverConfig''': ['''layer_norm_eps'''],
# used internally to calculate the feature size
'''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate the feature size
'''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate the feature size
'''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate `mlp_dim`
'''SamVisionConfig''': ['''mlp_ratio'''],
# For (head) training, but so far not implemented
'''ClapAudioConfig''': ['''num_classes'''],
# Not used, but providing useful information to users
'''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
'CLIPSegConfig': True,
'DeformableDetrConfig': True,
'DetaConfig': True,
'DinatConfig': True,
'DonutSwinConfig': True,
'EfficientFormerConfig': True,
'FSMTConfig': True,
'JukeboxConfig': True,
'LayoutLMv2Config': True,
'MaskFormerSwinConfig': True,
'MT5Config': True,
'NatConfig': True,
'OneFormerConfig': True,
'PerceiverConfig': True,
'RagConfig': True,
'SpeechT5Config': True,
'SwinConfig': True,
'Swin2SRConfig': True,
'Swinv2Config': True,
'SwitchTransformersConfig': True,
'TableTransformerConfig': True,
'TapasConfig': True,
'TransfoXLConfig': True,
'UniSpeechConfig': True,
'UniSpeechSatConfig': True,
'WavLMConfig': True,
'WhisperConfig': True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
'JukeboxPriorConfig': True,
# TODO: @Younes (for `is_decoder`)
'Pix2StructTextConfig': True,
}
)
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[int] , __snake_case : Dict , __snake_case : Dict , __snake_case : Any ):
'''simple docstring'''
lowercase = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f'config.{attribute}' in modeling_source
or f'getattr(config, \"{attribute}\"' in modeling_source
or f'getattr(self.config, \"{attribute}\"' in modeling_source
):
lowercase = True
# Deal with multi-line cases
elif (
re.search(
rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"' , _a , )
is not None
):
lowercase = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
lowercase = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
lowercase = [
'''bos_index''',
'''eos_index''',
'''pad_index''',
'''unk_index''',
'''mask_index''',
'''image_size''',
'''use_cache''',
'''out_features''',
'''out_indices''',
]
lowercase = ['''encoder_no_repeat_ngram_size''']
# Special cases to be allowed
lowercase = True
if not attribute_used:
lowercase = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
lowercase = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
lowercase = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
lowercase = True
elif attribute.endswith('_token_id' ):
lowercase = True
# configuration class specific cases
if not case_allowed:
lowercase = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
lowercase = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[Any] ):
'''simple docstring'''
lowercase = dict(inspect.signature(config_class.__init__ ).parameters )
lowercase = [x for x in list(signature.keys() ) if x not in ['''self''', '''kwargs''']]
lowercase = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
lowercase = {}
if len(config_class.attribute_map ) > 0:
lowercase = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
lowercase = inspect.getsourcefile(_a )
lowercase = os.path.dirname(_a )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
lowercase = [os.path.join(_a , _a ) for fn in os.listdir(_a ) if fn.startswith('modeling_' )]
# Get the source code strings
lowercase = []
for path in modeling_paths:
if os.path.isfile(_a ):
with open(_a ) as fp:
modeling_sources.append(fp.read() )
lowercase = []
for config_param, default_value in zip(_a , _a ):
# `attributes` here is all the variant names for `config_param`
lowercase = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(_a , _a , _a , _a ):
unused_attributes.append(attributes[0] )
return sorted(_a )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
lowercase = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda __snake_case : inspect.isclass(_a )
and issubclass(_a , _a )
and inspect.getmodule(_a ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
lowercase = check_config_attributes_being_used(_a )
if len(_a ) > 0:
lowercase = unused_attributes
if len(_a ) > 0:
lowercase = '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
for name, attributes in configs_with_unused_attributes.items():
error += f'{name}: {attributes}\n'
raise ValueError(_a )
if __name__ == "__main__":
check_config_attributes()
| 220 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def __lowercase ( _a ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
from __future__ import annotations
from typing import Any
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> Any:
lowerCAmelCase_ : Union[str, Any] = num_of_nodes
lowerCAmelCase_ : list[list[int]] = []
lowerCAmelCase_ : dict[int, int] = {}
def lowercase_ ( self , __lowercase , __lowercase , __lowercase ) -> Any:
self.m_edges.append([u_node, v_node, weight] )
def lowercase_ ( self , __lowercase ) -> Optional[Any]:
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def lowercase_ ( self , __lowercase ) -> str:
if self.m_component[u_node] != u_node:
for k in self.m_component:
lowerCAmelCase_ : List[Any] = self.find_component(lowercase_ )
def lowercase_ ( self , __lowercase , __lowercase , __lowercase ) -> Any:
if component_size[u_node] <= component_size[v_node]:
lowerCAmelCase_ : int = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowercase_ )
elif component_size[u_node] >= component_size[v_node]:
lowerCAmelCase_ : Dict = self.find_component(lowercase_ )
component_size[u_node] += component_size[v_node]
self.set_component(lowercase_ )
def lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : Union[str, Any] = 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_ : Optional[Any] = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
lowerCAmelCase_ : Any = edge
lowerCAmelCase_ : List[str] = self.m_component[u]
lowerCAmelCase_ : 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
):
lowerCAmelCase_ : Union[str, Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowercase_ , lowercase_ ):
lowerCAmelCase_ : Dict = edge
lowerCAmelCase_ : Optional[Any] = self.m_component[u]
lowerCAmelCase_ : Optional[int] = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowercase_ , lowercase_ , lowercase_ )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
lowerCAmelCase_ : Any = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def lowerCAmelCase ( )-> Any:
pass
if __name__ == "__main__":
import doctest
doctest.testmod() | 262 |
"""simple docstring"""
import sys
lowercase__ : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __lowercase ( _a ):
snake_case_ : List[Any] = 1
for digit in s:
product *= int(_a )
return product
def __lowercase ( _a = N ):
snake_case_ : Optional[int] = -sys.maxsize - 1
snake_case_ : str = n[:13]
snake_case_ : List[Any] = 13
while cur_index < len(_a ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
snake_case_ : int = substr[1:] + n[cur_index]
cur_index += 1
else:
snake_case_ : Optional[Any] = max(_a , str_eval(_a ) )
snake_case_ : Any = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'{solution() = }')
| 264 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A: Optional[Any] = logging.get_logger(__name__)
A: Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A: Union[str, Any] = {
'''vocab_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
},
'''merges_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
},
}
A: int = {
'''gpt2''': 1_0_2_4,
'''gpt2-medium''': 1_0_2_4,
'''gpt2-large''': 1_0_2_4,
'''gpt2-xl''': 1_0_2_4,
'''distilgpt2''': 1_0_2_4,
}
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase__ ):
__lowerCAmelCase : Optional[int] = VOCAB_FILES_NAMES
__lowerCAmelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : Tuple = ["""input_ids""", """attention_mask"""]
__lowerCAmelCase : str = GPTaTokenizer
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="<|endoftext|>" , _SCREAMING_SNAKE_CASE="<|endoftext|>" , _SCREAMING_SNAKE_CASE="<|endoftext|>" , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ) -> List[str]:
'''simple docstring'''
super().__init__(
lowercase_ , lowercase_ , tokenizer_file=lowercase_ , unk_token=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , add_prefix_space=lowercase_ , **lowercase_ , )
UpperCAmelCase : List[str] = kwargs.pop("""add_bos_token""" , lowercase_ )
UpperCAmelCase : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , lowercase_ ) != add_prefix_space:
UpperCAmelCase : Tuple = getattr(lowercase_ , pre_tok_state.pop("""type""" ) )
UpperCAmelCase : Optional[int] = add_prefix_space
UpperCAmelCase : int = pre_tok_class(**lowercase_ )
UpperCAmelCase : str = add_prefix_space
def SCREAMING_SNAKE_CASE ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str = kwargs.get("""is_split_into_words""" , lowercase_ )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[Any] = kwargs.get("""is_split_into_words""" , lowercase_ )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowercase_ , add_special_tokens=lowercase_ ) + [self.eos_token_id] )
if len(lowercase_ ) > self.model_max_length:
UpperCAmelCase : Tuple = input_ids[-self.model_max_length :]
return input_ids
| 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : List[Any] = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Any = ['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : int = [
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = [
'''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDistilBertForMaskedLM''',
'''TFDistilBertForMultipleChoice''',
'''TFDistilBertForQuestionAnswering''',
'''TFDistilBertForSequenceClassification''',
'''TFDistilBertForTokenClassification''',
'''TFDistilBertMainLayer''',
'''TFDistilBertModel''',
'''TFDistilBertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''FlaxDistilBertForMaskedLM''',
'''FlaxDistilBertForMultipleChoice''',
'''FlaxDistilBertForQuestionAnswering''',
'''FlaxDistilBertForSequenceClassification''',
'''FlaxDistilBertForTokenClassification''',
'''FlaxDistilBertModel''',
'''FlaxDistilBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
lowercase__ : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class snake_case :
"""simple docstring"""
@staticmethod
def __lowerCAmelCase ( *lowerCamelCase__ : str ,**lowerCamelCase__ : Optional[int] ):
pass
def a_ ( lowerCamelCase ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowerCAmelCase__ : Tuple = (
'''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
"""simple docstring"""
snake_case__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : int ,lowerCamelCase__ : Any ):
UpperCAmelCase__ = pipeline(
'document-question-answering' ,model=lowercase_ ,tokenizer=lowercase_ ,image_processor=lowercase_ )
UpperCAmelCase__ = INVOICE_URL
UpperCAmelCase__ = list(zip(*apply_tesseract(load_image(lowercase_ ) ,lowercase_ ,'' ) ) )
UpperCAmelCase__ = '''What is the placebo?'''
UpperCAmelCase__ = [
{
'''image''': load_image(lowercase_ ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def __lowerCAmelCase ( self : int ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Any ):
UpperCAmelCase__ = dqa_pipeline(lowercase_ ,top_k=2 )
self.assertEqual(
lowercase_ ,[
[
{'score': ANY(lowercase_ ), 'answer': ANY(lowercase_ ), 'start': ANY(lowercase_ ), 'end': ANY(lowercase_ )},
{'score': ANY(lowercase_ ), 'answer': ANY(lowercase_ ), 'start': ANY(lowercase_ ), 'end': ANY(lowercase_ )},
]
]
* 3 ,)
@require_torch
@require_detectrona
@require_pytesseract
def __lowerCAmelCase ( self : Any ):
UpperCAmelCase__ = pipeline('document-question-answering' ,model='hf-internal-testing/tiny-random-layoutlmv2' )
UpperCAmelCase__ = INVOICE_URL
UpperCAmelCase__ = '''How many cats are there?'''
UpperCAmelCase__ = [
{'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
UpperCAmelCase__ = dqa_pipeline(image=lowercase_ ,question=lowercase_ ,top_k=2 )
self.assertEqual(nested_simplify(lowercase_ ,decimals=4 ) ,lowercase_ )
UpperCAmelCase__ = dqa_pipeline({'image': image, 'question': question} ,top_k=2 )
self.assertEqual(nested_simplify(lowercase_ ,decimals=4 ) ,lowercase_ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
UpperCAmelCase__ = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
UpperCAmelCase__ = dqa_pipeline(image=lowercase_ ,question=lowercase_ ,top_k=2 )
self.assertEqual(lowercase_ ,[] )
# We can optionnally pass directly the words and bounding boxes
UpperCAmelCase__ = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
UpperCAmelCase__ = []
UpperCAmelCase__ = []
UpperCAmelCase__ = dqa_pipeline(image=lowercase_ ,question=lowercase_ ,words=lowercase_ ,boxes=lowercase_ ,top_k=2 )
self.assertEqual(lowercase_ ,[] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __lowerCAmelCase ( self : Optional[int] ):
UpperCAmelCase__ = pipeline(
'document-question-answering' ,model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' ,revision='9977165' ,)
UpperCAmelCase__ = INVOICE_URL
UpperCAmelCase__ = '''What is the invoice number?'''
UpperCAmelCase__ = dqa_pipeline(image=lowercase_ ,question=lowercase_ ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
UpperCAmelCase__ = dqa_pipeline({'image': image, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
UpperCAmelCase__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
[
{'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
],
]
* 2 ,)
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = pipeline(
'document-question-answering' ,model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' ,revision='9977165' ,max_seq_len=50 ,)
UpperCAmelCase__ = INVOICE_URL
UpperCAmelCase__ = '''What is the invoice number?'''
UpperCAmelCase__ = dqa_pipeline(image=lowercase_ ,question=lowercase_ ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
UpperCAmelCase__ = dqa_pipeline({'image': image, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
UpperCAmelCase__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
[
{'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 ,)
@slow
@require_torch
@require_pytesseract
@require_vision
def __lowerCAmelCase ( self : Dict ):
UpperCAmelCase__ = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' ,revision='3dc6de3' ,add_prefix_space=lowercase_ )
UpperCAmelCase__ = pipeline(
'document-question-answering' ,model='impira/layoutlm-document-qa' ,tokenizer=lowercase_ ,revision='3dc6de3' ,)
UpperCAmelCase__ = INVOICE_URL
UpperCAmelCase__ = '''What is the invoice number?'''
UpperCAmelCase__ = dqa_pipeline(image=lowercase_ ,question=lowercase_ ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
] ,)
UpperCAmelCase__ = dqa_pipeline({'image': image, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
] ,)
UpperCAmelCase__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
[
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
]
]
* 2 ,)
UpperCAmelCase__ = list(zip(*apply_tesseract(load_image(lowercase_ ) ,lowercase_ ,'' ) ) )
# This model should also work if `image` is set to None
UpperCAmelCase__ = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
] ,)
@slow
@require_torch
@require_pytesseract
@require_vision
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' ,revision='3dc6de3' ,add_prefix_space=lowercase_ )
UpperCAmelCase__ = pipeline(
'document-question-answering' ,model='impira/layoutlm-document-qa' ,tokenizer=lowercase_ ,revision='3dc6de3' ,max_seq_len=50 ,)
UpperCAmelCase__ = INVOICE_URL
UpperCAmelCase__ = '''What is the invoice number?'''
UpperCAmelCase__ = dqa_pipeline(image=lowercase_ ,question=lowercase_ ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
UpperCAmelCase__ = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
[
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 ,)
UpperCAmelCase__ = list(zip(*apply_tesseract(load_image(lowercase_ ) ,lowercase_ ,'' ) ) )
# This model should also work if `image` is set to None
UpperCAmelCase__ = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowercase_ ,decimals=4 ) ,[
{'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
@slow
@require_torch
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = pipeline(
'document-question-answering' ,model='naver-clova-ix/donut-base-finetuned-docvqa' ,tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa' ) ,feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' ,)
UpperCAmelCase__ = INVOICE_URL
UpperCAmelCase__ = '''What is the invoice number?'''
UpperCAmelCase__ = dqa_pipeline(image=lowercase_ ,question=lowercase_ ,top_k=2 )
self.assertEqual(nested_simplify(lowercase_ ,decimals=4 ) ,[{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def __lowerCAmelCase ( self : Any ):
pass
| 98 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = logging.get_logger(__name__)
def __lowercase ( _a , _a=False ):
snake_case_ : List[str] = []
# fmt: off
# stem:
rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''') )
rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''') )
rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''') )
# backbone
rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case_ : Optional[int] = [(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'''),
] )
# fmt: on
return rename_keys
def __lowercase ( _a , _a , _a=False ):
for i in range(config.num_hidden_layers ):
if base_model:
snake_case_ : List[str] = ''''''
else:
snake_case_ : Dict = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ : List[str] = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
snake_case_ : Optional[int] = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Any = in_proj_weight[
: config.hidden_size, :
]
snake_case_ : Dict = in_proj_bias[: config.hidden_size]
snake_case_ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case_ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case_ : Dict = in_proj_weight[
-config.hidden_size :, :
]
snake_case_ : str = in_proj_bias[-config.hidden_size :]
def __lowercase ( _a ):
snake_case_ : Dict = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(_a , _a )
def __lowercase ( _a , _a , _a ):
snake_case_ : Union[str, Any] = dct.pop(_a )
snake_case_ : Union[str, Any] = val
def __lowercase ( ):
snake_case_ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ : Tuple = Image.open(requests.get(_a , stream=_a ).raw )
return im
@torch.no_grad()
def __lowercase ( _a , _a , _a=False ):
snake_case_ : str = BitConfig(
global_padding='''same''' , layer_type='''bottleneck''' , depths=(3, 4, 9) , out_features=['''stage3'''] , embedding_dynamic_padding=_a , )
snake_case_ : Tuple = ViTHybridConfig(backbone_config=_a , image_size=384 , num_labels=1_000 )
snake_case_ : int = False
# load original model from timm
snake_case_ : str = timm.create_model(_a , pretrained=_a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case_ : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_a )
snake_case_ : int = create_rename_keys(_a , _a )
for src, dest in rename_keys:
rename_key(_a , _a , _a )
read_in_q_k_v(_a , _a , _a )
snake_case_ : Optional[Any] = '''huggingface/label-files'''
snake_case_ : Any = '''imagenet-1k-id2label.json'''
snake_case_ : Dict = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
snake_case_ : Dict = {int(_a ): v for k, v in idalabel.items()}
snake_case_ : Optional[int] = idalabel
snake_case_ : Optional[Any] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
snake_case_ : Optional[Any] = ViTHybridModel(_a ).eval()
else:
snake_case_ : Any = ViTHybridForImageClassification(_a ).eval()
model.load_state_dict(_a )
# create image processor
snake_case_ : Optional[Any] = create_transform(**resolve_data_config({} , model=_a ) )
snake_case_ : List[Any] = transform.transforms
snake_case_ : Optional[Any] = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
snake_case_ : List[Any] = ViTHybridImageProcessor(
do_resize=_a , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_a , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=_a , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Optional[int] = transform(_a ).unsqueeze(0 )
snake_case_ : int = processor(_a , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(_a , _a )
# verify logits
with torch.no_grad():
snake_case_ : List[str] = model(_a )
snake_case_ : Any = outputs.logits
print('''Predicted class:''' , logits.argmax(-1 ).item() )
if base_model:
snake_case_ : Optional[Any] = timm_model.forward_features(_a )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_a , outputs.pooler_output , atol=1E-3 )
else:
snake_case_ : int = timm_model(_a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_a , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(f"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_a )
if push_to_hub:
print(f"Pushing model and processor to the hub {vit_name}" )
model.push_to_hub(f"ybelkada/{vit_name}" )
processor.push_to_hub(f"ybelkada/{vit_name}" )
if __name__ == "__main__":
lowercase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--vit_name''',
default='''vit_base_r50_s16_384''',
type=str,
help='''Name of the hybrid ViT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
lowercase__ : Any = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 264 | 0 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __lowercase ( lowerCamelCase : Dict="" ):
UpperCamelCase_ : List[str] = tempfile.mkdtemp()
return os.path.join(_a , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _lowercase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : int = torch.rand(1_2 , dtype=torch.floataa ) - 0.5
UpperCamelCase_ : Optional[int] = AgentAudio(lowercase_ )
UpperCamelCase_ : List[str] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowercase_ ) )
# Ensure that the file contains the same value as the original tensor
UpperCamelCase_ : int = sf.read(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , torch.tensor(lowercase_ ) , atol=1e-4 ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Any:
"""simple docstring"""
UpperCamelCase_ : Any = torch.rand(1_2 , dtype=torch.floataa ) - 0.5
UpperCamelCase_ : List[str] = get_new_path(suffix='.wav' )
sf.write(lowercase_ , lowercase_ , 1_6_0_0_0 )
UpperCamelCase_ : Tuple = AgentAudio(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1e-4 ) )
self.assertEqual(agent_type.to_string() , lowercase_ )
@require_vision
@require_torch
class _lowercase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = torch.randint(0 , 2_5_6 , (6_4, 6_4, 3) )
UpperCamelCase_ : str = AgentImage(lowercase_ )
UpperCamelCase_ : Union[str, Any] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type._tensor , atol=1e-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : Any = Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '''000000039769.png'''
UpperCamelCase_ : Optional[int] = Image.open(lowercase_ )
UpperCamelCase_ : Tuple = AgentImage(lowercase_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : int = Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '''000000039769.png'''
UpperCamelCase_ : Dict = Image.open(lowercase_ )
UpperCamelCase_ : List[str] = AgentImage(lowercase_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
class _lowercase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Tuple = '''Hey!'''
UpperCamelCase_ : Optional[Any] = AgentText(lowercase_ )
self.assertEqual(lowercase_ , agent_type.to_string() )
self.assertEqual(lowercase_ , agent_type.to_raw() )
self.assertEqual(lowercase_ , lowercase_ )
| 175 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
lowercase__ : str = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def __lowercase ( _a , _a ):
snake_case_ : Optional[int] = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
snake_case_ : List[Any] = int(re.match(r'''.*layer_(\d*).*''' , _a )[1] )
layer_number -= 3
return f"h.{layer_number}." + key
def __lowercase ( _a ):
if dtype == torch.bool:
return 1 / 8
snake_case_ : Dict = re.search(r'''[^\d](\d+)$''' , str(_a ) )
if bit_search is None:
raise ValueError(f"`dtype` is not a valid dtype: {dtype}." )
snake_case_ : Optional[int] = int(bit_search.groups()[0] )
return bit_size // 8
def __lowercase ( _a , _a , _a , _a , _a ):
# Construct model
if bloom_config_file == "":
snake_case_ : int = BloomConfig()
else:
snake_case_ : List[str] = BloomConfig.from_json_file(_a )
if shard_model:
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : int = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[str] = {'''weight_map''': {}, '''metadata''': {}}
snake_case_ : Any = 0
snake_case_ : Union[str, Any] = None
snake_case_ : List[str] = BloomConfig()
for j, file in enumerate(_a ):
print('''Processing file: {}'''.format(_a ) )
snake_case_ : Dict = None
for i in range(_a ):
# load all TP files
snake_case_ : Union[str, Any] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : List[str] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : Any = temp.pop(_a )
if tensors is None:
snake_case_ : Any = temp
else:
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : List[str] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Any = tensors[key] / pretraining_tp
torch.save(
_a , os.path.join(
_a , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
snake_case_ : List[str] = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
snake_case_ : List[str] = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) )
snake_case_ : int = BloomConfig()
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Dict = total_size
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(_a , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f:
snake_case_ : Tuple = json.dumps(_a , indent=2 , sort_keys=_a ) + '''\n'''
f.write(_a )
else:
snake_case_ : Union[str, Any] = BloomModel(_a )
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : Dict = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[Any] = None
for i, file in enumerate(_a ):
snake_case_ : Optional[Any] = None
for i in range(_a ):
# load all TP files
snake_case_ : List[str] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : Optional[Any] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : str = temp.pop(_a )
if tensors is None:
snake_case_ : int = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : Optional[Any] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Union[str, Any] = tensors[key] / pretraining_tp
snake_case_ : Any = model.load_state_dict(_a , strict=_a )
assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
if missing_keys is None:
snake_case_ : Optional[int] = set(other_keys.missing_keys )
else:
snake_case_ : Tuple = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f"The keys {missing_keys} are missing"
# Save pytorch-model
os.makedirs(_a , exist_ok=_a )
snake_case_ : List[str] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}" )
if config.torch_dtype is not None:
snake_case_ : Optional[Any] = model.to(config.torch_dtype )
torch.save(model.state_dict() , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
lowercase__ : List[Any] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 264 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A_ : int = None
A_ : Any = logging.get_logger(__name__)
A_ : List[str] = '''▁'''
A_ : Optional[int] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
A_ : str = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
'''tokenizer_file''': {
'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
},
}
A_ : List[Any] = {
'''google/pegasus-xsum''': 5_1_2,
}
class lowercase ( lowerCAmelCase__ ):
"""simple docstring"""
UpperCAmelCase = VOCAB_FILES_NAMES
UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase = PegasusTokenizer
UpperCAmelCase = ["""input_ids""", """attention_mask"""]
def __init__( self ,a_=None ,a_=None ,a_="<pad>" ,a_="</s>" ,a_="<unk>" ,a_="<mask_2>" ,a_="<mask_1>" ,a_=None ,a_=103 ,**a_ ,) -> Dict:
_UpperCAmelCase : Dict = offset
if additional_special_tokens is not None:
if not isinstance(lowercase_ ,lowercase_ ):
raise TypeError(
f'''additional_special_tokens should be of type {type(lowercase_ )}, but is'''
f''' {type(lowercase_ )}''' )
_UpperCAmelCase : str = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(lowercase_ ) ,self.offset - 1 )
]
if len(set(lowercase_ ) ) != len(lowercase_ ):
raise ValueError(
"""Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
_UpperCAmelCase : Union[str, Any] = additional_special_tokens_extended
else:
_UpperCAmelCase : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 ,self.offset )]
super().__init__(
lowercase_ ,tokenizer_file=lowercase_ ,pad_token=lowercase_ ,eos_token=lowercase_ ,unk_token=lowercase_ ,mask_token=lowercase_ ,mask_token_sent=lowercase_ ,offset=lowercase_ ,additional_special_tokens=lowercase_ ,**lowercase_ ,)
_UpperCAmelCase : List[Any] = vocab_file
_UpperCAmelCase : List[Any] = False if not self.vocab_file else True
def _snake_case ( self ,a_ ) -> List[Any]:
_UpperCAmelCase : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"""There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
f''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def _snake_case ( self ,a_ ,a_ = None ,a_ = False ) -> List[str]:
if already_has_special_tokens:
return self._special_token_mask(lowercase_ )
elif token_ids_a is None:
return self._special_token_mask(lowercase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _snake_case ( self ,a_ ,a_=None ) -> List[str]:
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 _snake_case ( self ,a_ ,a_ = None ) -> Optional[int]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(lowercase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase : Dict = os.path.join(
lowercase_ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file ,lowercase_ )
return (out_vocab_file,)
| 215 |
"""simple docstring"""
def __lowercase ( _a , _a , _a=False ):
if isinstance(_a , _a ) and isinstance(_a , _a ):
snake_case_ : Union[str, Any] = len(set_a.intersection(_a ) )
if alternative_union:
snake_case_ : Any = len(_a ) + len(_a )
else:
snake_case_ : str = len(set_a.union(_a ) )
return intersection / union
if isinstance(_a , (list, tuple) ) and isinstance(_a , (list, tuple) ):
snake_case_ : str = [element for element in set_a if element in set_b]
if alternative_union:
snake_case_ : Tuple = len(_a ) + len(_a )
return len(_a ) / union
else:
snake_case_ : List[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_a ) / len(_a )
return len(_a ) / len(_a )
return None
if __name__ == "__main__":
lowercase__ : Any = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowercase__ : Optional[Any] = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 264 | 0 |
'''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
A__ : Dict =logging.get_logger(__name__)
A__ : Optional[int] ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
A__ : List[str] ={
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
A__ : List[str] ={
'''allenai/led-base-16384''': 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
_lowerCAmelCase = bs[:]
_lowerCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_a )
cs.append(2**8 + n )
n += 1
_lowerCAmelCase = [chr(_a ) for n in cs]
return dict(zip(_a , _a ) )
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = set()
_lowerCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCAmelCase = char
return pairs
class UpperCAmelCase ( lowerCAmelCase__ ):
_lowercase: List[str] = VOCAB_FILES_NAMES
_lowercase: Any = PRETRAINED_VOCAB_FILES_MAP
_lowercase: Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase: Optional[int] = ["""input_ids""", """attention_mask"""]
def __init__( self : Optional[Any] , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : int="replace" , __snake_case : Any="<s>" , __snake_case : List[str]="</s>" , __snake_case : int="</s>" , __snake_case : List[str]="<s>" , __snake_case : Tuple="<unk>" , __snake_case : Optional[int]="<pad>" , __snake_case : Union[str, Any]="<mask>" , __snake_case : Dict=False , **__snake_case : Optional[Any] , ) -> List[str]:
_lowerCAmelCase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else bos_token
_lowerCAmelCase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else eos_token
_lowerCAmelCase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else sep_token
_lowerCAmelCase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else cls_token
_lowerCAmelCase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else unk_token
_lowerCAmelCase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else pad_token
# 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
super().__init__(
errors=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , cls_token=lowercase_ , pad_token=lowercase_ , mask_token=lowercase_ , add_prefix_space=lowercase_ , **lowercase_ , )
with open(lowercase_ , encoding="""utf-8""" ) as vocab_handle:
_lowerCAmelCase = json.load(lowercase_ )
_lowerCAmelCase = {v: k for k, v in self.encoder.items()}
_lowerCAmelCase = errors # how to handle errors in decoding
_lowerCAmelCase = bytes_to_unicode()
_lowerCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(lowercase_ , encoding="""utf-8""" ) as merges_handle:
_lowerCAmelCase = merges_handle.read().split("""\n""" )[1:-1]
_lowerCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCAmelCase = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
_lowerCAmelCase = {}
_lowerCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCAmelCase = 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 lowercase__ ( self : Union[str, Any] ) -> str:
return len(self.encoder )
def lowercase__ ( self : Optional[Any] ) -> int:
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase__ ( self : Tuple , __snake_case : Tuple ) -> Optional[int]:
if token in self.cache:
return self.cache[token]
_lowerCAmelCase = tuple(lowercase_ )
_lowerCAmelCase = get_pairs(lowercase_ )
if not pairs:
return token
while True:
_lowerCAmelCase = min(lowercase_ , key=lambda __snake_case : self.bpe_ranks.get(lowercase_ , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCAmelCase = bigram
_lowerCAmelCase = []
_lowerCAmelCase = 0
while i < len(lowercase_ ):
try:
_lowerCAmelCase = word.index(lowercase_ , lowercase_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCAmelCase = j
if word[i] == first and i < len(lowercase_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCAmelCase = tuple(lowercase_ )
_lowerCAmelCase = new_word
if len(lowercase_ ) == 1:
break
else:
_lowerCAmelCase = get_pairs(lowercase_ )
_lowerCAmelCase = ''' '''.join(lowercase_ )
_lowerCAmelCase = word
return word
def lowercase__ ( self : Any , __snake_case : Union[str, Any] ) -> Optional[int]:
_lowerCAmelCase = []
for token in re.findall(self.pat , lowercase_ ):
_lowerCAmelCase = ''''''.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(lowercase_ ).split(""" """ ) )
return bpe_tokens
def lowercase__ ( self : List[Any] , __snake_case : List[str] ) -> Any:
return self.encoder.get(lowercase_ , self.encoder.get(self.unk_token ) )
def lowercase__ ( self : Union[str, Any] , __snake_case : Dict ) -> Tuple:
return self.decoder.get(lowercase_ )
def lowercase__ ( self : Tuple , __snake_case : Union[str, Any] ) -> Union[str, Any]:
_lowerCAmelCase = ''''''.join(lowercase_ )
_lowerCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def lowercase__ ( self : Optional[int] , __snake_case : str , __snake_case : Optional[str] = None ) -> Any:
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["""merges_file"""] )
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowercase_ , ensure_ascii=lowercase_ ) + """\n""" )
_lowerCAmelCase = 0
with open(lowercase_ , """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 __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(lowercase_ ) + """\n""" )
index += 1
return vocab_file, merge_file
def lowercase__ ( self : str , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> Tuple:
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 lowercase__ ( self : Any , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ) -> List[Any]:
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 lowercase__ ( self : int , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> Tuple:
_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]
def lowercase__ ( self : Union[str, Any] , __snake_case : str , __snake_case : str=False , **__snake_case : Dict ) -> str:
_lowerCAmelCase = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowercase_ ) > 0 and not text[0].isspace()):
_lowerCAmelCase = ''' ''' + text
return (text, kwargs)
def lowercase__ ( self : Any , __snake_case : Union[Dict[str, EncodedInput], BatchEncoding] , __snake_case : Optional[int] = None , __snake_case : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case : Optional[int] = None , __snake_case : Optional[bool] = None , ) -> Any:
_lowerCAmelCase = super()._pad(
encoded_inputs=lowercase_ , max_length=lowercase_ , padding_strategy=lowercase_ , pad_to_multiple_of=lowercase_ , return_attention_mask=lowercase_ , )
# Load from model defaults
if return_attention_mask is None:
_lowerCAmelCase = '''attention_mask''' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCAmelCase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCAmelCase = len(encoded_inputs["""global_attention_mask"""] ) != len(lowercase_ )
if needs_to_be_padded:
_lowerCAmelCase = len(lowercase_ ) - 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`
_lowerCAmelCase = (
encoded_inputs['''global_attention_mask'''] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCAmelCase = [-1] * difference + encoded_inputs[
'''global_attention_mask'''
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return encoded_inputs
| 70 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowercase__ : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __lowercase ( ):
snake_case_ : Optional[Any] = Github(os.environ['''GITHUB_TOKEN'''] )
snake_case_ : Any = g.get_repo('''huggingface/diffusers''' )
snake_case_ : Any = repo.get_issues(state='''open''' )
for issue in open_issues:
snake_case_ : str = sorted(issue.get_comments() , key=lambda _a : i.created_at , reverse=_a )
snake_case_ : Dict = comments[0] if len(_a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 264 | 0 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
# Return True if there is node that has not iterated.
UpperCAmelCase_ : str = [False] * len(_a )
UpperCAmelCase_ : Tuple = []
queue.append(_a )
UpperCAmelCase_ : Optional[Any] = True
while queue:
UpperCAmelCase_ : Tuple = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_a )
UpperCAmelCase_ : Union[str, Any] = True
UpperCAmelCase_ : List[str] = u
return visited[t]
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
# This array is filled by BFS and to store path
UpperCAmelCase_ : List[Any] = [-1] * (len(_a ))
UpperCAmelCase_ : Dict = 0
while bfs(_a, _a, _a, _a ):
UpperCAmelCase_ : Tuple = float("Inf" )
UpperCAmelCase_ : Optional[int] = sink
while s != source:
# Find the minimum value in select path
UpperCAmelCase_ : Optional[Any] = min(_a, graph[parent[s]][s] )
UpperCAmelCase_ : str = parent[s]
max_flow += path_flow
UpperCAmelCase_ : Tuple = sink
while v != source:
UpperCAmelCase_ : List[Any] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
UpperCAmelCase_ : Optional[int] = parent[v]
return max_flow
_a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
_a = 0, 5
print(ford_fulkerson(graph, source, sink))
| 61 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowercase ( _a , _a ):
# Load checkpoint
snake_case_ : Optional[Any] = torch.load(_a , map_location='''cpu''' )
snake_case_ : Union[str, Any] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
snake_case_ : Dict = {}
for k, v in state_dict.items():
if "pred_layer" in k:
snake_case_ : Union[str, Any] = v
else:
snake_case_ : Dict = v
snake_case_ : Union[str, Any] = chkpt['''params''']
snake_case_ : int = {n: v for n, v in config.items() if not isinstance(_a , (torch.FloatTensor, numpy.ndarray) )}
snake_case_ : int = chkpt['''dico_word2id''']
snake_case_ : str = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(f"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(_a , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
print(f"Save vocab file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
if __name__ == "__main__":
lowercase__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowercase__ : List[str] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 264 | 0 |
from __future__ import annotations
class A__:
"""simple docstring"""
def __init__( self , _lowercase = 0 ) -> List[Any]:
a_ : List[Any] = key
def UpperCamelCase__ ( self , _lowercase , _lowercase ) -> Any:
assert isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ )
a_ : List[Any] = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(lowercase_ ) ^ key ) for ch in content]
def UpperCamelCase__ ( self , _lowercase , _lowercase ) -> Optional[int]:
assert isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ )
a_ : List[Any] = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(lowercase_ ) ^ key ) for ch in content]
def UpperCamelCase__ ( self , _lowercase , _lowercase = 0 ) -> Dict:
assert isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ )
a_ : List[Any] = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
a_ : str = ''''''
for ch in content:
ans += chr(ord(lowercase_ ) ^ key )
return ans
def UpperCamelCase__ ( self , _lowercase , _lowercase = 0 ) -> Any:
assert isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ )
a_ : Any = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
a_ : Any = ''''''
for ch in content:
ans += chr(ord(lowercase_ ) ^ key )
return ans
def UpperCamelCase__ ( self , _lowercase , _lowercase = 0 ) -> int:
assert isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ )
try:
with open(lowercase_ ) as fin, open("""encrypt.out""" , """w+""" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(lowercase_ , lowercase_ ) )
except OSError:
return False
return True
def UpperCamelCase__ ( self , _lowercase , _lowercase ) -> Union[str, Any]:
assert isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ )
try:
with open(lowercase_ ) as fin, open("""decrypt.out""" , """w+""" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(lowercase_ , lowercase_ ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 248 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 264 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ : Optional[int] = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Union[str, Any] = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Any = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 338 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __lowercase ( _a="" ):
snake_case_ : List[str] = tempfile.mkdtemp()
return os.path.join(_a , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : str ):
snake_case_ : int = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : Optional[int] = AgentAudio(lowercase_ )
snake_case_ : List[str] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowercase_ ) )
# Ensure that the file contains the same value as the original tensor
snake_case_, snake_case_ : int = sf.read(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , torch.tensor(lowercase_ ) , atol=1E-4 ) )
def _snake_case ( self : Optional[int] ):
snake_case_ : Any = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : List[str] = get_new_path(suffix='''.wav''' )
sf.write(lowercase_ , lowercase_ , 16000 )
snake_case_ : Tuple = AgentAudio(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
self.assertEqual(agent_type.to_string() , lowercase_ )
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Tuple ):
snake_case_ : List[Any] = torch.randint(0 , 256 , (64, 64, 3) )
snake_case_ : str = AgentImage(lowercase_ )
snake_case_ : Union[str, Any] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type._tensor , atol=1E-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : Any = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Optional[int] = Image.open(lowercase_ )
snake_case_ : Tuple = AgentImage(lowercase_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : int = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Dict = Image.open(lowercase_ )
snake_case_ : List[str] = AgentImage(lowercase_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Any ):
snake_case_ : Tuple = '''Hey!'''
snake_case_ : Optional[Any] = AgentText(lowercase_ )
self.assertEqual(lowercase_ , agent_type.to_string() )
self.assertEqual(lowercase_ , agent_type.to_raw() )
self.assertEqual(lowercase_ , lowercase_ )
| 264 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
A_ : Any = ["""image_processor""", """tokenizer"""]
A_ : Optional[Any] = """ViTImageProcessor"""
A_ : Any = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self : Optional[int] , _A : Any=None , _A : str=None , **_A : Tuple ) -> int:
__magic_name__ : Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , lowercase_ , )
__magic_name__ : Tuple = kwargs.pop('feature_extractor' )
__magic_name__ : int = 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__(lowercase_ , lowercase_ )
def __call__( self : Tuple , _A : Dict=None , _A : Union[str, Any]=None , _A : List[str]=None , _A : str=None , **_A : Union[str, Any] ) -> int:
if text is None and visual_prompt is None and images is None:
raise ValueError('You have to specify either text, visual prompt or images.' )
if text is not None and visual_prompt is not None:
raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.' )
if text is not None:
__magic_name__ : Tuple = self.tokenizer(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if visual_prompt is not None:
__magic_name__ : Tuple = self.image_processor(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if images is not None:
__magic_name__ : Tuple = self.image_processor(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if visual_prompt is not None and images is not None:
__magic_name__ : str = {
'''pixel_values''': image_features.pixel_values,
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
__magic_name__ : Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
__magic_name__ : Optional[int] = {
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowercase_ ) , tensor_type=lowercase_ )
def __lowerCAmelCase ( self : Union[str, Any] , *_A : Union[str, Any] , **_A : Optional[Any] ) -> List[Any]:
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )
def __lowerCAmelCase ( self : List[Any] , *_A : Dict , **_A : Optional[Any] ) -> Tuple:
return self.tokenizer.decode(*lowercase_ , **lowercase_ )
@property
def __lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , lowercase_ , )
return self.image_processor_class
@property
def __lowerCAmelCase ( self : Optional[Any] ) -> int:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , lowercase_ , )
return self.image_processor | 331 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : str = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
lowercase__ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class a ( unittest.TestCase ):
def UpperCamelCase_ ( self ):
lowercase = logging.get_logger()
# the current default level is logging.WARNING
lowercase = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(lowercase_ )
def UpperCamelCase_ ( self ):
lowercase = logging.get_verbosity()
lowercase = logging.get_logger('transformers.models.bart.tokenization_bart' )
lowercase = '''Testing 1, 2, 3'''
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(lowercase_ ) as cl:
logger.warning(lowercase_ )
self.assertEqual(cl.out , msg + '\n' )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(lowercase_ ) as cl:
logger.warning(lowercase_ )
self.assertEqual(cl.out , '' )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(lowercase_ ) as cl:
logger.warning(lowercase_ )
self.assertEqual(cl.out , msg + '\n' )
# restore to the original level
logging.set_verbosity(lowercase_ )
@mockenv(TRANSFORMERS_VERBOSITY='error' )
def UpperCamelCase_ ( self ):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
lowercase = logging.get_logger('transformers.models.bart.tokenization_bart' )
lowercase = os.getenv('TRANSFORMERS_VERBOSITY' , lowercase_ )
lowercase = logging.log_levels[env_level_str]
lowercase = logging.get_verbosity()
self.assertEqual(
lowercase_ , lowercase_ , F'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}' , )
# restore to the original level
lowercase = ''''''
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY='super-error' )
def UpperCamelCase_ ( self ):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
lowercase = logging.logging.getLogger()
with CaptureLogger(lowercase_ ) as cl:
# this action activates the env var
logging.get_logger('transformers.models.bart.tokenization_bart' )
self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error' , cl.out )
# no need to restore as nothing was changed
def UpperCamelCase_ ( self ):
# testing `logger.warning_advice()`
transformers.utils.logging._reset_library_root_logger()
lowercase = logging.get_logger('transformers.models.bart.tokenization_bart' )
lowercase = '''Testing 1, 2, 3'''
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1' ):
# nothing should be logged as env var disables this method
with CaptureLogger(lowercase_ ) as cl:
logger.warning_advice(lowercase_ )
self.assertEqual(cl.out , '' )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='' ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(lowercase_ ) as cl:
logger.warning_advice(lowercase_ )
self.assertEqual(cl.out , msg + '\n' )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 220 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : str = XLMRobertaTokenizer
_lowerCAmelCase : int = XLMRobertaTokenizerFast
_lowerCAmelCase : str = True
_lowerCAmelCase : Dict = True
def _snake_case ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ : List[str] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def _snake_case ( self : str ):
snake_case_ : List[Any] = '''<pad>'''
snake_case_ : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def _snake_case ( self : Union[str, Any] ):
snake_case_ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowercase_ ) , 1002 )
def _snake_case ( self : Union[str, Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def _snake_case ( self : Dict ):
snake_case_ : Optional[Any] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
snake_case_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowercase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
snake_case_ : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
snake_case_ : List[Any] = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _snake_case ( self : List[str] ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
snake_case_ : int = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : int = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : Tuple = tokenizer_r.save_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
snake_case_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : Union[str, Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[str] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : List[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[Any] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : Tuple = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
snake_case_ : Optional[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : Dict = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@cached_property
def _snake_case ( self : List[str] ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _snake_case ( self : Optional[Any] ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowercase_ , f.name )
snake_case_ : Any = XLMRobertaTokenizer(f.name , keep_accents=lowercase_ )
snake_case_ : List[Any] = pickle.dumps(lowercase_ )
pickle.loads(lowercase_ )
def _snake_case ( self : Tuple ):
if not self.test_rust_tokenizer:
return
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ : Optional[int] = self.get_rust_tokenizer()
snake_case_ : Dict = '''I was born in 92000, and this is falsé.'''
snake_case_ : Optional[int] = tokenizer.tokenize(lowercase_ )
snake_case_ : Tuple = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : List[str] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
snake_case_ : str = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ : Any = tokenizer.encode(lowercase_ )
snake_case_ : int = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
@slow
def _snake_case ( self : Tuple ):
snake_case_ : int = '''Hello World!'''
snake_case_ : int = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : List[Any] ):
snake_case_ : Any = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
snake_case_ : Optional[int] = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : Dict ):
# fmt: off
snake_case_ : int = {'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 264 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> str:
if len(_a ) <= 1:
return [tuple(_a )]
lowerCAmelCase_ : Optional[int] = []
def generate(lowerCAmelCase_ , lowerCAmelCase_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , _a )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
lowerCAmelCase_ : Optional[int] = arr[k - 1], arr[i]
else: # k is odd
lowerCAmelCase_ : Any = arr[k - 1], arr[0]
generate(k - 1 , _a )
generate(len(_a ) , _a )
return res
if __name__ == "__main__":
_UpperCAmelCase : Any =input("""Enter numbers separated by a comma:\n""").strip()
_UpperCAmelCase : Union[str, Any] =[int(item) for item in user_input.split(""",""")]
print(heaps(arr)) | 262 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[Any] = """gpt_neox"""
def __init__( self : List[str] , lowercase_ : str=50432 , lowercase_ : List[Any]=6144 , lowercase_ : List[Any]=44 , lowercase_ : Union[str, Any]=64 , lowercase_ : List[str]=24576 , lowercase_ : List[Any]="gelu" , lowercase_ : str=0.25 , lowercase_ : Optional[int]=10000 , lowercase_ : Optional[int]=0.0 , lowercase_ : Optional[int]=0.0 , lowercase_ : int=0.1 , lowercase_ : Tuple=2048 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : List[str]=1E-5 , lowercase_ : str=True , lowercase_ : str=0 , lowercase_ : Union[str, Any]=2 , lowercase_ : List[str]=False , lowercase_ : Optional[int]=True , lowercase_ : List[Any]=None , **lowercase_ : Optional[int] , ):
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : Dict = num_attention_heads
snake_case_ : List[Any] = intermediate_size
snake_case_ : List[Any] = hidden_act
snake_case_ : str = rotary_pct
snake_case_ : Dict = rotary_emb_base
snake_case_ : Optional[int] = attention_dropout
snake_case_ : Tuple = hidden_dropout
snake_case_ : Tuple = classifier_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Any = use_cache
snake_case_ : Optional[int] = tie_word_embeddings
snake_case_ : Any = use_parallel_residual
snake_case_ : Union[str, Any] = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def _snake_case ( self : Optional[int] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , lowercase_ ) 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}" )
snake_case_ : Any = self.rope_scaling.get('''type''' , lowercase_ )
snake_case_ : Union[str, Any] = self.rope_scaling.get('''factor''' , lowercase_ )
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(lowercase_ , lowercase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 264 | 0 |
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( lowerCAmelCase__ ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> int:
'''simple docstring'''
super().__init__(
lowercase_ , split=lowercase_ , features=lowercase_ , cache_dir=lowercase_ , keep_in_memory=lowercase_ , streaming=lowercase_ , num_proc=lowercase_ , **lowercase_ , )
UpperCAmelCase : Dict = field
UpperCAmelCase : Optional[int] = path_or_paths if isinstance(lowercase_ , lowercase_ ) else {self.split: path_or_paths}
UpperCAmelCase : List[Any] = Json(
cache_dir=lowercase_ , data_files=lowercase_ , features=lowercase_ , field=lowercase_ , **lowercase_ , )
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
if self.streaming:
UpperCAmelCase : str = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Optional[int] = None
UpperCAmelCase : List[str] = None
UpperCAmelCase : str = None
self.builder.download_and_prepare(
download_config=lowercase_ , download_mode=lowercase_ , verification_mode=lowercase_ , base_path=lowercase_ , num_proc=self.num_proc , )
UpperCAmelCase : Tuple = self.builder.as_dataset(
split=self.split , verification_mode=lowercase_ , in_memory=self.keep_in_memory )
return dataset
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> List[Any]:
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(F"num_proc {num_proc} must be an integer > 0." )
UpperCAmelCase : Any = dataset
UpperCAmelCase : str = path_or_buf
UpperCAmelCase : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : int = num_proc
UpperCAmelCase : Union[str, Any] = '''utf-8'''
UpperCAmelCase : List[Any] = to_json_kwargs
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Optional[int] = self.to_json_kwargs.pop("""path_or_buf""" , lowercase_ )
UpperCAmelCase : List[Any] = self.to_json_kwargs.pop("""orient""" , """records""" )
UpperCAmelCase : str = self.to_json_kwargs.pop("""lines""" , True if orient == """records""" else False )
UpperCAmelCase : Union[str, Any] = self.to_json_kwargs.pop("""index""" , False if orient in ["""split""", """table"""] else True )
UpperCAmelCase : List[str] = self.to_json_kwargs.pop("""compression""" , lowercase_ )
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=lowercase_ ) as buffer:
UpperCAmelCase : str = self._write(file_obj=lowercase_ , orient=lowercase_ , lines=lowercase_ , index=lowercase_ , **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.""" )
UpperCAmelCase : List[str] = self._write(
file_obj=self.path_or_buf , orient=lowercase_ , lines=lowercase_ , index=lowercase_ , **self.to_json_kwargs )
return written
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Tuple = args
UpperCAmelCase : Dict = query_table(
table=self.dataset.data , key=slice(lowercase_ , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Optional[int] = batch.to_pandas().to_json(
path_or_buf=lowercase_ , orient=lowercase_ , lines=lowercase_ , index=lowercase_ , **lowercase_ )
if not json_str.endswith("""\n""" ):
json_str += "\n"
return json_str.encode(self.encoding )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ) -> str:
'''simple docstring'''
UpperCAmelCase : List[Any] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating json from Arrow format""" , ):
UpperCAmelCase : List[Any] = self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(lowercase_ )
else:
UpperCAmelCase : Any = 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 , lowercase_ , lowercase_ )] , ) , 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(lowercase_ )
return written
| 109 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowercase__ : int = None
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : List[str] = '''▁'''
lowercase__ : Optional[int] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
lowercase__ : str = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
'''tokenizer_file''': {
'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
},
}
lowercase__ : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
_lowerCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Tuple = PegasusTokenizer
_lowerCAmelCase : str = ["""input_ids""", """attention_mask"""]
def __init__( self : Any , lowercase_ : Optional[Any]=None , lowercase_ : int=None , lowercase_ : Tuple="<pad>" , lowercase_ : int="</s>" , lowercase_ : Tuple="<unk>" , lowercase_ : str="<mask_2>" , lowercase_ : Optional[Any]="<mask_1>" , lowercase_ : str=None , lowercase_ : List[str]=103 , **lowercase_ : List[Any] , ):
snake_case_ : Dict = offset
if additional_special_tokens is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError(
f"additional_special_tokens should be of type {type(lowercase_ )}, but is"
f" {type(lowercase_ )}" )
snake_case_ : str = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f"<unk_{i}>" for i in range(len(lowercase_ ) , self.offset - 1 )
]
if len(set(lowercase_ ) ) != len(lowercase_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}." )
snake_case_ : Union[str, Any] = additional_special_tokens_extended
else:
snake_case_ : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f"<unk_{i}>" for i in range(2 , self.offset )]
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , pad_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , mask_token=lowercase_ , mask_token_sent=lowercase_ , offset=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
snake_case_ : List[Any] = vocab_file
snake_case_ : List[Any] = False if not self.vocab_file else True
def _snake_case ( self : str , lowercase_ : Union[str, Any] ):
snake_case_ : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
f" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}" )
return [1 if x in all_special_ids else 0 for x in seq]
def _snake_case ( self : int , lowercase_ : List , lowercase_ : Optional[List] = None , lowercase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(lowercase_ )
elif token_ids_a is None:
return self._special_token_mask(lowercase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _snake_case ( self : List[Any] , lowercase_ : Optional[int] , lowercase_ : str=None ):
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 _snake_case ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ : Dict = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
return (out_vocab_file,)
| 264 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ : Dict = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
lowerCAmelCase__ : str = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
UpperCAmelCase__ = int(re.match(r'.*layer_(\d*).*' , _a )[1] )
layer_number -= 3
return f'''h.{layer_number}.''' + key
def a_ ( lowerCamelCase ):
if dtype == torch.bool:
return 1 / 8
UpperCAmelCase__ = re.search(r'[^\d](\d+)$' , str(_a ) )
if bit_search is None:
raise ValueError(f'''`dtype` is not a valid dtype: {dtype}.''' )
UpperCAmelCase__ = int(bit_search.groups()[0] )
return bit_size // 8
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# Construct model
if bloom_config_file == "":
UpperCAmelCase__ = BloomConfig()
else:
UpperCAmelCase__ = BloomConfig.from_json_file(_a )
if shard_model:
UpperCAmelCase__ = os.listdir(_a )
UpperCAmelCase__ = sorted(filter(lambda lowerCamelCase : s.startswith('layer' ) and "model_00" in s , _a ) )
UpperCAmelCase__ = {'''weight_map''': {}, '''metadata''': {}}
UpperCAmelCase__ = 0
UpperCAmelCase__ = None
UpperCAmelCase__ = BloomConfig()
for j, file in enumerate(_a ):
print('Processing file: {}'.format(_a ) )
UpperCAmelCase__ = None
for i in range(_a ):
# load all TP files
UpperCAmelCase__ = file.replace('model_00' , f'''model_0{i}''' )
UpperCAmelCase__ = torch.load(os.path.join(_a , _a ) , map_location='cpu' )
# Rename keys in the transformers names
UpperCAmelCase__ = list(temp.keys() )
for key in keys:
UpperCAmelCase__ = temp.pop(_a )
if tensors is None:
UpperCAmelCase__ = temp
else:
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
UpperCAmelCase__ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
UpperCAmelCase__ = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
UpperCAmelCase__ = tensors[key] / pretraining_tp
torch.save(
_a , os.path.join(
_a , 'pytorch_model_{}-of-{}.bin'.format(str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
UpperCAmelCase__ = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
UpperCAmelCase__ = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) )
UpperCAmelCase__ = BloomConfig()
UpperCAmelCase__ = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
UpperCAmelCase__ = total_size
with open(_a , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(_a , WEIGHTS_NAME + '.index.json' ) , 'w' , encoding='utf-8' ) as f:
UpperCAmelCase__ = json.dumps(_a , indent=2 , sort_keys=_a ) + '''\n'''
f.write(_a )
else:
UpperCAmelCase__ = BloomModel(_a )
UpperCAmelCase__ = os.listdir(_a )
UpperCAmelCase__ = sorted(filter(lambda lowerCamelCase : s.startswith('layer' ) and "model_00" in s , _a ) )
UpperCAmelCase__ = None
for i, file in enumerate(_a ):
UpperCAmelCase__ = None
for i in range(_a ):
# load all TP files
UpperCAmelCase__ = file.replace('model_00' , f'''model_0{i}''' )
UpperCAmelCase__ = torch.load(os.path.join(_a , _a ) , map_location='cpu' )
# Rename keys in the transformers names
UpperCAmelCase__ = list(temp.keys() )
for key in keys:
UpperCAmelCase__ = temp.pop(_a )
if tensors is None:
UpperCAmelCase__ = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
UpperCAmelCase__ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
UpperCAmelCase__ = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
UpperCAmelCase__ = tensors[key] / pretraining_tp
UpperCAmelCase__ = model.load_state_dict(_a , strict=_a )
assert not other_keys.unexpected_keys, f'''The keys {other_keys.unexpected_keys} are unexpected'''
if missing_keys is None:
UpperCAmelCase__ = set(other_keys.missing_keys )
else:
UpperCAmelCase__ = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f'''The keys {missing_keys} are missing'''
# Save pytorch-model
os.makedirs(_a , exist_ok=_a )
UpperCAmelCase__ = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
UpperCAmelCase__ = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}''' )
if config.torch_dtype is not None:
UpperCAmelCase__ = model.to(config.torch_dtype )
torch.save(model.state_dict() , _a )
print(f'''Save configuration file to {pytorch_config_dump_path}''' )
with open(_a , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCAmelCase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--bloom_checkpoint_path',
default=None,
type=str,
required=True,
help='Path to the Megatron-LM checkpoint path.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--bloom_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--shard_model',
action='store_true',
help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
)
parser.add_argument(
'--pretraining_tp',
default=4,
type=int,
help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
)
lowerCAmelCase__ : List[Any] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 98 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int=13 , lowercase_ : Optional[int]=7 , lowercase_ : Any=True , lowercase_ : Dict=True , lowercase_ : Dict=True , lowercase_ : Optional[Any]=99 , lowercase_ : Union[str, Any]=32 , lowercase_ : str=5 , lowercase_ : Union[str, Any]=4 , lowercase_ : Any=37 , lowercase_ : Tuple="gelu" , lowercase_ : Dict=0.1 , lowercase_ : Tuple=0.1 , lowercase_ : Optional[int]=512 , lowercase_ : Optional[Any]=16 , lowercase_ : Optional[Any]=2 , lowercase_ : Optional[Any]=0.02 , lowercase_ : List[Any]=3 , lowercase_ : Union[str, Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ : Any = parent
snake_case_ : List[str] = batch_size
snake_case_ : List[Any] = seq_length
snake_case_ : Optional[int] = is_training
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Optional[Any] = use_labels
snake_case_ : Union[str, Any] = vocab_size
snake_case_ : Any = hidden_size
snake_case_ : List[Any] = num_hidden_layers
snake_case_ : Any = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : Tuple = max_position_embeddings
snake_case_ : int = type_vocab_size
snake_case_ : Tuple = type_sequence_label_size
snake_case_ : str = initializer_range
snake_case_ : Tuple = num_labels
snake_case_ : str = num_choices
snake_case_ : Any = scope
snake_case_ : Dict = self.vocab_size - 1
def _snake_case ( self : int ):
snake_case_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Optional[Any] = None
if self.use_token_type_ids:
snake_case_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : str = None
snake_case_ : Dict = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ : int = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
snake_case_ : Any = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _snake_case ( self : Tuple , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Dict , *lowercase_ : Dict ):
snake_case_ : List[Any] = OpenAIGPTModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Any = model(lowercase_ , token_type_ids=lowercase_ , head_mask=lowercase_ )
snake_case_ : Optional[Any] = model(lowercase_ , token_type_ids=lowercase_ )
snake_case_ : Optional[Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : List[Any] , *lowercase_ : Optional[Any] ):
snake_case_ : Union[str, Any] = OpenAIGPTLMHeadModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Union[str, Any] = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : List[str] , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Dict , *lowercase_ : Union[str, Any] ):
snake_case_ : Tuple = OpenAIGPTDoubleHeadsModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Dict = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : Any , lowercase_ : str , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , *lowercase_ : Any ):
snake_case_ : int = self.num_labels
snake_case_ : Any = OpenAIGPTForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Optional[Any] = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : int ):
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : str = config_and_inputs
snake_case_ : str = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : Dict = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_lowerCAmelCase : int = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_lowerCAmelCase : Union[str, Any] = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _snake_case ( self : Tuple , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _snake_case ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str]=False ):
snake_case_ : Dict = super()._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowercase_ , )
snake_case_ : int = inputs_dict['''labels''']
snake_case_ : Optional[Any] = inputs_dict['''labels''']
snake_case_ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowercase_ , )
snake_case_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase_ )
return inputs_dict
def _snake_case ( self : Any ):
snake_case_ : List[str] = OpenAIGPTModelTester(self )
snake_case_ : Dict = ConfigTester(self , config_class=lowercase_ , n_embd=37 )
def _snake_case ( self : List[str] ):
self.config_tester.run_common_tests()
def _snake_case ( self : Optional[Any] ):
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowercase_ )
def _snake_case ( self : List[str] ):
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowercase_ )
def _snake_case ( self : int ):
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowercase_ )
def _snake_case ( self : List[str] ):
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_ )
@slow
def _snake_case ( self : Dict ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Optional[Any] = OpenAIGPTModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
@slow
def _snake_case ( self : Optional[int] ):
snake_case_ : Optional[Any] = OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
model.to(lowercase_ )
snake_case_ : List[str] = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=lowercase_ ) # the president is
snake_case_ : List[Any] = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
snake_case_ : Optional[Any] = model.generate(lowercase_ , do_sample=lowercase_ )
self.assertListEqual(output_ids[0].tolist() , lowercase_ )
| 264 | 0 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
a_ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
a_ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def __lowercase ( lowerCamelCase : str ):
if "://" in dataset_path:
UpperCamelCase_ : str = dataset_path.split('://' )[1]
return dataset_path
def __lowercase ( lowerCamelCase : List[str] ):
if fs is not None and fs.protocol != "file":
return True
else:
return False
def __lowercase ( lowerCamelCase : int , lowerCamelCase : List[Any] , lowerCamelCase : Optional[int] ):
UpperCamelCase_ : Union[str, Any] = not is_remote_filesystem(_a )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_a ) , fs._strip_protocol(_a ) )
else:
fs.mv(_a , _a , recursive=_a )
def __lowercase ( ):
if hasattr(fsspec.asyn , 'reset_lock' ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase_ : Any = None
UpperCamelCase_ : Tuple = None
UpperCamelCase_ : Optional[Any] = threading.Lock()
| 175 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Any , lowercase_ : TransformeraDModel , lowercase_ : AutoencoderKL , lowercase_ : KarrasDiffusionSchedulers , lowercase_ : Optional[Dict[int, str]] = None , ):
super().__init__()
self.register_modules(transformer=lowercase_ , vae=lowercase_ , scheduler=lowercase_ )
# create a imagenet -> id dictionary for easier use
snake_case_ : Tuple = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''',''' ):
snake_case_ : str = int(lowercase_ )
snake_case_ : Any = dict(sorted(self.labels.items() ) )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, List[str]] ):
if not isinstance(lowercase_ , lowercase_ ):
snake_case_ : Tuple = list(lowercase_ )
for l in label:
if l not in self.labels:
raise ValueError(
f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}." )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self : Optional[int] , lowercase_ : List[int] , lowercase_ : float = 4.0 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : int = 50 , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , ):
snake_case_ : Any = len(lowercase_ )
snake_case_ : List[str] = self.transformer.config.sample_size
snake_case_ : Union[str, Any] = self.transformer.config.in_channels
snake_case_ : str = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowercase_ , device=self.device , dtype=self.transformer.dtype , )
snake_case_ : Optional[Any] = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
snake_case_ : Optional[int] = torch.tensor(lowercase_ , device=self.device ).reshape(-1 )
snake_case_ : Dict = torch.tensor([1000] * batch_size , device=self.device )
snake_case_ : Tuple = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
snake_case_ : List[Any] = latent_model_input[: len(lowercase_ ) // 2]
snake_case_ : Union[str, Any] = torch.cat([half, half] , dim=0 )
snake_case_ : Optional[Any] = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
snake_case_ : int = t
if not torch.is_tensor(lowercase_ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
snake_case_ : Tuple = latent_model_input.device.type == '''mps'''
if isinstance(lowercase_ , lowercase_ ):
snake_case_ : List[str] = torch.floataa if is_mps else torch.floataa
else:
snake_case_ : Optional[int] = torch.intaa if is_mps else torch.intaa
snake_case_ : List[Any] = torch.tensor([timesteps] , dtype=lowercase_ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
snake_case_ : str = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
snake_case_ : Tuple = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
snake_case_ : List[Any] = self.transformer(
lowercase_ , timestep=lowercase_ , class_labels=lowercase_ ).sample
# perform guidance
if guidance_scale > 1:
snake_case_, snake_case_ : Dict = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
snake_case_, snake_case_ : Any = torch.split(lowercase_ , len(lowercase_ ) // 2 , dim=0 )
snake_case_ : int = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
snake_case_ : str = torch.cat([half_eps, half_eps] , dim=0 )
snake_case_ : List[Any] = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
snake_case_, snake_case_ : Optional[Any] = torch.split(lowercase_ , lowercase_ , dim=1 )
else:
snake_case_ : List[str] = noise_pred
# compute previous image: x_t -> x_t-1
snake_case_ : int = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
if guidance_scale > 1:
snake_case_, snake_case_ : Optional[Any] = latent_model_input.chunk(2 , dim=0 )
else:
snake_case_ : Dict = latent_model_input
snake_case_ : Union[str, Any] = 1 / self.vae.config.scaling_factor * latents
snake_case_ : Tuple = self.vae.decode(lowercase_ ).sample
snake_case_ : str = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ : Union[str, Any] = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case_ : Union[str, Any] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowercase_ )
| 264 | 0 |
'''simple docstring'''
from copy import deepcopy
class lowercase :
"""simple docstring"""
def __init__( self ,a_ = None ,a_ = None ) -> Any:
if arr is None and size is not None:
_UpperCAmelCase : Dict = size
_UpperCAmelCase : List[Any] = [0] * size
elif arr is not None:
self.init(lowercase_ )
else:
raise ValueError("""Either arr or size must be specified""" )
def _snake_case ( self ,a_ ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = len(lowercase_ )
_UpperCAmelCase : Tuple = deepcopy(lowercase_ )
for i in range(1 ,self.size ):
_UpperCAmelCase : Tuple = self.next_(lowercase_ )
if j < self.size:
self.tree[j] += self.tree[i]
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Optional[int] = self.tree[:]
for i in range(self.size - 1 ,0 ,-1 ):
_UpperCAmelCase : List[Any] = self.next_(lowercase_ )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def _snake_case ( a_ ) -> List[Any]:
return index + (index & (-index))
@staticmethod
def _snake_case ( a_ ) -> Union[str, Any]:
return index - (index & (-index))
def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]:
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
_UpperCAmelCase : Any = self.next_(lowercase_ )
def _snake_case ( self ,a_ ,a_ ) -> List[Any]:
self.add(lowercase_ ,value - self.get(lowercase_ ) )
def _snake_case ( self ,a_ ) -> Optional[Any]:
if right == 0:
return 0
_UpperCAmelCase : Optional[int] = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
_UpperCAmelCase : Union[str, Any] = self.prev(lowercase_ )
return result
def _snake_case ( self ,a_ ,a_ ) -> int:
return self.prefix(lowercase_ ) - self.prefix(lowercase_ )
def _snake_case ( self ,a_ ) -> Any:
return self.query(lowercase_ ,index + 1 )
def _snake_case ( self ,a_ ) -> Optional[Any]:
value -= self.tree[0]
if value < 0:
return -1
_UpperCAmelCase : Optional[int] = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
_UpperCAmelCase : Tuple = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 215 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self : List[Any] ):
snake_case_ : List[str] = ''''''
snake_case_ : Tuple = ''''''
snake_case_ : int = []
snake_case_ : Optional[int] = 0
snake_case_ : Optional[Any] = 256
snake_case_ : Tuple = 0
snake_case_ : Tuple = 0
snake_case_ : Optional[Any] = 0
snake_case_ : Any = 0
def _snake_case ( self : Optional[Any] , lowercase_ : List[Any] ):
snake_case_ : List[Any] = cva.imread(lowercase_ , 0 )
snake_case_ : Tuple = copy.deepcopy(self.img )
snake_case_, snake_case_, snake_case_ : List[Any] = plt.hist(self.img.ravel() , 256 , [0, 256] , label='''x''' )
snake_case_ : str = np.sum(lowercase_ )
for i in range(len(lowercase_ ) ):
snake_case_ : Optional[Any] = x[i] / self.k
self.sk += prk
snake_case_ : Any = (self.L - 1) * self.sk
if self.rem != 0:
snake_case_ : Dict = int(last % last )
snake_case_ : Union[str, Any] = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowercase_ )
snake_case_ : int = int(np.ma.count(self.img ) / self.img[1].size )
snake_case_ : Tuple = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
snake_case_ : Union[str, Any] = self.img[j][i]
if num != self.last_list[num]:
snake_case_ : List[str] = self.last_list[num]
cva.imwrite('''output_data/output.jpg''' , self.img )
def _snake_case ( self : Tuple ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _snake_case ( self : int ):
cva.imshow('''Output-Image''' , self.img )
cva.imshow('''Input-Image''' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowercase__ : Any = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
lowercase__ : Any = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 264 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_a ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 70 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] ):
snake_case_ : str = []
def _snake_case ( self : List[Any] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : List[str] , **lowercase_ : Tuple ):
self.events.append('''on_init_end''' )
def _snake_case ( self : List[Any] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_train_begin''' )
def _snake_case ( self : Any , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_train_end''' )
def _snake_case ( self : str , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any] , **lowercase_ : List[Any] ):
self.events.append('''on_epoch_begin''' )
def _snake_case ( self : Tuple , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ):
self.events.append('''on_epoch_end''' )
def _snake_case ( self : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , **lowercase_ : Optional[Any] ):
self.events.append('''on_step_begin''' )
def _snake_case ( self : int , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , **lowercase_ : List[str] ):
self.events.append('''on_step_end''' )
def _snake_case ( self : str , lowercase_ : int , lowercase_ : Dict , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_evaluate''' )
def _snake_case ( self : Dict , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : List[Any] , **lowercase_ : str ):
self.events.append('''on_predict''' )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int , **lowercase_ : Union[str, Any] ):
self.events.append('''on_save''' )
def _snake_case ( self : str , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : Any ):
self.events.append('''on_log''' )
def _snake_case ( self : Dict , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Union[str, Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_prediction_step''' )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : List[str] ):
snake_case_ : Tuple = tempfile.mkdtemp()
def _snake_case ( self : Tuple ):
shutil.rmtree(self.output_dir )
def _snake_case ( self : int , lowercase_ : Union[str, Any]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=64 , lowercase_ : Union[str, Any]=64 , lowercase_ : Union[str, Any]=None , lowercase_ : Any=False , **lowercase_ : List[Any] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
snake_case_ : int = RegressionDataset(length=lowercase_ )
snake_case_ : Any = RegressionDataset(length=lowercase_ )
snake_case_ : int = RegressionModelConfig(a=lowercase_ , b=lowercase_ )
snake_case_ : Tuple = RegressionPreTrainedModel(lowercase_ )
snake_case_ : Any = TrainingArguments(self.output_dir , disable_tqdm=lowercase_ , report_to=[] , **lowercase_ )
return Trainer(
lowercase_ , lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , callbacks=lowercase_ , )
def _snake_case ( self : Optional[int] , lowercase_ : Any , lowercase_ : List[Any] ):
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
# Order doesn't matter
snake_case_ : Any = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
snake_case_ : List[str] = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
for cba, cba in zip(lowercase_ , lowercase_ ):
if isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , lowercase_ )
elif isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , cba.__class__ )
elif not isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(cba.__class__ , lowercase_ )
else:
self.assertEqual(lowercase_ , lowercase_ )
def _snake_case ( self : Optional[Any] , lowercase_ : Tuple ):
snake_case_ : Tuple = ['''on_init_end''', '''on_train_begin''']
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = len(trainer.get_eval_dataloader() )
snake_case_ : List[Any] = ['''on_prediction_step'''] * len(trainer.get_eval_dataloader() ) + ['''on_log''', '''on_evaluate''']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('''on_epoch_begin''' )
for _ in range(lowercase_ ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('''on_log''' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('''on_save''' )
expected_events.append('''on_epoch_end''' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _snake_case ( self : List[str] ):
snake_case_ : Union[str, Any] = self.get_trainer()
snake_case_ : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# Callbacks passed at init are added to the default callbacks
snake_case_ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
snake_case_ : Optional[int] = self.get_trainer(disable_tqdm=lowercase_ )
snake_case_ : List[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : int ):
snake_case_ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
snake_case_ : List[Any] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : Dict = self.get_trainer()
snake_case_ : Optional[int] = trainer.pop_callback(lowercase_ )
self.assertEqual(cb.__class__ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# We can also add, pop, or remove by instance
snake_case_ : Optional[int] = self.get_trainer()
snake_case_ : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : List[Any] = self.get_trainer()
snake_case_ : Optional[int] = trainer.callback_handler.callbacks[0]
snake_case_ : Optional[Any] = trainer.pop_callback(lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : List[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='''ignore''' , category=lowercase_ )
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# Independent log/save/eval
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
snake_case_ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='''steps''' )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='''epoch''' )
trainer.train()
snake_case_ : Dict = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# A bit of everything
snake_case_ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy='''steps''' , )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# warning should be emitted for duplicated callbacks
with patch('''transformers.trainer_callback.logger.warning''' ) as warn_mock:
snake_case_ : Dict = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(lowercase_ ) in warn_mock.call_args[0][0]
| 264 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
_a = 8
def __a ( __lowerCamelCase, __lowerCamelCase=BITS ):
UpperCAmelCase_ : Any = x.device
UpperCAmelCase_ : str = (x * 255).int().clamp(0, 255 )
UpperCAmelCase_ : Tuple = 2 ** torch.arange(bits - 1, -1, -1, device=_a )
UpperCAmelCase_ : Tuple = rearrange(_a, "d -> d 1 1" )
UpperCAmelCase_ : Any = rearrange(_a, "b c h w -> b c 1 h w" )
UpperCAmelCase_ : List[str] = ((x & mask) != 0).float()
UpperCAmelCase_ : Optional[int] = rearrange(_a, "b c d h w -> b (c d) h w" )
UpperCAmelCase_ : Union[str, Any] = bits * 2 - 1
return bits
def __a ( __lowerCamelCase, __lowerCamelCase=BITS ):
UpperCAmelCase_ : str = x.device
UpperCAmelCase_ : Any = (x > 0).int()
UpperCAmelCase_ : Any = 2 ** torch.arange(bits - 1, -1, -1, device=_a, dtype=torch.intaa )
UpperCAmelCase_ : List[str] = rearrange(_a, "d -> d 1 1" )
UpperCAmelCase_ : Dict = rearrange(_a, "b (c d) h w -> b c d h w", d=8 )
UpperCAmelCase_ : Optional[Any] = reduce(x * mask, "b c d h w -> b c h w", "sum" )
return (dec / 255).clamp(0.0, 1.0 )
def __a ( self, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 0.0, __lowerCamelCase = True, __lowerCamelCase=None, __lowerCamelCase = True, ):
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler" )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
UpperCAmelCase_ : List[Any] = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
UpperCAmelCase_ : int = self.alphas_cumprod[timestep]
UpperCAmelCase_ : Tuple = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
UpperCAmelCase_ : Tuple = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase_ : Optional[int] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
UpperCAmelCase_ : Tuple = self.bit_scale
if self.config.clip_sample:
UpperCAmelCase_ : Dict = torch.clamp(_a, -scale, _a )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
UpperCAmelCase_ : Optional[int] = self._get_variance(_a, _a )
UpperCAmelCase_ : Union[str, Any] = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
UpperCAmelCase_ : Union[str, Any] = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase_ : Optional[int] = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase_ : Tuple = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
UpperCAmelCase_ : List[str] = model_output.device if torch.is_tensor(_a ) else '''cpu'''
UpperCAmelCase_ : Tuple = torch.randn(model_output.shape, dtype=model_output.dtype, generator=_a ).to(_a )
UpperCAmelCase_ : Optional[Any] = self._get_variance(_a, _a ) ** 0.5 * eta * noise
UpperCAmelCase_ : Optional[int] = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=_a, pred_original_sample=_a )
def __a ( self, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase="epsilon", __lowerCamelCase=None, __lowerCamelCase = True, ):
UpperCAmelCase_ : Optional[Any] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
UpperCAmelCase_ : List[str] = torch.split(_a, sample.shape[1], dim=1 )
else:
UpperCAmelCase_ : Union[str, Any] = None
# 1. compute alphas, betas
UpperCAmelCase_ : Union[str, Any] = self.alphas_cumprod[t]
UpperCAmelCase_ : Optional[int] = self.alphas_cumprod[t - 1] if t > 0 else self.one
UpperCAmelCase_ : Optional[Any] = 1 - alpha_prod_t
UpperCAmelCase_ : Any = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
UpperCAmelCase_ : Optional[int] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
UpperCAmelCase_ : Union[str, Any] = model_output
else:
raise ValueError(f"""Unsupported prediction_type {prediction_type}.""" )
# 3. Clip "predicted x_0"
UpperCAmelCase_ : Union[str, Any] = self.bit_scale
if self.config.clip_sample:
UpperCAmelCase_ : str = torch.clamp(_a, -scale, _a )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase_ : Tuple = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
UpperCAmelCase_ : Any = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase_ : str = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCAmelCase_ : str = 0
if t > 0:
UpperCAmelCase_ : int = torch.randn(
model_output.size(), dtype=model_output.dtype, layout=model_output.layout, generator=_a ).to(model_output.device )
UpperCAmelCase_ : List[Any] = (self._get_variance(_a, predicted_variance=_a ) ** 0.5) * noise
UpperCAmelCase_ : List[str] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=_a, pred_original_sample=_a )
class A_ (lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , lowercase_ , lowercase_ , lowercase_ = 1.0 , ):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ : Union[str, Any] = bit_scale
UpperCAmelCase_ : Any = (
ddim_bit_scheduler_step if isinstance(lowercase_ , lowercase_ ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self , lowercase_ = 256 , lowercase_ = 256 , lowercase_ = 50 , lowercase_ = None , lowercase_ = 1 , lowercase_ = "pil" , lowercase_ = True , **lowercase_ , ):
"""simple docstring"""
UpperCAmelCase_ : int = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) , generator=lowercase_ , )
UpperCAmelCase_ : str = decimal_to_bits(lowercase_ ) * self.bit_scale
UpperCAmelCase_ : Dict = latents.to(self.device )
self.scheduler.set_timesteps(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
UpperCAmelCase_ : Any = self.unet(lowercase_ , lowercase_ ).sample
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase_ : Dict = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
UpperCAmelCase_ : Tuple = bits_to_decimal(lowercase_ )
if output_type == "pil":
UpperCAmelCase_ : List[Any] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase_ )
| 61 |
"""simple docstring"""
import numpy as np
def __lowercase ( _a ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case : Dict = logging.get_logger(__name__)
def _UpperCAmelCase ( a__ , a__=False):
'''simple docstring'''
a_ : List[str] = []
# fmt: off
# stem:
rename_keys.append(("""cls_token""", """vit.embeddings.cls_token"""))
rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings"""))
rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""))
rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""))
# backbone
rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight"""))
rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight"""))
rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias"""))
for stage_idx in range(len(config.backbone_config.depths)):
for layer_idx in range(config.backbone_config.depths[stage_idx]):
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight'''))
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias'''))
# transformer encoder
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight'''))
rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias'''))
rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight'''))
rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias'''))
rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight'''))
rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias'''))
rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight'''))
rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias'''))
rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight'''))
rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias'''))
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
])
# if just the base model, we should remove "vit" from all keys that start with "vit"
a_ : Optional[int] = [(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"""),
])
# fmt: on
return rename_keys
def _UpperCAmelCase ( a__ , a__ , a__=False):
'''simple docstring'''
for i in range(config.num_hidden_layers):
if base_model:
a_ : List[str] = ''''''
else:
a_ : Dict = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
a_ : List[str] = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''')
a_ : Optional[int] = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''')
# next, add query, keys and values (in that order) to the state dict
a_ : Any = in_proj_weight[
: config.hidden_size, :
]
a_ : Dict = in_proj_bias[: config.hidden_size]
a_ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
a_ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
a_ : Dict = in_proj_weight[
-config.hidden_size :, :
]
a_ : str = in_proj_bias[-config.hidden_size :]
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : Dict = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(_a , _a)
def _UpperCAmelCase ( a__ , a__ , a__):
'''simple docstring'''
a_ : Union[str, Any] = dct.pop(_a)
a_ : Union[str, Any] = val
def _UpperCAmelCase ( ):
'''simple docstring'''
a_ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
a_ : Tuple = Image.open(requests.get(_a , stream=_a).raw)
return im
@torch.no_grad()
def _UpperCAmelCase ( a__ , a__ , a__=False):
'''simple docstring'''
a_ : str = BitConfig(
global_padding="""same""" , layer_type="""bottleneck""" , depths=(3, 4, 9) , out_features=["""stage3"""] , embedding_dynamic_padding=_a , )
a_ : Tuple = ViTHybridConfig(backbone_config=_a , image_size=3_8_4 , num_labels=1_0_0_0)
a_ : int = False
# load original model from timm
a_ : str = timm.create_model(_a , pretrained=_a)
timm_model.eval()
# load state_dict of original model, remove and rename some keys
a_ : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_a)
a_ : int = create_rename_keys(_a , _a)
for src, dest in rename_keys:
rename_key(_a , _a , _a)
read_in_q_k_v(_a , _a , _a)
a_ : Optional[Any] = '''huggingface/label-files'''
a_ : Any = '''imagenet-1k-id2label.json'''
a_ : Dict = json.load(open(hf_hub_download(_a , _a , repo_type="""dataset""") , """r"""))
a_ : Dict = {int(_a): v for k, v in idalabel.items()}
a_ : Optional[int] = idalabel
a_ : Optional[Any] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
a_ : Optional[Any] = ViTHybridModel(_a).eval()
else:
a_ : Any = ViTHybridForImageClassification(_a).eval()
model.load_state_dict(_a)
# create image processor
a_ : Optional[Any] = create_transform(**resolve_data_config({} , model=_a))
a_ : List[Any] = transform.transforms
a_ : Optional[Any] = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
a_ : List[Any] = ViTHybridImageProcessor(
do_resize=_a , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_a , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=_a , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
a_ : Optional[int] = prepare_img()
a_ : Optional[int] = transform(_a).unsqueeze(0)
a_ : int = processor(_a , return_tensors="""pt""").pixel_values
# verify pixel values
assert torch.allclose(_a , _a)
# verify logits
with torch.no_grad():
a_ : List[str] = model(_a)
a_ : Any = outputs.logits
print("""Predicted class:""" , logits.argmax(-1).item())
if base_model:
a_ : Optional[Any] = timm_model.forward_features(_a)
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_a , outputs.pooler_output , atol=1e-3)
else:
a_ : int = timm_model(_a)
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_a , outputs.logits , atol=1e-3)
print("""Looks ok!""")
if pytorch_dump_folder_path is not None:
Path(_a).mkdir(exist_ok=_a)
print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}''')
model.save_pretrained(_a)
print(f'''Saving processor to {pytorch_dump_folder_path}''')
processor.save_pretrained(_a)
if push_to_hub:
print(f'''Pushing model and processor to the hub {vit_name}''')
model.push_to_hub(f'''ybelkada/{vit_name}''')
processor.push_to_hub(f'''ybelkada/{vit_name}''')
if __name__ == "__main__":
__snake_case : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--vit_name""",
default="""vit_base_r50_s16_384""",
type=str,
help="""Name of the hybrid ViT timm model you\'d like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
__snake_case : Any = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 248 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] , lowercase_ : str , lowercase_ : int ):
snake_case_ : Dict = params
snake_case_ : Union[str, Any] = np.array(lowercase_ )
snake_case_ : str = np.array([len(lowercase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : Dict , lowercase_ : Union[str, Any] ):
return (self.token_ids[index], self.lengths[index])
def __len__( self : List[Any] ):
return len(self.lengths )
def _snake_case ( self : Tuple ):
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def _snake_case ( self : Tuple ):
snake_case_ : str = self.params.max_model_input_size
snake_case_ : Dict = self.lengths > max_len
logger.info(f"Splitting {sum(lowercase_ )} too long sequences." )
def divide_chunks(lowercase_ : Tuple , lowercase_ : Optional[Any] ):
return [l[i : i + n] for i in range(0 , len(lowercase_ ) , lowercase_ )]
snake_case_ : Tuple = []
snake_case_ : Any = []
if self.params.mlm:
snake_case_, snake_case_ : Union[str, Any] = self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
else:
snake_case_, snake_case_ : Dict = self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
snake_case_ : Any = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
snake_case_ : Dict = np.insert(lowercase_ , 0 , lowercase_ )
if sub_s[-1] != sep_id:
snake_case_ : Tuple = np.insert(lowercase_ , len(lowercase_ ) , lowercase_ )
assert len(lowercase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowercase_ )
new_tok_ids.extend(lowercase_ )
new_lengths.extend([len(lowercase_ ) for l in sub_seqs] )
snake_case_ : List[str] = np.array(lowercase_ )
snake_case_ : Optional[Any] = np.array(lowercase_ )
def _snake_case ( self : Optional[int] ):
snake_case_ : List[Any] = len(self )
snake_case_ : List[str] = self.lengths > 11
snake_case_ : Dict = self.token_ids[indices]
snake_case_ : Dict = self.lengths[indices]
snake_case_ : str = len(self )
logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def _snake_case ( self : Tuple ):
if "unk_token" not in self.params.special_tok_ids:
return
else:
snake_case_ : str = self.params.special_tok_ids['''unk_token''']
snake_case_ : str = len(self )
snake_case_ : int = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
snake_case_ : str = (unk_occs / self.lengths) < 0.5
snake_case_ : Optional[Any] = self.token_ids[indices]
snake_case_ : Optional[int] = self.lengths[indices]
snake_case_ : Dict = len(self )
logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def _snake_case ( self : Dict ):
if not self.params.is_master:
return
logger.info(f"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def _snake_case ( self : List[str] , lowercase_ : Dict ):
snake_case_ : Optional[int] = [t[0] for t in batch]
snake_case_ : str = [t[1] for t in batch]
assert len(lowercase_ ) == len(lowercase_ )
# Max for paddings
snake_case_ : str = max(lowercase_ )
# Pad token ids
if self.params.mlm:
snake_case_ : Tuple = self.params.special_tok_ids['''pad_token''']
else:
snake_case_ : Dict = self.params.special_tok_ids['''unk_token''']
snake_case_ : Any = [list(t.astype(lowercase_ ) ) + [pad_idx] * (max_seq_len_ - len(lowercase_ )) for t in token_ids]
assert len(tk_ ) == len(lowercase_ )
assert all(len(lowercase_ ) == max_seq_len_ for t in tk_ )
snake_case_ : str = torch.tensor(tk_ ) # (bs, max_seq_len_)
snake_case_ : Optional[int] = torch.tensor(lowercase_ ) # (bs)
return tk_t, lg_t
| 264 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> List[str]:
lowerCAmelCase = list(_a )
lowerCAmelCase = list(_a )
lowerCAmelCase = 0
for i in range(len(_a ) ):
if lista[i] != lista[i]:
count += 1
lowerCAmelCase = '''_'''
if count > 1:
return False
else:
return "".join(_a )
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> Dict:
lowerCAmelCase = []
while True:
lowerCAmelCase = ['''$'''] * len(_a )
lowerCAmelCase = []
for i in range(len(_a ) ):
for j in range(i + 1 , len(_a ) ):
lowerCAmelCase = compare_string(binary[i] , binary[j] )
if k is False:
lowerCAmelCase = '''*'''
lowerCAmelCase = '''*'''
temp.append('''X''' )
for i in range(len(_a ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(_a ) == 0:
return pi
lowerCAmelCase = list(set(_a ) )
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> Optional[int]:
lowerCAmelCase = []
for minterm in minterms:
lowerCAmelCase = ''''''
for _ in range(_a ):
lowerCAmelCase = str(minterm % 2 ) + string
minterm //= 2
temp.append(_a )
return temp
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ ) -> Any:
lowerCAmelCase = list(_a )
lowerCAmelCase = list(_a )
lowerCAmelCase = 0
for i in range(len(_a ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> Union[str, Any]:
lowerCAmelCase = []
lowerCAmelCase = [0] * len(_a )
for i in range(len(chart[0] ) ):
lowerCAmelCase = 0
lowerCAmelCase = -1
for j in range(len(_a ) ):
if chart[j][i] == 1:
count += 1
lowerCAmelCase = j
if count == 1:
lowerCAmelCase = 1
for i in range(len(_a ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(_a ) ):
lowerCAmelCase = 0
temp.append(prime_implicants[i] )
while True:
lowerCAmelCase = 0
lowerCAmelCase = -1
lowerCAmelCase = 0
for i in range(len(_a ) ):
lowerCAmelCase = chart[i].count(1 )
if count_n > max_n:
lowerCAmelCase = count_n
lowerCAmelCase = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(_a ) ):
lowerCAmelCase = 0
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> Tuple:
lowerCAmelCase = [[0 for x in range(len(_a ) )] for x in range(len(_a ) )]
for i in range(len(_a ) ):
lowerCAmelCase = prime_implicants[i].count('''_''' )
for j in range(len(_a ) ):
if is_for_table(prime_implicants[i] , binary[j] , _a ):
lowerCAmelCase = 1
return chart
def SCREAMING_SNAKE_CASE_ ( ) -> List[Any]:
lowerCAmelCase = int(input('''Enter the no. of variables\n''' ) )
lowerCAmelCase = [
float(_a )
for x in input(
'''Enter the decimal representation of Minterms \'Spaces Separated\'\n''' ).split()
]
lowerCAmelCase = decimal_to_binary(_a , _a )
lowerCAmelCase = check(_a )
print('''Prime Implicants are:''' )
print(_a )
lowerCAmelCase = prime_implicant_chart(_a , _a )
lowerCAmelCase = selection(_a , _a )
print('''Essential Prime Implicants are:''' )
print(_a )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 338 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __lowercase ( _a , _a , _a = "x" , _a = 10**-10 , _a = 1 , ):
snake_case_ : Any = symbols(_a )
snake_case_ : int = lambdify(_a , _a )
snake_case_ : Optional[Any] = lambdify(_a , diff(_a , _a ) )
snake_case_ : Optional[Any] = starting_point
while True:
if diff_function(_a ) != 0:
snake_case_ : Optional[int] = prev_guess - multiplicity * func(_a ) / diff_function(
_a )
else:
raise ZeroDivisionError('''Could not find root''' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
snake_case_ : int = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
# Find root of polynomial
# Find fourth Root of 5
print(f'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')
# Find value of e
print(
'''The root of log(y) - 1 = 0 is ''',
f'{newton_raphson("log(y) - 1", 2, variable="y")}',
)
# Exponential Roots
print(
'''The root of exp(x) - 1 = 0 is''',
f'{newton_raphson("exp(x) - 1", 10, precision=0.005)}',
)
# Find root of cos(x)
print(f'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')
| 264 | 0 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def lowerCamelCase ( lowerCAmelCase : Optional[int] , lowerCAmelCase : Any , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : List[str] ):
"""simple docstring"""
__magic_name__ : str = cva.getAffineTransform(_a , _a )
return cva.warpAffine(_a , _a , (rows, cols) )
if __name__ == "__main__":
# read original image
lowerCAmelCase :Union[str, Any] = cva.imread(
str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
)
# turn image in gray scale value
lowerCAmelCase :Dict = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
lowerCAmelCase :Tuple = gray_img.shape
# set different points to rotate image
lowerCAmelCase :Tuple = np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
lowerCAmelCase :str = np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
lowerCAmelCase :Union[str, Any] = np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
lowerCAmelCase :int = np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)
# add all rotated images in a list
lowerCAmelCase :Tuple = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
lowerCAmelCase :Tuple = plt.figure(1)
lowerCAmelCase :Optional[Any] = ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
plt.title(titles[i])
plt.axis('''off''')
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show() | 331 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
"""simple docstring"""
import os
import re
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 : Any = logging.get_logger(__name__)
_UpperCamelCase : str = {'''vocab_file''': '''spiece.model'''}
_UpperCamelCase : Dict = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
}
}
_UpperCamelCase : Any = {
'''google/bigbird-roberta-base''': 4_0_9_6,
'''google/bigbird-roberta-large''': 4_0_9_6,
'''google/bigbird-base-trivia-itc''': 4_0_9_6,
}
class a ( lowerCAmelCase__ ):
UpperCAmelCase_ : Union[str, Any] =VOCAB_FILES_NAMES
UpperCAmelCase_ : Any =PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ : int =["""input_ids""", """attention_mask"""]
UpperCAmelCase_ : List[int] =[]
def __init__( self , _lowerCamelCase , _lowerCamelCase="<unk>" , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<pad>" , _lowerCamelCase="[SEP]" , _lowerCamelCase="[MASK]" , _lowerCamelCase="[CLS]" , _lowerCamelCase = None , **_lowerCamelCase , ):
lowercase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else bos_token
lowercase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else eos_token
lowercase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else unk_token
lowercase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else pad_token
lowercase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else cls_token
lowercase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
lowercase = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else mask_token
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , pad_token=lowercase_ , sep_token=lowercase_ , mask_token=lowercase_ , cls_token=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
lowercase = vocab_file
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase_ )
@property
def UpperCamelCase_ ( self ):
return self.sp_model.get_piece_size()
def UpperCamelCase_ ( self ):
lowercase = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self , _lowerCamelCase ):
lowercase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase_ ( self , _lowerCamelCase ):
return self.sp_model.encode(lowercase_ , out_type=lowercase_ )
def UpperCamelCase_ ( self , _lowerCamelCase ):
return self.sp_model.piece_to_id(lowercase_ )
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = self.sp_model.IdToPiece(lowercase_ )
return token
def UpperCamelCase_ ( self , _lowerCamelCase ):
lowercase = []
lowercase = ''''''
lowercase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowercase_ ) + token
lowercase = True
lowercase = []
else:
current_sub_tokens.append(lowercase_ )
lowercase = False
out_string += self.sp_model.decode(lowercase_ )
return out_string.strip()
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = False , _lowerCamelCase = None , _lowerCamelCase = True , **_lowerCamelCase , ):
lowercase = kwargs.pop('use_source_tokenizer' , lowercase_ )
lowercase = self.convert_ids_to_tokens(lowercase_ , skip_special_tokens=lowercase_ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
lowercase = []
lowercase = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowercase_ ) )
lowercase = []
sub_texts.append(lowercase_ )
else:
current_sub_text.append(lowercase_ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowercase_ ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
lowercase = re.sub(R' (\[(MASK|SEP)\])' , R'\1' , ' '.join(lowercase_ ) )
else:
lowercase = ''''''.join(lowercase_ )
lowercase = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
lowercase = self.clean_up_tokenization(lowercase_ )
return clean_text
else:
return text
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ):
if not os.path.isdir(lowercase_ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase = os.path.join(
lowercase_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ , 'wb' ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase = [self.cls_token_id]
lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = 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] + ([0] * len(lowercase_ )) + [1]
def UpperCamelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ):
lowercase = [self.sep_token_id]
lowercase = [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]
| 220 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def __lowercase ( _a ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple: # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def lowerCAmelCase ( )-> Tuple:
with parallel_backend('''spark''' ):
assert ParallelBackendConfig.backend_name == "spark"
lowerCAmelCase_ : Union[str, Any] = [1, 2, 3]
with pytest.raises(_a ):
with parallel_backend('''unsupported backend''' ):
map_nested(_a , _a , num_proc=2 )
with pytest.raises(_a ):
with parallel_backend('''unsupported backend''' ):
map_nested(_a , _a , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc''' , [2, -1] )
def lowerCAmelCase ( lowerCAmelCase_ )-> Dict:
lowerCAmelCase_ : Optional[Any] = [1, 2]
lowerCAmelCase_ : str = {'''a''': 1, '''b''': 2}
lowerCAmelCase_ : List[Any] = {'''a''': [1, 2], '''b''': [3, 4]}
lowerCAmelCase_ : Dict = {'''a''': {'''1''': 1}, '''b''': 2}
lowerCAmelCase_ : Optional[int] = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
lowerCAmelCase_ : Optional[Any] = [2, 3]
lowerCAmelCase_ : List[str] = {'''a''': 2, '''b''': 3}
lowerCAmelCase_ : Union[str, Any] = {'''a''': [2, 3], '''b''': [4, 5]}
lowerCAmelCase_ : Dict = {'''a''': {'''1''': 2}, '''b''': 3}
lowerCAmelCase_ : List[Any] = {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
with parallel_backend('''spark''' ):
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa
assert map_nested(_a , _a , num_proc=_a ) == expected_map_nested_sa | 262 |
"""simple docstring"""
import sys
lowercase__ : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __lowercase ( _a ):
snake_case_ : List[Any] = 1
for digit in s:
product *= int(_a )
return product
def __lowercase ( _a = N ):
snake_case_ : Optional[int] = -sys.maxsize - 1
snake_case_ : str = n[:13]
snake_case_ : List[Any] = 13
while cur_index < len(_a ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
snake_case_ : int = substr[1:] + n[cur_index]
cur_index += 1
else:
snake_case_ : Optional[Any] = max(_a , str_eval(_a ) )
snake_case_ : Any = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'{solution() = }')
| 264 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : List[Any] = 1000 ):
UpperCAmelCase : int = 3
UpperCAmelCase : Union[str, Any] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f"""{solution() = }""")
| 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : List[Any] = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Any = ['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : int = [
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = [
'''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDistilBertForMaskedLM''',
'''TFDistilBertForMultipleChoice''',
'''TFDistilBertForQuestionAnswering''',
'''TFDistilBertForSequenceClassification''',
'''TFDistilBertForTokenClassification''',
'''TFDistilBertMainLayer''',
'''TFDistilBertModel''',
'''TFDistilBertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''FlaxDistilBertForMaskedLM''',
'''FlaxDistilBertForMultipleChoice''',
'''FlaxDistilBertForQuestionAnswering''',
'''FlaxDistilBertForSequenceClassification''',
'''FlaxDistilBertForTokenClassification''',
'''FlaxDistilBertModel''',
'''FlaxDistilBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
lowercase__ : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
import argparse
import os
import re
lowerCAmelCase__ : List[Any] = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
lowerCAmelCase__ : Union[str, Any] = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCAmelCase__ : int = re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCAmelCase__ : Dict = re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCAmelCase__ : Union[str, Any] = re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCAmelCase__ : Optional[Any] = re.compile(r'\[([^\]]+)\]')
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = _re_indent.search(_a )
return "" if search is None else search.groups()[0]
def a_ ( lowerCamelCase , lowerCamelCase="" , lowerCamelCase=None , lowerCamelCase=None ):
UpperCAmelCase__ = 0
UpperCAmelCase__ = code.split('\n' )
if start_prompt is not None:
while not lines[index].startswith(_a ):
index += 1
UpperCAmelCase__ = ['''\n'''.join(lines[:index] )]
else:
UpperCAmelCase__ = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCAmelCase__ = [lines[index]]
index += 1
while index < len(_a ) and (end_prompt is None or not lines[index].startswith(_a )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(_a ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ' ' ):
current_block.append(lines[index] )
blocks.append('\n'.join(_a ) )
if index < len(_a ) - 1:
UpperCAmelCase__ = [lines[index + 1]]
index += 1
else:
UpperCAmelCase__ = []
else:
blocks.append('\n'.join(_a ) )
UpperCAmelCase__ = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(_a ) > 0:
blocks.append('\n'.join(_a ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(_a ):
blocks.append('\n'.join(lines[index:] ) )
return blocks
def a_ ( lowerCamelCase ):
def _inner(lowerCamelCase ):
return key(_a ).lower().replace('_' , '' )
return _inner
def a_ ( lowerCamelCase , lowerCamelCase=None ):
# If no key is provided, we use a noop.
def noop(lowerCamelCase ):
return x
if key is None:
UpperCAmelCase__ = noop
# Constants are all uppercase, they go first.
UpperCAmelCase__ = [obj for obj in objects if key(_a ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCAmelCase__ = [obj for obj in objects if key(_a )[0].isupper() and not key(_a ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCAmelCase__ = [obj for obj in objects if not key(_a )[0].isupper()]
UpperCAmelCase__ = ignore_underscore(_a )
return sorted(_a , key=_a ) + sorted(_a , key=_a ) + sorted(_a , key=_a )
def a_ ( lowerCamelCase ):
# This inner function sort imports between [ ].
def _replace(lowerCamelCase ):
UpperCAmelCase__ = match.groups()[0]
if "," not in imports:
return f'''[{imports}]'''
UpperCAmelCase__ = [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:
UpperCAmelCase__ = keys[:-1]
return "[" + ", ".join([f'''\"{k}\"''' for k in sort_objects(_a )] ) + "]"
UpperCAmelCase__ = import_statement.split('\n' )
if len(_a ) > 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.
UpperCAmelCase__ = 2 if lines[1].strip() == '''[''' else 1
UpperCAmelCase__ = [(i, _re_strip_line.search(_a ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCAmelCase__ = sort_objects(_a , key=lambda lowerCamelCase : x[1] )
UpperCAmelCase__ = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(_a ) == 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:
UpperCAmelCase__ = _re_bracket_content.sub(_replace , lines[1] )
else:
UpperCAmelCase__ = [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:
UpperCAmelCase__ = keys[:-1]
UpperCAmelCase__ = get_indent(lines[1] ) + ''', '''.join([f'''\"{k}\"''' for k in sort_objects(_a )] )
return "\n".join(_a )
else:
# Finally we have to deal with imports fitting on one line
UpperCAmelCase__ = _re_bracket_content.sub(_replace , _a )
return import_statement
def a_ ( lowerCamelCase , lowerCamelCase=True ):
with open(_a , 'r' ) as f:
UpperCAmelCase__ = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCAmelCase__ = split_code_in_indented_blocks(
_a , 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(_a ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCAmelCase__ = main_blocks[block_idx]
UpperCAmelCase__ = block.split('\n' )
# Get to the start of the imports.
UpperCAmelCase__ = 0
while line_idx < len(_a ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCAmelCase__ = len(_a )
else:
line_idx += 1
if line_idx >= len(_a ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCAmelCase__ = '''\n'''.join(block_lines[line_idx:-1] )
UpperCAmelCase__ = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCAmelCase__ = split_code_in_indented_blocks(_a , indent_level=_a )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCAmelCase__ = _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.
UpperCAmelCase__ = [(pattern.search(_a ).groups()[0] if pattern.search(_a ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCAmelCase__ = [(i, key) for i, key in enumerate(_a ) if key is not None]
UpperCAmelCase__ = [x[0] for x in sorted(_a , key=lambda lowerCamelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCAmelCase__ = 0
UpperCAmelCase__ = []
for i in range(len(_a ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCAmelCase__ = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(_a )
count += 1
# And we put our main block back together with its first and last line.
UpperCAmelCase__ = '''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(_a ):
if check_only:
return True
else:
print(f'''Overwriting {file}.''' )
with open(_a , 'w' ) as f:
f.write('\n'.join(_a ) )
def a_ ( lowerCamelCase=True ):
UpperCAmelCase__ = []
for root, _, files in os.walk(_a ):
if "__init__.py" in files:
UpperCAmelCase__ = sort_imports(os.path.join(_a , '__init__.py' ) , check_only=_a )
if result:
UpperCAmelCase__ = [os.path.join(_a , '__init__.py' )]
if len(_a ) > 0:
raise ValueError(f'''Would overwrite {len(_a )} files, run `make style`.''' )
if __name__ == "__main__":
lowerCAmelCase__ : Any = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
lowerCAmelCase__ : Dict = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 98 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = logging.get_logger(__name__)
def __lowercase ( _a , _a=False ):
snake_case_ : List[str] = []
# fmt: off
# stem:
rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''') )
rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''') )
rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''') )
# backbone
rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight") )
rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case_ : Optional[int] = [(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'''),
] )
# fmt: on
return rename_keys
def __lowercase ( _a , _a , _a=False ):
for i in range(config.num_hidden_layers ):
if base_model:
snake_case_ : List[str] = ''''''
else:
snake_case_ : Dict = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ : List[str] = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
snake_case_ : Optional[int] = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Any = in_proj_weight[
: config.hidden_size, :
]
snake_case_ : Dict = in_proj_bias[: config.hidden_size]
snake_case_ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case_ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case_ : Dict = in_proj_weight[
-config.hidden_size :, :
]
snake_case_ : str = in_proj_bias[-config.hidden_size :]
def __lowercase ( _a ):
snake_case_ : Dict = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(_a , _a )
def __lowercase ( _a , _a , _a ):
snake_case_ : Union[str, Any] = dct.pop(_a )
snake_case_ : Union[str, Any] = val
def __lowercase ( ):
snake_case_ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ : Tuple = Image.open(requests.get(_a , stream=_a ).raw )
return im
@torch.no_grad()
def __lowercase ( _a , _a , _a=False ):
snake_case_ : str = BitConfig(
global_padding='''same''' , layer_type='''bottleneck''' , depths=(3, 4, 9) , out_features=['''stage3'''] , embedding_dynamic_padding=_a , )
snake_case_ : Tuple = ViTHybridConfig(backbone_config=_a , image_size=384 , num_labels=1_000 )
snake_case_ : int = False
# load original model from timm
snake_case_ : str = timm.create_model(_a , pretrained=_a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case_ : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_a )
snake_case_ : int = create_rename_keys(_a , _a )
for src, dest in rename_keys:
rename_key(_a , _a , _a )
read_in_q_k_v(_a , _a , _a )
snake_case_ : Optional[Any] = '''huggingface/label-files'''
snake_case_ : Any = '''imagenet-1k-id2label.json'''
snake_case_ : Dict = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
snake_case_ : Dict = {int(_a ): v for k, v in idalabel.items()}
snake_case_ : Optional[int] = idalabel
snake_case_ : Optional[Any] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
snake_case_ : Optional[Any] = ViTHybridModel(_a ).eval()
else:
snake_case_ : Any = ViTHybridForImageClassification(_a ).eval()
model.load_state_dict(_a )
# create image processor
snake_case_ : Optional[Any] = create_transform(**resolve_data_config({} , model=_a ) )
snake_case_ : List[Any] = transform.transforms
snake_case_ : Optional[Any] = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
snake_case_ : List[Any] = ViTHybridImageProcessor(
do_resize=_a , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_a , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=_a , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Optional[int] = transform(_a ).unsqueeze(0 )
snake_case_ : int = processor(_a , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(_a , _a )
# verify logits
with torch.no_grad():
snake_case_ : List[str] = model(_a )
snake_case_ : Any = outputs.logits
print('''Predicted class:''' , logits.argmax(-1 ).item() )
if base_model:
snake_case_ : Optional[Any] = timm_model.forward_features(_a )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_a , outputs.pooler_output , atol=1E-3 )
else:
snake_case_ : int = timm_model(_a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_a , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(f"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_a )
if push_to_hub:
print(f"Pushing model and processor to the hub {vit_name}" )
model.push_to_hub(f"ybelkada/{vit_name}" )
processor.push_to_hub(f"ybelkada/{vit_name}" )
if __name__ == "__main__":
lowercase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--vit_name''',
default='''vit_base_r50_s16_384''',
type=str,
help='''Name of the hybrid ViT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
lowercase__ : Any = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 264 | 0 |
def __lowercase ( lowerCamelCase : List[Any] , lowerCamelCase : List[Any] , lowerCamelCase : Dict ):
return round(float(moles / volume ) * nfactor )
def __lowercase ( lowerCamelCase : int , lowerCamelCase : Optional[int] , lowerCamelCase : Dict ):
return round(float((moles * 0.0_8_2_1 * temperature) / (volume) ) )
def __lowercase ( lowerCamelCase : int , lowerCamelCase : Any , lowerCamelCase : int ):
return round(float((moles * 0.0_8_2_1 * temperature) / (pressure) ) )
def __lowercase ( lowerCamelCase : Union[str, Any] , lowerCamelCase : Tuple , lowerCamelCase : List[Any] ):
return round(float((pressure * volume) / (0.0_8_2_1 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 175 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Dict = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
lowercase__ : str = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def __lowercase ( _a , _a ):
snake_case_ : Optional[int] = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
snake_case_ : List[Any] = int(re.match(r'''.*layer_(\d*).*''' , _a )[1] )
layer_number -= 3
return f"h.{layer_number}." + key
def __lowercase ( _a ):
if dtype == torch.bool:
return 1 / 8
snake_case_ : Dict = re.search(r'''[^\d](\d+)$''' , str(_a ) )
if bit_search is None:
raise ValueError(f"`dtype` is not a valid dtype: {dtype}." )
snake_case_ : Optional[int] = int(bit_search.groups()[0] )
return bit_size // 8
def __lowercase ( _a , _a , _a , _a , _a ):
# Construct model
if bloom_config_file == "":
snake_case_ : int = BloomConfig()
else:
snake_case_ : List[str] = BloomConfig.from_json_file(_a )
if shard_model:
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : int = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[str] = {'''weight_map''': {}, '''metadata''': {}}
snake_case_ : Any = 0
snake_case_ : Union[str, Any] = None
snake_case_ : List[str] = BloomConfig()
for j, file in enumerate(_a ):
print('''Processing file: {}'''.format(_a ) )
snake_case_ : Dict = None
for i in range(_a ):
# load all TP files
snake_case_ : Union[str, Any] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : List[str] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : Any = temp.pop(_a )
if tensors is None:
snake_case_ : Any = temp
else:
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : List[str] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Any = tensors[key] / pretraining_tp
torch.save(
_a , os.path.join(
_a , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
snake_case_ : List[str] = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
snake_case_ : List[str] = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(_a ) ).zfill(5 ) )
snake_case_ : int = BloomConfig()
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Dict = total_size
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(_a , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f:
snake_case_ : Tuple = json.dumps(_a , indent=2 , sort_keys=_a ) + '''\n'''
f.write(_a )
else:
snake_case_ : Union[str, Any] = BloomModel(_a )
snake_case_ : List[str] = os.listdir(_a )
snake_case_ : Dict = sorted(filter(lambda _a : s.startswith('''layer''' ) and "model_00" in s , _a ) )
snake_case_ : List[Any] = None
for i, file in enumerate(_a ):
snake_case_ : Optional[Any] = None
for i in range(_a ):
# load all TP files
snake_case_ : List[str] = file.replace('''model_00''' , f"model_0{i}" )
snake_case_ : Optional[Any] = torch.load(os.path.join(_a , _a ) , map_location='''cpu''' )
# Rename keys in the transformers names
snake_case_ : str = list(temp.keys() )
for key in keys:
snake_case_ : str = temp.pop(_a )
if tensors is None:
snake_case_ : int = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ : Tuple = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ : Optional[Any] = torch.cat([tensors[key], temp[key]] , dim=_a )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_a ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ : Union[str, Any] = tensors[key] / pretraining_tp
snake_case_ : Any = model.load_state_dict(_a , strict=_a )
assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
if missing_keys is None:
snake_case_ : Optional[int] = set(other_keys.missing_keys )
else:
snake_case_ : Tuple = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f"The keys {missing_keys} are missing"
# Save pytorch-model
os.makedirs(_a , exist_ok=_a )
snake_case_ : List[str] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}" )
if config.torch_dtype is not None:
snake_case_ : Optional[Any] = model.to(config.torch_dtype )
torch.save(model.state_dict() , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
lowercase__ : List[Any] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 264 | 0 |
'''simple docstring'''
import random
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = False )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : dict = {i: [] for i in range(_a )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(_a )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(_a ):
for j in range(i + 1 , _a ):
if random.random() < probability:
graph[i].append(_a )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(_a )
return graph
def snake_case_ ( lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
return {
i: [j for j in range(_a ) if i != j] for i in range(_a )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 215 |
"""simple docstring"""
def __lowercase ( _a , _a , _a=False ):
if isinstance(_a , _a ) and isinstance(_a , _a ):
snake_case_ : Union[str, Any] = len(set_a.intersection(_a ) )
if alternative_union:
snake_case_ : Any = len(_a ) + len(_a )
else:
snake_case_ : str = len(set_a.union(_a ) )
return intersection / union
if isinstance(_a , (list, tuple) ) and isinstance(_a , (list, tuple) ):
snake_case_ : str = [element for element in set_a if element in set_b]
if alternative_union:
snake_case_ : Tuple = len(_a ) + len(_a )
return len(_a ) / union
else:
snake_case_ : List[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_a ) / len(_a )
return len(_a ) / len(_a )
return None
if __name__ == "__main__":
lowercase__ : Any = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowercase__ : Optional[Any] = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 264 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ):
"""simple docstring"""
_lowerCAmelCase = coefficient_matrix.shape
_lowerCAmelCase = constant_matrix.shape
if rowsa != colsa:
_lowerCAmelCase = f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(_a )
if colsa != 1:
_lowerCAmelCase = f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(_a )
if rowsa != rowsa:
_lowerCAmelCase = (
'''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
f"received {rowsa}x{colsa} and {rowsa}x{colsa}"
)
raise ValueError(_a )
if len(_a ) != rowsa:
_lowerCAmelCase = (
'''Number of initial values must be equal to number of rows in coefficient '''
f"matrix but received {len(_a )} and {rowsa}"
)
raise ValueError(_a )
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""" )
_lowerCAmelCase = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
_lowerCAmelCase = table.shape
strictly_diagonally_dominant(_a )
# Iterates the whole matrix for given number of times
for _ in range(_a ):
_lowerCAmelCase = []
for row in range(_a ):
_lowerCAmelCase = 0
for col in range(_a ):
if col == row:
_lowerCAmelCase = table[row][col]
elif col == cols - 1:
_lowerCAmelCase = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
_lowerCAmelCase = (temp + val) / denom
new_val.append(_a )
_lowerCAmelCase = new_val
return [float(_a ) for i in new_val]
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = table.shape
_lowerCAmelCase = True
for i in range(0 , _a ):
_lowerCAmelCase = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 70 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowercase__ : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __lowercase ( ):
snake_case_ : Optional[Any] = Github(os.environ['''GITHUB_TOKEN'''] )
snake_case_ : Any = g.get_repo('''huggingface/diffusers''' )
snake_case_ : Any = repo.get_issues(state='''open''' )
for issue in open_issues:
snake_case_ : str = sorted(issue.get_comments() , key=lambda _a : i.created_at , reverse=_a )
snake_case_ : Dict = comments[0] if len(_a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 264 | 0 |
"""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
_a = logging.get_logger(__name__)
_a = {
'''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
# See all ViT models at https://huggingface.co/models?filter=vit
}
class A_ (lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = """vit"""
def __init__( self , lowercase_=768 , lowercase_=12 , lowercase_=12 , lowercase_=3072 , lowercase_="gelu" , lowercase_=0.0 , lowercase_=0.0 , lowercase_=0.02 , lowercase_=1E-1_2 , lowercase_=224 , lowercase_=16 , lowercase_=3 , lowercase_=True , lowercase_=16 , **lowercase_ , ):
"""simple docstring"""
super().__init__(**lowercase_ )
UpperCAmelCase_ : Tuple = hidden_size
UpperCAmelCase_ : List[str] = num_hidden_layers
UpperCAmelCase_ : Optional[int] = num_attention_heads
UpperCAmelCase_ : List[str] = intermediate_size
UpperCAmelCase_ : Union[str, Any] = hidden_act
UpperCAmelCase_ : int = hidden_dropout_prob
UpperCAmelCase_ : str = attention_probs_dropout_prob
UpperCAmelCase_ : Optional[int] = initializer_range
UpperCAmelCase_ : Tuple = layer_norm_eps
UpperCAmelCase_ : Tuple = image_size
UpperCAmelCase_ : List[Any] = patch_size
UpperCAmelCase_ : str = num_channels
UpperCAmelCase_ : Optional[Any] = qkv_bias
UpperCAmelCase_ : str = encoder_stride
class A_ (lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = version.parse("""1.11""" )
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return 1E-4
| 61 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowercase ( _a , _a ):
# Load checkpoint
snake_case_ : Optional[Any] = torch.load(_a , map_location='''cpu''' )
snake_case_ : Union[str, Any] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
snake_case_ : Dict = {}
for k, v in state_dict.items():
if "pred_layer" in k:
snake_case_ : Union[str, Any] = v
else:
snake_case_ : Dict = v
snake_case_ : Union[str, Any] = chkpt['''params''']
snake_case_ : int = {n: v for n, v in config.items() if not isinstance(_a , (torch.FloatTensor, numpy.ndarray) )}
snake_case_ : int = chkpt['''dico_word2id''']
snake_case_ : str = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(f"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(_a , _a )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
print(f"Save vocab file to {pytorch_config_dump_path}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , indent=2 ) + '''\n''' )
if __name__ == "__main__":
lowercase__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowercase__ : List[str] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 264 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
a_ : Optional[Any] = torch.load(_a , map_location="""cpu""")
a_ : Union[str, Any] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
a_ : Dict = {}
for k, v in state_dict.items():
if "pred_layer" in k:
a_ : Union[str, Any] = v
else:
a_ : Dict = v
a_ : Union[str, Any] = chkpt['''params''']
a_ : int = {n: v for n, v in config.items() if not isinstance(_a , (torch.FloatTensor, numpy.ndarray))}
a_ : int = chkpt['''dico_word2id''']
a_ : str = {s + '''</w>''' if s.find("""@@""") == -1 and i > 1_3 else s.replace("""@@""" , """"""): i for s, i in vocab.items()}
# Save pytorch-model
a_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
a_ : Union[str, Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
a_ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(f'''Save PyTorch model to {pytorch_weights_dump_path}''')
torch.save(_a , _a)
print(f'''Save configuration file to {pytorch_config_dump_path}''')
with open(_a , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(_a , indent=2) + """\n""")
print(f'''Save vocab file to {pytorch_config_dump_path}''')
with open(_a , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(_a , indent=2) + """\n""")
if __name__ == "__main__":
__snake_case : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__snake_case : List[str] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 248 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 264 | 0 |
import sys
lowercase__ : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> Optional[Any]:
lowerCAmelCase = 1
for digit in s:
product *= int(_a )
return product
def SCREAMING_SNAKE_CASE_ ( snake_case__ = N ) -> Optional[Any]:
lowerCAmelCase = -sys.maxsize - 1
lowerCAmelCase = n[:1_3]
lowerCAmelCase = 1_3
while cur_index < len(_a ) - 1_3:
if int(n[cur_index] ) >= int(substr[0] ):
lowerCAmelCase = substr[1:] + n[cur_index]
cur_index += 1
else:
lowerCAmelCase = max(_a , str_eval(_a ) )
lowerCAmelCase = n[cur_index : cur_index + 1_3]
cur_index += 1_3
return largest_product
if __name__ == "__main__":
print(f'{solution() = }')
| 338 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def __lowercase ( _a="" ):
snake_case_ : List[str] = tempfile.mkdtemp()
return os.path.join(_a , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : str ):
snake_case_ : int = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : Optional[int] = AgentAudio(lowercase_ )
snake_case_ : List[str] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowercase_ ) )
# Ensure that the file contains the same value as the original tensor
snake_case_, snake_case_ : int = sf.read(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , torch.tensor(lowercase_ ) , atol=1E-4 ) )
def _snake_case ( self : Optional[int] ):
snake_case_ : Any = torch.rand(12 , dtype=torch.floataa ) - 0.5
snake_case_ : List[str] = get_new_path(suffix='''.wav''' )
sf.write(lowercase_ , lowercase_ , 16000 )
snake_case_ : Tuple = AgentAudio(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , agent_type.to_raw() , atol=1E-4 ) )
self.assertEqual(agent_type.to_string() , lowercase_ )
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Tuple ):
snake_case_ : List[Any] = torch.randint(0 , 256 , (64, 64, 3) )
snake_case_ : str = AgentImage(lowercase_ )
snake_case_ : Union[str, Any] = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowercase_ , agent_type._tensor , atol=1E-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : Any = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Optional[int] = Image.open(lowercase_ )
snake_case_ : Tuple = AgentImage(lowercase_ )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
def _snake_case ( self : str ):
snake_case_ : int = Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
snake_case_ : Dict = Image.open(lowercase_ )
snake_case_ : List[str] = AgentImage(lowercase_ )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowercase_ ) )
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Any ):
snake_case_ : Tuple = '''Hey!'''
snake_case_ : Optional[Any] = AgentText(lowercase_ )
self.assertEqual(lowercase_ , agent_type.to_string() )
self.assertEqual(lowercase_ , agent_type.to_raw() )
self.assertEqual(lowercase_ , lowercase_ )
| 264 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[int] = False ):
"""simple docstring"""
if not isinstance(_a , _a ):
__magic_name__ : int = f'Expected string as input, found {type(_a )}'
raise ValueError(_a )
if not isinstance(_a , _a ):
__magic_name__ : Any = f'Expected boolean as use_pascal parameter, found {type(_a )}'
raise ValueError(_a )
__magic_name__ : List[Any] = input_str.split('_' )
__magic_name__ : List[str] = 0 if use_pascal else 1
__magic_name__ : str = words[start_index:]
__magic_name__ : str = [word[0].upper() + word[1:] for word in words_to_capitalize]
__magic_name__ : Optional[Any] = '''''' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod() | 331 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : str = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
lowercase__ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[Any] ):
'''simple docstring'''
lowercase = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
lowercase = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase = True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowercase = [3, 3, 3, 3]
lowercase = [5, 5, 5, 5]
elif "fl4" in model_name:
lowercase = [4, 4, 4, 4]
lowercase = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowercase = [3, 3, 3, 3]
if "lrf" in model_name:
lowercase = [3, 3, 3, 3]
else:
lowercase = [2, 2, 2, 2]
if "tiny" in model_name:
lowercase = 96
elif "small" in model_name:
lowercase = 96
elif "base" in model_name:
lowercase = 1_28
elif "large" in model_name:
lowercase = 1_92
elif "xlarge" in model_name:
lowercase = 2_56
elif "huge" in model_name:
lowercase = 3_52
# set label information
lowercase = '''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
lowercase = '''imagenet-22k-id2label.json'''
else:
lowercase = '''imagenet-1k-id2label.json'''
lowercase = json.load(open(hf_hub_download(_a , _a , repo_type='dataset' ) , 'r' ) )
lowercase = {int(_a ): v for k, v in idalabel.items()}
lowercase = {v: k for k, v in idalabel.items()}
lowercase = FocalNetConfig(
embed_dim=_a , depths=_a , focal_levels=_a , focal_windows=_a , use_conv_embed=_a , idalabel=_a , labelaid=_a , use_post_layernorm=_a , use_layerscale=_a , )
return config
def _SCREAMING_SNAKE_CASE ( __snake_case : Tuple ):
'''simple docstring'''
if "patch_embed.proj" in name:
lowercase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
lowercase = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
lowercase = '''encoder.''' + name
if "encoder.layers" in name:
lowercase = name.replace('encoder.layers' , 'encoder.stages' )
if "downsample.proj" in name:
lowercase = name.replace('downsample.proj' , 'downsample.projection' )
if "blocks" in name:
lowercase = name.replace('blocks' , 'layers' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowercase = name.replace('modulation.f' , 'modulation.projection_in' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowercase = name.replace('modulation.h' , 'modulation.projection_context' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowercase = name.replace('modulation.proj' , 'modulation.projection_out' )
if name == "norm.weight":
lowercase = '''layernorm.weight'''
if name == "norm.bias":
lowercase = '''layernorm.bias'''
if "head" in name:
lowercase = name.replace('head' , 'classifier' )
else:
lowercase = '''focalnet.''' + name
return name
def _SCREAMING_SNAKE_CASE ( __snake_case : Tuple , __snake_case : List[Any] , __snake_case : List[Any]=False ):
'''simple docstring'''
lowercase = {
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
lowercase = model_name_to_url[model_name]
print('Checkpoint URL: ' , _a )
lowercase = torch.hub.load_state_dict_from_url(_a , map_location='cpu' )['''model''']
# rename keys
for key in state_dict.copy().keys():
lowercase = state_dict.pop(_a )
lowercase = val
lowercase = get_focalnet_config(_a )
lowercase = FocalNetForImageClassification(_a )
model.eval()
# load state dict
model.load_state_dict(_a )
# verify conversion
lowercase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase = BitImageProcessor(
do_resize=_a , size={'shortest_edge': 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=_a , crop_size=2_24 , do_normalize=_a , image_mean=_a , image_std=_a , )
lowercase = Image.open(requests.get(_a , stream=_a ).raw )
lowercase = processor(images=_a , return_tensors='pt' )
lowercase = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
lowercase = image_transforms(_a ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , _a , atol=1e-4 )
lowercase = model(**_a )
lowercase = outputs.logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
print('First values of logits:' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowercase = torch.tensor([0.2166, -0.4368, 0.2191] )
elif model_name == "focalnet-tiny-lrf":
lowercase = torch.tensor([1.1669, 0.0125, -0.1695] )
elif model_name == "focalnet-small":
lowercase = torch.tensor([0.4917, -0.0430, 0.1341] )
elif model_name == "focalnet-small-lrf":
lowercase = torch.tensor([-0.2588, -0.5342, -0.2331] )
elif model_name == "focalnet-base":
lowercase = torch.tensor([-0.1655, -0.4090, -0.1730] )
elif model_name == "focalnet-base-lrf":
lowercase = torch.tensor([0.5306, -0.0483, -0.3928] )
assert torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f'Saving model and processor of {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_a )
processor.save_pretrained(_a )
if push_to_hub:
print(f'Pushing model and processor of {model_name} to the hub...' )
model.push_to_hub(f'{model_name}' )
processor.push_to_hub(f'{model_name}' )
if __name__ == "__main__":
_UpperCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='focalnet-tiny',
type=str,
help='Name of the FocalNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub.',
)
_UpperCamelCase : Optional[int] = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 220 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : str = XLMRobertaTokenizer
_lowerCAmelCase : int = XLMRobertaTokenizerFast
_lowerCAmelCase : str = True
_lowerCAmelCase : Dict = True
def _snake_case ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ : List[str] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def _snake_case ( self : str ):
snake_case_ : List[Any] = '''<pad>'''
snake_case_ : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def _snake_case ( self : Union[str, Any] ):
snake_case_ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowercase_ ) , 1002 )
def _snake_case ( self : Union[str, Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def _snake_case ( self : Dict ):
snake_case_ : Optional[Any] = XLMRobertaTokenizer(lowercase_ , keep_accents=lowercase_ )
snake_case_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowercase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
snake_case_ : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
snake_case_ : List[Any] = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _snake_case ( self : List[str] ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
snake_case_ : int = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : int = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : Tuple = tokenizer_r.save_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
snake_case_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : Union[str, Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[Any] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[str] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : List[str] = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
snake_case_ : List[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : List[str] = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
snake_case_ : Optional[Any] = tempfile.mkdtemp()
snake_case_ : List[Any] = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
snake_case_ : Tuple = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
snake_case_ : Optional[Any] = tokenizer_r.from_pretrained(lowercase_ )
snake_case_ : Dict = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@cached_property
def _snake_case ( self : List[str] ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _snake_case ( self : Optional[Any] ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowercase_ , f.name )
snake_case_ : Any = XLMRobertaTokenizer(f.name , keep_accents=lowercase_ )
snake_case_ : List[Any] = pickle.dumps(lowercase_ )
pickle.loads(lowercase_ )
def _snake_case ( self : Tuple ):
if not self.test_rust_tokenizer:
return
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ : Optional[int] = self.get_rust_tokenizer()
snake_case_ : Dict = '''I was born in 92000, and this is falsé.'''
snake_case_ : Optional[int] = tokenizer.tokenize(lowercase_ )
snake_case_ : Tuple = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : List[str] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
snake_case_ : str = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ : Any = tokenizer.encode(lowercase_ )
snake_case_ : int = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
@slow
def _snake_case ( self : Tuple ):
snake_case_ : int = '''Hello World!'''
snake_case_ : int = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : List[Any] ):
snake_case_ : Any = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
snake_case_ : Optional[int] = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowercase_ , self.big_tokenizer.encode(lowercase_ ) )
@slow
def _snake_case ( self : Dict ):
# fmt: off
snake_case_ : int = {'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 264 | 0 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
# load base model
lowerCAmelCase_ : Dict = StableDiffusionPipeline.from_pretrained(_a , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
lowerCAmelCase_ : List[Any] = load_file(_a )
lowerCAmelCase_ : Optional[int] = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
lowerCAmelCase_ : int = key.split('''.''' )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_''' )[-1].split('''_''' )
lowerCAmelCase_ : Optional[int] = pipeline.text_encoder
else:
lowerCAmelCase_ : Tuple = key.split('''.''' )[0].split(LORA_PREFIX_UNET + '''_''' )[-1].split('''_''' )
lowerCAmelCase_ : Optional[Any] = pipeline.unet
# find the target layer
lowerCAmelCase_ : Dict = layer_infos.pop(0 )
while len(_a ) > -1:
try:
lowerCAmelCase_ : Optional[int] = curr_layer.__getattr__(_a )
if len(_a ) > 0:
lowerCAmelCase_ : str = layer_infos.pop(0 )
elif len(_a ) == 0:
break
except Exception:
if len(_a ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
lowerCAmelCase_ : str = layer_infos.pop(0 )
lowerCAmelCase_ : Optional[int] = []
if "lora_down" in key:
pair_keys.append(key.replace('''lora_down''' , '''lora_up''' ) )
pair_keys.append(_a )
else:
pair_keys.append(_a )
pair_keys.append(key.replace('''lora_up''' , '''lora_down''' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
lowerCAmelCase_ : Dict = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
lowerCAmelCase_ : List[Any] = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_a , _a ).unsqueeze(2 ).unsqueeze(3 )
else:
lowerCAmelCase_ : Optional[int] = state_dict[pair_keys[0]].to(torch.floataa )
lowerCAmelCase_ : Tuple = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_a , _a )
# update visited list
for item in pair_keys:
visited.append(_a )
return pipeline
if __name__ == "__main__":
_UpperCAmelCase : Optional[int] =argparse.ArgumentParser()
parser.add_argument(
"""--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
)
parser.add_argument(
"""--lora_prefix_text_encoder""",
default="""lora_te""",
type=str,
help="""The prefix of text encoder weight in safetensors""",
)
parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
parser.add_argument(
"""--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
)
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
_UpperCAmelCase : str =parser.parse_args()
_UpperCAmelCase : int =args.base_model_path
_UpperCAmelCase : Union[str, Any] =args.checkpoint_path
_UpperCAmelCase : List[Any] =args.dump_path
_UpperCAmelCase : Optional[int] =args.lora_prefix_unet
_UpperCAmelCase : Dict =args.lora_prefix_text_encoder
_UpperCAmelCase : List[str] =args.alpha
_UpperCAmelCase : Dict =convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
_UpperCAmelCase : Optional[int] =pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) | 262 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : List[Any] = """gpt_neox"""
def __init__( self : List[str] , lowercase_ : str=50432 , lowercase_ : List[Any]=6144 , lowercase_ : List[Any]=44 , lowercase_ : Union[str, Any]=64 , lowercase_ : List[str]=24576 , lowercase_ : List[Any]="gelu" , lowercase_ : str=0.25 , lowercase_ : Optional[int]=10000 , lowercase_ : Optional[int]=0.0 , lowercase_ : Optional[int]=0.0 , lowercase_ : int=0.1 , lowercase_ : Tuple=2048 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : List[str]=1E-5 , lowercase_ : str=True , lowercase_ : str=0 , lowercase_ : Union[str, Any]=2 , lowercase_ : List[str]=False , lowercase_ : Optional[int]=True , lowercase_ : List[Any]=None , **lowercase_ : Optional[int] , ):
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : Dict = num_attention_heads
snake_case_ : List[Any] = intermediate_size
snake_case_ : List[Any] = hidden_act
snake_case_ : str = rotary_pct
snake_case_ : Dict = rotary_emb_base
snake_case_ : Optional[int] = attention_dropout
snake_case_ : Tuple = hidden_dropout
snake_case_ : Tuple = classifier_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Any = use_cache
snake_case_ : Optional[int] = tie_word_embeddings
snake_case_ : Any = use_parallel_residual
snake_case_ : Union[str, Any] = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def _snake_case ( self : Optional[int] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , lowercase_ ) 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}" )
snake_case_ : Any = self.rope_scaling.get('''type''' , lowercase_ )
snake_case_ : Union[str, Any] = self.rope_scaling.get('''factor''' , lowercase_ )
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(lowercase_ , lowercase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 264 | 0 |
'''simple docstring'''
from collections import defaultdict
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : int = first_str.lower().strip()
UpperCAmelCase : Tuple = second_str.lower().strip()
# Remove whitespace
UpperCAmelCase : List[Any] = first_str.replace(""" """ , """""" )
UpperCAmelCase : Optional[Any] = second_str.replace(""" """ , """""" )
# Strings of different lengths are not anagrams
if len(_lowercase ) != len(_lowercase ):
return False
# Default values for count should be 0
UpperCAmelCase : defaultdict[str, int] = defaultdict(_lowercase )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(_lowercase ) ):
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()
a : str = input("""Enter the first string """).strip()
a : Union[str, Any] = input("""Enter the second string """).strip()
a : Any = check_anagrams(input_a, input_b)
print(F'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')
| 265 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_lowercase , int(b / 2 ) ) * actual_power(_lowercase , int(b / 2 ) )
else:
return a * actual_power(_lowercase , int(b / 2 ) ) * actual_power(_lowercase , int(b / 2 ) )
def __lowerCamelCase ( _lowercase , _lowercase ) -> float:
if b < 0:
return 1 / actual_power(_lowercase , _lowercase )
return actual_power(_lowercase , _lowercase )
if __name__ == "__main__":
print(power(-2, -3))
| 265 | 1 |
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