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'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
A =True
except ImportError:
A =False
A =logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case_ (_a : Namespace ):
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class _a ( __a ):
@staticmethod
def A ( lowercase : ArgumentParser ):
'''simple docstring'''
UpperCAmelCase = parser.add_parser('''add-new-model''' )
add_new_model_parser.add_argument('''--testing''' , action='''store_true''' , help='''If in testing mode.''' )
add_new_model_parser.add_argument('''--testing_file''' , type=lowercase , help='''Configuration file on which to run.''' )
add_new_model_parser.add_argument(
'''--path''' , type=lowercase , help='''Path to cookiecutter. Should only be used for testing purposes.''' )
add_new_model_parser.set_defaults(func=lowercase )
def __init__( self : List[Any] , lowercase : bool , lowercase : str , lowercase : Dict=None , *lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = testing
UpperCAmelCase = testing_file
UpperCAmelCase = path
def A ( self : Optional[Any] ):
'''simple docstring'''
warnings.warn(
'''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
'''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
'''checks, you should use `transformers-cli add-new-model-like` instead.''' )
if not _has_cookiecutter:
raise ImportError(
'''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
'''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n''' )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
UpperCAmelCase = [directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
if len(lowercase ) > 0:
raise ValueError(
'''Several directories starting with `cookiecutter-template-` in current working directory. '''
'''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
'''change your working directory.''' )
UpperCAmelCase = (
Path(lowercase ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
UpperCAmelCase = path_to_transformer_root / '''templates''' / '''adding_a_new_model'''
# Execute cookiecutter
if not self._testing:
cookiecutter(str(lowercase ) )
else:
with open(self._testing_file , '''r''' ) as configuration_file:
UpperCAmelCase = json.load(lowercase )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=lowercase , extra_context=lowercase , )
UpperCAmelCase = [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]
# Retrieve configuration
with open(directory + '''/configuration.json''' , '''r''' ) as configuration_file:
UpperCAmelCase = json.load(lowercase )
UpperCAmelCase = configuration['''lowercase_modelname''']
UpperCAmelCase = configuration['''generate_tensorflow_pytorch_and_flax''']
os.remove(f"{directory}/configuration.json" )
UpperCAmelCase = '''PyTorch''' in generate_tensorflow_pytorch_and_flax
UpperCAmelCase = '''TensorFlow''' in generate_tensorflow_pytorch_and_flax
UpperCAmelCase = '''Flax''' in generate_tensorflow_pytorch_and_flax
UpperCAmelCase = f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
os.makedirs(lowercase , exist_ok=lowercase )
os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}" , exist_ok=lowercase )
# Tests require submodules as they have parent imports
with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py" , '''w''' ):
pass
shutil.move(
f"{directory}/__init__.py" , f"{model_dir}/__init__.py" , )
shutil.move(
f"{directory}/configuration_{lowercase_model_name}.py" , f"{model_dir}/configuration_{lowercase_model_name}.py" , )
def remove_copy_lines(lowercase : Union[str, Any] ):
with open(lowercase , '''r''' ) as f:
UpperCAmelCase = f.readlines()
with open(lowercase , '''w''' ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(lowercase )
if output_pytorch:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_{lowercase_model_name}.py" , f"{model_dir}/modeling_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py" )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_tf_{lowercase_model_name}.py" , f"{model_dir}/modeling_tf_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_tf_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py" )
if output_flax:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_flax_{lowercase_model_name}.py" , f"{model_dir}/modeling_flax_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_flax_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/{lowercase_model_name}.md" , f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md" , )
shutil.move(
f"{directory}/tokenization_{lowercase_model_name}.py" , f"{model_dir}/tokenization_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/tokenization_fast_{lowercase_model_name}.py" , f"{model_dir}/tokenization_{lowercase_model_name}_fast.py" , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(lowercase : str , lowercase : str , lowercase : List[str] ):
# Create temp file
UpperCAmelCase , UpperCAmelCase = mkstemp()
UpperCAmelCase = False
with fdopen(lowercase , '''w''' ) as new_file:
with open(lowercase ) as old_file:
for line in old_file:
new_file.write(lowercase )
if line_to_copy_below in line:
UpperCAmelCase = True
for line_to_copy in lines_to_copy:
new_file.write(lowercase )
if not line_found:
raise ValueError(f"Line {line_to_copy_below} was not found in file." )
# Copy the file permissions from the old file to the new file
copymode(lowercase , lowercase )
# Remove original file
remove(lowercase )
# Move new file
move(lowercase , lowercase )
def skip_units(lowercase : List[Any] ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(lowercase : Tuple ):
with open(lowercase ) as datafile:
UpperCAmelCase = []
UpperCAmelCase = False
UpperCAmelCase = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
UpperCAmelCase = line.split('''"''' )[1]
UpperCAmelCase = skip_units(lowercase )
elif "# Below: " in line and "##" not in line:
UpperCAmelCase = line.split('''"''' )[1]
UpperCAmelCase = skip_units(lowercase )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(lowercase , lowercase , lowercase )
UpperCAmelCase = []
elif "# Replace with" in line and "##" not in line:
UpperCAmelCase = []
elif "##" not in line:
lines_to_copy.append(lowercase )
remove(lowercase )
replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py" )
os.rmdir(lowercase )
| 34
|
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : dict , _a : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
UpperCAmelCase = cst_fwd.get(_a , np.inf )
UpperCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
UpperCAmelCase = new_cost_f
UpperCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
UpperCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def snake_case_ (_a : str , _a : str , _a : dict , _a : dict ):
UpperCAmelCase = -1
UpperCAmelCase = set()
UpperCAmelCase = set()
UpperCAmelCase = {source: 0}
UpperCAmelCase = {destination: 0}
UpperCAmelCase = {source: None}
UpperCAmelCase = {destination: None}
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
UpperCAmelCase , UpperCAmelCase = queue_forward.get()
visited_forward.add(_a )
UpperCAmelCase , UpperCAmelCase = queue_backward.get()
visited_backward.add(_a )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
UpperCAmelCase = shortest_distance
return shortest_path_distance
A ={
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
A ={
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def snake_case_ (_a : Any ):
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def snake_case_ (_a : List[Any] ):
UpperCAmelCase = create_tensor(_a )
UpperCAmelCase = gather(_a )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def snake_case_ (_a : str ):
UpperCAmelCase = [state.process_index]
UpperCAmelCase = gather_object(_a )
assert len(_a ) == state.num_processes, F"{gathered_obj}, {len(_a )} != {state.num_processes}"
assert gathered_obj == list(range(state.num_processes ) ), F"{gathered_obj} != {list(range(state.num_processes ) )}"
def snake_case_ (_a : Optional[int] ):
UpperCAmelCase = create_tensor(_a )
UpperCAmelCase = broadcast(_a )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def snake_case_ (_a : Optional[Any] ):
# We need to pad the tensor with one more element if we are the main process
# to ensure that we can pad
if state.is_main_process:
UpperCAmelCase = torch.arange(state.num_processes + 1 ).to(state.device )
else:
UpperCAmelCase = torch.arange(state.num_processes ).to(state.device )
UpperCAmelCase = pad_across_processes(_a )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def snake_case_ (_a : Tuple ):
# For now runs on only two processes
if state.num_processes != 2:
return
UpperCAmelCase = create_tensor(_a )
UpperCAmelCase = reduce(_a , '''sum''' )
UpperCAmelCase = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(_a , _a ), F"{reduced_tensor} != {truth_tensor}"
def snake_case_ (_a : Optional[Any] ):
# For now runs on only two processes
if state.num_processes != 2:
return
UpperCAmelCase = create_tensor(_a )
UpperCAmelCase = reduce(_a , '''mean''' )
UpperCAmelCase = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(_a , _a ), F"{reduced_tensor} != {truth_tensor}"
def snake_case_ (_a : Any ):
# For xla_spawn (TPUs)
main()
def snake_case_ ():
UpperCAmelCase = PartialState()
state.print(F"State: {state}" )
state.print('''testing gather''' )
test_gather(_a )
state.print('''testing gather_object''' )
test_gather_object(_a )
state.print('''testing broadcast''' )
test_broadcast(_a )
state.print('''testing pad_across_processes''' )
test_pad_across_processes(_a )
state.print('''testing reduce_sum''' )
test_reduce_sum(_a )
state.print('''testing reduce_mean''' )
test_reduce_mean(_a )
if __name__ == "__main__":
main()
| 34
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
def snake_case_ (_a : List[str] ):
UpperCAmelCase = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
UpperCAmelCase = 1_2_8
elif "12-12" in model_name:
UpperCAmelCase = 1_2
UpperCAmelCase = 1_2
elif "14-14" in model_name:
UpperCAmelCase = 1_4
UpperCAmelCase = 1_4
elif "16-16" in model_name:
UpperCAmelCase = 1_6
UpperCAmelCase = 1_6
else:
raise ValueError('''Model not supported''' )
UpperCAmelCase = '''huggingface/label-files'''
if "speech-commands" in model_name:
UpperCAmelCase = 3_5
UpperCAmelCase = '''speech-commands-v2-id2label.json'''
else:
UpperCAmelCase = 5_2_7
UpperCAmelCase = '''audioset-id2label.json'''
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ (_a : Tuple ):
if "module.v" in name:
UpperCAmelCase = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
UpperCAmelCase = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
UpperCAmelCase = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
UpperCAmelCase = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
UpperCAmelCase = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
UpperCAmelCase = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
UpperCAmelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
UpperCAmelCase = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
UpperCAmelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
UpperCAmelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
UpperCAmelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
UpperCAmelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
UpperCAmelCase = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
UpperCAmelCase = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
UpperCAmelCase = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def snake_case_ (_a : Dict , _a : List[Any] ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(_a )
if "qkv" in key:
UpperCAmelCase = key.split('''.''' )
UpperCAmelCase = int(key_split[3] )
UpperCAmelCase = config.hidden_size
if "weight" in key:
UpperCAmelCase = val[:dim, :]
UpperCAmelCase = val[dim : dim * 2, :]
UpperCAmelCase = val[-dim:, :]
else:
UpperCAmelCase = val[:dim]
UpperCAmelCase = val[dim : dim * 2]
UpperCAmelCase = val[-dim:]
else:
UpperCAmelCase = val
return orig_state_dict
def snake_case_ (_a : Tuple ):
UpperCAmelCase = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(_a , _a )
@torch.no_grad()
def snake_case_ (_a : int , _a : Union[str, Any] , _a : Dict=False ):
UpperCAmelCase = get_audio_spectrogram_transformer_config(_a )
UpperCAmelCase = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
UpperCAmelCase = model_name_to_url[model_name]
UpperCAmelCase = torch.hub.load_state_dict_from_url(_a , map_location='''cpu''' )
# remove some keys
remove_keys(_a )
# rename some keys
UpperCAmelCase = convert_state_dict(_a , _a )
# load 🤗 model
UpperCAmelCase = ASTForAudioClassification(_a )
model.eval()
model.load_state_dict(_a )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
UpperCAmelCase = -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
UpperCAmelCase = 4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
UpperCAmelCase = 1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
UpperCAmelCase = ASTFeatureExtractor(mean=_a , std=_a , max_length=_a )
if "speech-commands" in model_name:
UpperCAmelCase = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
UpperCAmelCase = dataset[0]['''audio''']['''array''']
else:
UpperCAmelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
UpperCAmelCase , UpperCAmelCase = torchaudio.load(_a )
UpperCAmelCase = waveform.squeeze().numpy()
UpperCAmelCase = feature_extractor(_a , sampling_rate=1_6_0_0_0 , return_tensors='''pt''' )
# forward pass
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
UpperCAmelCase = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
UpperCAmelCase = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
UpperCAmelCase = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
UpperCAmelCase = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
UpperCAmelCase = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
UpperCAmelCase = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
UpperCAmelCase = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
UpperCAmelCase = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , _a , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(F"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_a )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(F"MIT/{model_name}" )
feature_extractor.push_to_hub(F"MIT/{model_name}" )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub.'
)
A =parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _a ( __a , unittest.TestCase ):
__a : Any = KandinskyVaaImgaImgPipeline
__a : Optional[int] = ["""image_embeds""", """negative_image_embeds""", """image"""]
__a : int = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
__a : Any = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
__a : Union[str, Any] = False
@property
def A ( self : List[Any] ):
'''simple docstring'''
return 32
@property
def A ( self : Dict ):
'''simple docstring'''
return 32
@property
def A ( self : str ):
'''simple docstring'''
return self.time_input_dim
@property
def A ( self : Dict ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def A ( self : Optional[int] ):
'''simple docstring'''
return 100
@property
def A ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
UpperCAmelCase = UNetaDConditionModel(**lowercase )
return model
@property
def A ( self : Any ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def A ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.dummy_unet
UpperCAmelCase = self.dummy_movq
UpperCAmelCase = {
'''num_train_timesteps''': 1_000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_0085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
UpperCAmelCase = DDIMScheduler(**lowercase )
UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def A ( self : Optional[int] , lowercase : int , lowercase : List[Any]=0 ):
'''simple docstring'''
UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(lowercase ) ).to(lowercase )
UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
lowercase )
# create init_image
UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase ) ).to(lowercase )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase = Image.fromarray(np.uinta(lowercase ) ).convert('''RGB''' ).resize((256, 256) )
if str(lowercase ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(lowercase )
else:
UpperCAmelCase = torch.Generator(device=lowercase ).manual_seed(lowercase )
UpperCAmelCase = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = '''cpu'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowercase )
UpperCAmelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = pipe(**self.get_dummy_inputs(lowercase ) )
UpperCAmelCase = output.images
UpperCAmelCase = pipe(
**self.get_dummy_inputs(lowercase ) , return_dict=lowercase , )[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase = np.array(
[0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
def A ( self : List[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
UpperCAmelCase = '''A red cartoon frog, 4k'''
UpperCAmelCase = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(lowercase )
UpperCAmelCase = KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
UpperCAmelCase = pipeline.to(lowercase )
pipeline.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase , UpperCAmelCase = pipe_prior(
lowercase , generator=lowercase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
UpperCAmelCase = 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''' , )
UpperCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowercase , lowercase )
| 34
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
UpperCAmelCase = m
else:
UpperCAmelCase = m # noqa: E741
return r
def snake_case_ (_a : list[int] ):
if len(_a ) == 0:
return 0
UpperCAmelCase = [0] * len(_a )
UpperCAmelCase = 1
UpperCAmelCase = v[0]
for i in range(1 , len(_a ) ):
if v[i] < tail[0]:
UpperCAmelCase = v[i]
elif v[i] > tail[length - 1]:
UpperCAmelCase = v[i]
length += 1
else:
UpperCAmelCase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
A ={
'169M': 12,
'430M': 24,
'1B5': 24,
'3B': 32,
'7B': 32,
'14B': 40,
}
A ={
'169M': 7_68,
'430M': 10_24,
'1B5': 20_48,
'3B': 25_60,
'7B': 40_96,
'14B': 51_20,
}
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = list(state_dict.keys() )
for name in state_dict_keys:
UpperCAmelCase = state_dict.pop(_a )
# emb -> embedding
if name.startswith('''emb.''' ):
UpperCAmelCase = name.replace('''emb.''' , '''embeddings.''' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('''blocks.0.ln0''' ):
UpperCAmelCase = name.replace('''blocks.0.ln0''' , '''blocks.0.pre_ln''' )
# att -> attention
UpperCAmelCase = re.sub(R'''blocks\.(\d+)\.att''' , R'''blocks.\1.attention''' , _a )
# ffn -> feed_forward
UpperCAmelCase = re.sub(R'''blocks\.(\d+)\.ffn''' , R'''blocks.\1.feed_forward''' , _a )
# time_mix_k -> time_mix_key and reshape
if name.endswith('''.time_mix_k''' ):
UpperCAmelCase = name.replace('''.time_mix_k''' , '''.time_mix_key''' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('''.time_mix_v''' ):
UpperCAmelCase = name.replace('''.time_mix_v''' , '''.time_mix_value''' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('''.time_mix_r''' ):
UpperCAmelCase = name.replace('''.time_mix_r''' , '''.time_mix_receptance''' )
if name != "head.weight":
UpperCAmelCase = '''rwkv.''' + name
UpperCAmelCase = weight
return state_dict
def snake_case_ (_a : List[str] , _a : int , _a : Optional[Any] , _a : Dict=None , _a : List[str]=None , _a : Union[str, Any]=False , _a : Tuple=None ):
# 1. If possible, build the tokenizer.
if tokenizer_file is None:
print('''No `--tokenizer_file` provided, we will use the default tokenizer.''' )
UpperCAmelCase = 5_0_2_7_7
UpperCAmelCase = AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''' )
else:
UpperCAmelCase = PreTrainedTokenizerFast(tokenizer_file=_a )
UpperCAmelCase = len(_a )
tokenizer.save_pretrained(_a )
# 2. Build the config
UpperCAmelCase = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
UpperCAmelCase = candidate
break
if size is None:
raise ValueError('''Could not infer the size, please provide it with the `--size` argument.''' )
if size not in possible_sizes:
raise ValueError(F"`size` should be one of {possible_sizes}, got {size}." )
UpperCAmelCase = RwkvConfig(
vocab_size=_a , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(_a )
# 3. Download model file then convert state_dict
UpperCAmelCase = hf_hub_download(_a , _a )
UpperCAmelCase = torch.load(_a , map_location='''cpu''' )
UpperCAmelCase = convert_state_dict(_a )
# 4. Split in shards and save
UpperCAmelCase , UpperCAmelCase = shard_checkpoint(_a )
for shard_file, shard in shards.items():
torch.save(_a , os.path.join(_a , _a ) )
if index is not None:
UpperCAmelCase = os.path.join(_a , _a )
# Save the index as well
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
UpperCAmelCase = json.dumps(_a , indent=2 , sort_keys=_a ) + '''\n'''
f.write(_a )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.''' )
UpperCAmelCase = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
UpperCAmelCase = torch.load(os.path.join(_a , _a ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(_a , _a ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('''Please provide a `model_name` to push the model to the Hub.''' )
UpperCAmelCase = AutoModelForCausalLM.from_pretrained(_a )
model.push_to_hub(_a , max_shard_size='''2GB''' )
tokenizer.push_to_hub(_a )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
)
parser.add_argument(
'--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
)
parser.add_argument(
'--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
)
parser.add_argument(
'--tokenizer_file',
default=None,
type=str,
help='Path to the tokenizer file to use (if not provided, only the model is converted).',
)
parser.add_argument(
'--size',
default=None,
type=str,
help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Push to the Hub the converted model.',
)
parser.add_argument(
'--model_name',
default=None,
type=str,
help='Name of the pushed model on the Hub, including the username / organization.',
)
A =parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 34
|
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase = [[0 for i in range(_a )] for j in range(_a )]
# since string of zero length match pattern of zero length
UpperCAmelCase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _a ):
UpperCAmelCase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _a ):
UpperCAmelCase = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _a ):
for j in range(1 , _a ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase = dp[i - 1][j]
else:
UpperCAmelCase = 0
else:
UpperCAmelCase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A ='aab'
A ='c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"""{input_string} matches the given pattern {pattern}""")
else:
print(f"""{input_string} does not match with the given pattern {pattern}""")
| 34
| 1
|
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _a ( __a ):
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase , '''embed_dim''' ) )
self.parent.assertTrue(hasattr(lowercase , '''num_heads''' ) )
class _a :
def __init__( self : Union[str, Any] , lowercase : Dict , lowercase : Dict=13 , lowercase : List[str]=64 , lowercase : Optional[Any]=3 , lowercase : Union[str, Any]=[16, 48, 96] , lowercase : Optional[Any]=[1, 3, 6] , lowercase : List[Any]=[1, 2, 10] , lowercase : Any=[7, 3, 3] , lowercase : int=[4, 2, 2] , lowercase : Tuple=[2, 1, 1] , lowercase : Optional[int]=[2, 2, 2] , lowercase : int=[False, False, True] , lowercase : str=[0.0, 0.0, 0.0] , lowercase : Union[str, Any]=0.02 , lowercase : Optional[int]=1E-12 , lowercase : Any=True , lowercase : Optional[int]=True , lowercase : Tuple=2 , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = patch_sizes
UpperCAmelCase = patch_stride
UpperCAmelCase = patch_padding
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = num_labels
UpperCAmelCase = num_channels
UpperCAmelCase = embed_dim
UpperCAmelCase = num_heads
UpperCAmelCase = stride_kv
UpperCAmelCase = depth
UpperCAmelCase = cls_token
UpperCAmelCase = attention_drop_rate
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def A ( self : Optional[int] ):
'''simple docstring'''
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , lowercase : str , lowercase : Tuple , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = CvtModel(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase )
UpperCAmelCase = (self.image_size, self.image_size)
UpperCAmelCase , UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def A ( self : Dict , lowercase : List[str] , lowercase : Dict , lowercase : int ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = CvtForImageClassification(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _a ( __a , __a , unittest.TestCase ):
__a : Dict = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
__a : Optional[int] = (
{"""feature-extraction""": CvtModel, """image-classification""": CvtForImageClassification}
if is_torch_available()
else {}
)
__a : List[str] = False
__a : Any = False
__a : Optional[int] = False
__a : Any = False
__a : str = False
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = CvtModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def A ( self : str ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : Optional[int] ):
'''simple docstring'''
return
@unittest.skip(reason='''Cvt does not output attentions''' )
def A ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def A ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def A ( self : Dict ):
'''simple docstring'''
pass
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
def check_hidden_states_output(lowercase : List[str] , lowercase : Any , lowercase : Dict ):
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
UpperCAmelCase = model(**self._prepare_for_class(lowercase , lowercase ) )
UpperCAmelCase = outputs.hidden_states
UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(lowercase ) , lowercase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = True
check_hidden_states_output(lowercase , lowercase , lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase = True
check_hidden_states_output(lowercase , lowercase , lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def A ( self : List[str] ):
'''simple docstring'''
pass
@slow
def A ( self : Optional[Any] ):
'''simple docstring'''
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = CvtModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def snake_case_ ():
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _a ( unittest.TestCase ):
@cached_property
def A ( self : Tuple ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowercase )
UpperCAmelCase = self.default_image_processor
UpperCAmelCase = prepare_img()
UpperCAmelCase = image_processor(images=lowercase , return_tensors='''pt''' ).to(lowercase )
# forward pass
with torch.no_grad():
UpperCAmelCase = model(**lowercase )
# verify the logits
UpperCAmelCase = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowercase )
UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1E-4 ) )
| 34
|
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A ='pt'
elif is_tf_available():
A ='tf'
else:
A ='jax'
class _a ( __a , unittest.TestCase ):
__a : Optional[Any] = PerceiverTokenizer
__a : str = False
def A ( self : Union[str, Any] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A ( self : Optional[int] ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def A ( self : Union[str, Any] , **lowercase : int ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[str]=False , lowercase : Union[str, Any]=20 , lowercase : Union[str, Any]=5 ):
'''simple docstring'''
UpperCAmelCase = []
for i in range(len(lowercase ) ):
try:
UpperCAmelCase = tokenizer.decode([i] , clean_up_tokenization_spaces=lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
UpperCAmelCase = list(filter(lambda lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , lowercase ) )
UpperCAmelCase = list(filter(lambda lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=lowercase ) , lowercase ) )
if max_length is not None and len(lowercase ) > max_length:
UpperCAmelCase = toks[:max_length]
if min_length is not None and len(lowercase ) < min_length and len(lowercase ) > 0:
while len(lowercase ) < min_length:
UpperCAmelCase = toks + toks
# toks_str = [t[1] for t in toks]
UpperCAmelCase = [t[0] for t in toks]
# Ensure consistency
UpperCAmelCase = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
if " " not in output_txt and len(lowercase ) > 1:
UpperCAmelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=lowercase )
)
if with_prefix_space:
UpperCAmelCase = ''' ''' + output_txt
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
return output_txt, output_ids
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = '''Unicode €.'''
UpperCAmelCase = tokenizer(lowercase )
UpperCAmelCase = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]Unicode €.[SEP]''' )
UpperCAmelCase = tokenizer('''e è é ê ë''' )
UpperCAmelCase = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
UpperCAmelCase = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
self.assertIsInstance(lowercase , lowercase )
if FRAMEWORK != "jax":
UpperCAmelCase = list(batch.input_ids.numpy()[0] )
else:
UpperCAmelCase = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowercase , lowercase )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , lowercase )
self.assertIn('''attention_mask''' , lowercase )
self.assertNotIn('''decoder_input_ids''' , lowercase )
self.assertNotIn('''decoder_attention_mask''' , lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = [
'''Summary of the text.''',
'''Another summary.''',
]
UpperCAmelCase = tokenizer(
text_target=lowercase , max_length=32 , padding='''max_length''' , truncation=lowercase , return_tensors=lowercase )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
shutil.rmtree(lowercase )
UpperCAmelCase = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
UpperCAmelCase = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowercase )
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
UpperCAmelCase = [f"<extra_id_{i}>" for i in range(125 )]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['''an_additional_special_token'''] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
UpperCAmelCase = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=lowercase )]
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , additional_special_tokens=lowercase , )
self.assertIn('''a_new_additional_special_token''' , tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ) , )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ) , '''�''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
pass
def A ( self : Any ):
'''simple docstring'''
pass
def A ( self : Dict ):
'''simple docstring'''
pass
def A ( self : str ):
'''simple docstring'''
pass
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers(fast=lowercase , do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
UpperCAmelCase = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
UpperCAmelCase = tokenizer.convert_tokens_to_string(lowercase )
self.assertIsInstance(lowercase , lowercase )
| 34
| 1
|
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
A =False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class _a ( unittest.TestCase ):
def __init__( self : Optional[int] , lowercase : Any , lowercase : List[Any]=7 , lowercase : Dict=3 , lowercase : Optional[Any]=18 , lowercase : Dict=30 , lowercase : Optional[int]=400 , lowercase : Union[str, Any]=None , lowercase : List[Any]=True , lowercase : Optional[int]=True , lowercase : List[Any]=None , ):
'''simple docstring'''
UpperCAmelCase = size if size is not None else {'''height''': 20, '''width''': 20}
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = num_channels
UpperCAmelCase = image_size
UpperCAmelCase = min_resolution
UpperCAmelCase = max_resolution
UpperCAmelCase = size
UpperCAmelCase = do_normalize
UpperCAmelCase = do_convert_rgb
UpperCAmelCase = [512, 1_024, 2_048, 4_096]
UpperCAmelCase = patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
def A ( self : Optional[Any] ):
'''simple docstring'''
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
UpperCAmelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw ).convert('''RGB''' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""" , )
@require_torch
@require_vision
class _a ( __a , unittest.TestCase ):
__a : Optional[Any] = PixaStructImageProcessor if is_vision_available() else None
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = PixaStructImageProcessingTester(self )
@property
def A ( self : Tuple ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase , '''do_normalize''' ) )
self.assertTrue(hasattr(lowercase , '''do_convert_rgb''' ) )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.image_processor_tester.prepare_dummy_image()
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
UpperCAmelCase = 2_048
UpperCAmelCase = image_processor(lowercase , return_tensors='''pt''' , max_patches=lowercase )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1E-3 , rtol=1E-3 ) )
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , Image.Image )
# Test not batched input
UpperCAmelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase = image_processor(
lowercase , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , Image.Image )
# Test not batched input
UpperCAmelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
UpperCAmelCase = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(lowercase ):
UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
UpperCAmelCase = '''Hello'''
UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=lowercase , header_text=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase = image_processor(
lowercase , return_tensors='''pt''' , max_patches=lowercase , header_text=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase , numpify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , np.ndarray )
UpperCAmelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase = image_processor(
lowercase , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase , torchify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , torch.Tensor )
# Test not batched input
UpperCAmelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase = image_processor(
lowercase , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""" , )
@require_torch
@require_vision
class _a ( __a , unittest.TestCase ):
__a : Tuple = PixaStructImageProcessor if is_vision_available() else None
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = PixaStructImageProcessingTester(self , num_channels=4 )
UpperCAmelCase = 3
@property
def A ( self : int ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase , '''do_normalize''' ) )
self.assertTrue(hasattr(lowercase , '''do_convert_rgb''' ) )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , Image.Image )
# Test not batched input
UpperCAmelCase = (
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase = image_processor(
lowercase , return_tensors='''pt''' , max_patches=lowercase ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 34
|
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return val
return default
def snake_case_ (_a : Dict , _a : Any=False ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return strtobool(_a ) == 1 # As its name indicates `strtobool` actually returns an int...
def snake_case_ (_a : str , _a : Optional[Any]="no" ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return value
| 34
| 1
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : list[int] ): # This function is recursive
UpperCAmelCase = len(_a )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
UpperCAmelCase = array[0]
UpperCAmelCase = False
UpperCAmelCase = 1
UpperCAmelCase = []
while not is_found and i < array_length:
if array[i] < pivot:
UpperCAmelCase = True
UpperCAmelCase = [element for element in array[i:] if element >= array[i]]
UpperCAmelCase = longest_subsequence(_a )
if len(_a ) > len(_a ):
UpperCAmelCase = temp_array
else:
i += 1
UpperCAmelCase = [element for element in array[1:] if element >= pivot]
UpperCAmelCase = [pivot, *longest_subsequence(_a )]
if len(_a ) > len(_a ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
|
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A =logging.getLogger(__name__)
A ='Hello world! cécé herlolip'
A =namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def snake_case_ (_a : List[Any] , _a : Any ):
UpperCAmelCase = BertAbsConfig(
temp_dir='''.''' , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder='''bert''' , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
UpperCAmelCase = torch.load(_a , lambda _a , _a : storage )
UpperCAmelCase = AbsSummarizer(_a , torch.device('''cpu''' ) , _a )
original.eval()
UpperCAmelCase = BertAbsSummarizer(_a , torch.device('''cpu''' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('''convert the model''' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('''Make sure that the models\' outputs are identical''' )
UpperCAmelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
# prepare the model inputs
UpperCAmelCase = tokenizer.encode('''This is sample éàalj\'-.''' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
UpperCAmelCase = tokenizer.encode('''This is sample 3 éàalj\'-.''' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
UpperCAmelCase = encoder_input_ids
UpperCAmelCase = decoder_input_ids
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
UpperCAmelCase = original(_a , _a , _a , _a , _a , _a , _a )[0]
UpperCAmelCase = original.generator(_a )
UpperCAmelCase = new_model(
_a , _a , _a , _a , _a )[0]
UpperCAmelCase = new_model.generator(_a )
UpperCAmelCase = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.allclose(_a , _a , atol=1E-3 )
if are_identical:
logging.info('''all weights are equal up to 1e-3''' )
else:
raise ValueError('''the weights are different. The new model is likely different from the original one.''' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('''saving the model\'s state dictionary''' )
torch.save(
new_model.state_dict() , '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''' )
if __name__ == "__main__":
A =argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
A =parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 34
| 1
|
'''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 _a ( __a ):
def __init__( self : str , lowercase : TransformeraDModel , lowercase : AutoencoderKL , lowercase : KarrasDiffusionSchedulers , lowercase : Optional[Dict[int, str]] = None , ):
'''simple docstring'''
super().__init__()
self.register_modules(transformer=lowercase , vae=lowercase , scheduler=lowercase )
# create a imagenet -> id dictionary for easier use
UpperCAmelCase = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''',''' ):
UpperCAmelCase = int(lowercase )
UpperCAmelCase = dict(sorted(self.labels.items() ) )
def A ( self : Tuple , lowercase : Union[str, List[str]] ):
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
UpperCAmelCase = 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 : str , 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 , ):
'''simple docstring'''
UpperCAmelCase = len(lowercase )
UpperCAmelCase = self.transformer.config.sample_size
UpperCAmelCase = self.transformer.config.in_channels
UpperCAmelCase = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowercase , device=self.device , dtype=self.transformer.dtype , )
UpperCAmelCase = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
UpperCAmelCase = torch.tensor(lowercase , device=self.device ).reshape(-1 )
UpperCAmelCase = torch.tensor([1_000] * batch_size , device=self.device )
UpperCAmelCase = 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:
UpperCAmelCase = latent_model_input[: len(lowercase ) // 2]
UpperCAmelCase = torch.cat([half, half] , dim=0 )
UpperCAmelCase = self.scheduler.scale_model_input(lowercase , lowercase )
UpperCAmelCase = 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+)
UpperCAmelCase = latent_model_input.device.type == '''mps'''
if isinstance(lowercase , lowercase ):
UpperCAmelCase = torch.floataa if is_mps else torch.floataa
else:
UpperCAmelCase = torch.intaa if is_mps else torch.intaa
UpperCAmelCase = torch.tensor([timesteps] , dtype=lowercase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
UpperCAmelCase = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCAmelCase = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
UpperCAmelCase = self.transformer(
lowercase , timestep=lowercase , class_labels=lowercase ).sample
# perform guidance
if guidance_scale > 1:
UpperCAmelCase , UpperCAmelCase = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
UpperCAmelCase , UpperCAmelCase = torch.split(lowercase , len(lowercase ) // 2 , dim=0 )
UpperCAmelCase = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
UpperCAmelCase = torch.cat([half_eps, half_eps] , dim=0 )
UpperCAmelCase = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
UpperCAmelCase , UpperCAmelCase = torch.split(lowercase , lowercase , dim=1 )
else:
UpperCAmelCase = noise_pred
# compute previous image: x_t -> x_t-1
UpperCAmelCase = self.scheduler.step(lowercase , lowercase , lowercase ).prev_sample
if guidance_scale > 1:
UpperCAmelCase , UpperCAmelCase = latent_model_input.chunk(2 , dim=0 )
else:
UpperCAmelCase = latent_model_input
UpperCAmelCase = 1 / self.vae.config.scaling_factor * latents
UpperCAmelCase = self.vae.decode(lowercase ).sample
UpperCAmelCase = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase = self.numpy_to_pil(lowercase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowercase )
| 34
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : List[Any] , **lowercase : List[str] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : int ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : int , *lowercase : Tuple , **lowercase : Dict ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : List[str] , *lowercase : Optional[int] , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Union[str, Any] , **lowercase : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[int] , *lowercase : Union[str, Any] , **lowercase : Any ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Any , *lowercase : Dict , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : Any = ["""flax""", """transformers"""]
def __init__( self : Any , *lowercase : Optional[Any] , **lowercase : Optional[int] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Optional[Any] , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : str , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
| 34
| 1
|
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def snake_case_ (_a : int = 1_0_0_0_0_0_0 , _a : int = 1_0 ):
UpperCAmelCase = defaultdict(_a )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
UpperCAmelCase = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
UpperCAmelCase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(_a , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 1_0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 34
|
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
A ={
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
A ={
'b0': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 2_24,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 2_40,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 14_08,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 2_60,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 15_36,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 3_00,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 17_92,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 3_80,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 20_48,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 4_56,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 23_04,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 5_28,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 25_60,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 6_00,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def snake_case_ (_a : List[str] ):
UpperCAmelCase = EfficientNetConfig()
UpperCAmelCase = CONFIG_MAP[model_name]['''hidden_dim''']
UpperCAmelCase = CONFIG_MAP[model_name]['''width_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''depth_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dropout_rate''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dw_padding''']
UpperCAmelCase = '''huggingface/label-files'''
UpperCAmelCase = '''imagenet-1k-id2label.json'''
UpperCAmelCase = 1_0_0_0
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ():
UpperCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase = Image.open(requests.get(_a , stream=_a ).raw )
return im
def snake_case_ (_a : str ):
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163] , do_center_crop=_a , )
return preprocessor
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
UpperCAmelCase = sorted(set(_a ) )
UpperCAmelCase = len(_a )
UpperCAmelCase = {b: str(_a ) for b, i in zip(_a , range(_a ) )}
UpperCAmelCase = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
UpperCAmelCase = block_name_mapping[b]
rename_keys.append((F"block{b}_expand_conv/kernel:0", F"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((F"block{b}_expand_bn/gamma:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((F"block{b}_expand_bn/beta:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(F"block{b}_expand_bn/moving_mean:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(F"block{b}_expand_bn/moving_variance:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(F"block{b}_dwconv/depthwise_kernel:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((F"block{b}_bn/gamma:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((F"block{b}_bn/beta:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(F"block{b}_bn/moving_mean:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(F"block{b}_bn/moving_variance:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((F"block{b}_se_reduce/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((F"block{b}_se_reduce/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((F"block{b}_se_expand/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((F"block{b}_se_expand/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(F"block{b}_project_conv/kernel:0", F"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((F"block{b}_project_bn/gamma:0", F"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((F"block{b}_project_bn/beta:0", F"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(F"block{b}_project_bn/moving_mean:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(F"block{b}_project_bn/moving_variance:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
UpperCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCAmelCase = '''efficientnet.''' + item[1]
UpperCAmelCase = '''classifier.weight'''
UpperCAmelCase = '''classifier.bias'''
return key_mapping
def snake_case_ (_a : Dict , _a : List[str] , _a : Dict ):
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
UpperCAmelCase = torch.from_numpy(np.transpose(_a ) )
else:
UpperCAmelCase = torch.from_numpy(_a )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_a )
@torch.no_grad()
def snake_case_ (_a : Optional[Any] , _a : List[str] , _a : Optional[int] , _a : Dict ):
UpperCAmelCase = model_classes[model_name](
include_top=_a , weights='''imagenet''' , input_tensor=_a , input_shape=_a , pooling=_a , classes=1_0_0_0 , classifier_activation='''softmax''' , )
UpperCAmelCase = original_model.trainable_variables
UpperCAmelCase = original_model.non_trainable_variables
UpperCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCAmelCase = param.numpy()
UpperCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
UpperCAmelCase = get_efficientnet_config(_a )
UpperCAmelCase = EfficientNetForImageClassification(_a ).eval()
UpperCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
UpperCAmelCase = rename_keys(_a )
replace_params(_a , _a , _a )
# Initialize preprocessor and preprocess input image
UpperCAmelCase = convert_image_processor(_a )
UpperCAmelCase = preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCAmelCase = hf_model(**_a )
UpperCAmelCase = outputs.logits.detach().numpy()
# Original model inference
UpperCAmelCase = False
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
UpperCAmelCase = image.img_to_array(_a )
UpperCAmelCase = np.expand_dims(_a , axis=0 )
UpperCAmelCase = original_model.predict(_a )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_a , _a , atol=1E-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(_a ):
os.mkdir(_a )
# Save converted model and image processor
hf_model.save_pretrained(_a )
preprocessor.save_pretrained(_a )
if push_to_hub:
# Push model and image processor to hub
print(F"Pushing converted {model_name} to the hub..." )
UpperCAmelCase = F"efficientnet-{model_name}"
preprocessor.push_to_hub(_a )
hf_model.push_to_hub(_a )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
A =parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
def __init__( self : Optional[Any] , lowercase : int , lowercase : str=13 , lowercase : Any=7 , lowercase : str=True , lowercase : int=True , lowercase : int=True , lowercase : Any=True , lowercase : Any=99 , lowercase : Any=32 , lowercase : Dict=5 , lowercase : Optional[int]=4 , lowercase : Dict=37 , lowercase : int="gelu" , lowercase : Union[str, Any]=0.1 , lowercase : Union[str, Any]=0.1 , lowercase : str=512 , lowercase : Tuple=16 , lowercase : List[str]=2 , lowercase : str=0.02 , lowercase : str=3 , lowercase : Dict=4 , lowercase : int=None , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_input_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = scope
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase = 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 = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : Tuple ):
'''simple docstring'''
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase , initializer_range=self.initializer_range , )
def A ( self : Optional[Any] , lowercase : Union[str, Any] , lowercase : Optional[Any] , lowercase : Tuple , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : List[Any] , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = NystromformerModel(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=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 A ( self : Union[str, Any] , lowercase : Optional[int] , lowercase : Any , lowercase : str , lowercase : int , lowercase : int , lowercase : Dict , lowercase : int ):
'''simple docstring'''
UpperCAmelCase = NystromformerForMaskedLM(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Union[str, Any] , lowercase : Optional[Any] , lowercase : Dict , lowercase : Tuple , lowercase : int , lowercase : Optional[Any] , lowercase : List[Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = NystromformerForQuestionAnswering(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , start_positions=lowercase , end_positions=lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[str] , lowercase : Optional[Any] , lowercase : List[str] , lowercase : Optional[Any] , lowercase : str , lowercase : Optional[int] , lowercase : Tuple , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = NystromformerForSequenceClassification(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Any , lowercase : str , lowercase : List[Any] , lowercase : str , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : Tuple , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = NystromformerForTokenClassification(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Optional[int] , lowercase : int , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : Any , lowercase : Optional[Any] , lowercase : List[str] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = self.num_choices
UpperCAmelCase = NystromformerForMultipleChoice(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _a ( __a , __a , unittest.TestCase ):
__a : Optional[int] = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
__a : List[Any] = (
{
"""feature-extraction""": NystromformerModel,
"""fill-mask""": NystromformerForMaskedLM,
"""question-answering""": NystromformerForQuestionAnswering,
"""text-classification""": NystromformerForSequenceClassification,
"""token-classification""": NystromformerForTokenClassification,
"""zero-shot""": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : Optional[int] = False
__a : int = False
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = NystromformerModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase , hidden_size=37 )
def A ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase = type
self.model_tester.create_and_check_model(*lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase )
@slow
def A ( self : List[Any] ):
'''simple docstring'''
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = NystromformerModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@require_torch
class _a ( unittest.TestCase ):
@slow
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = NystromformerModel.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
UpperCAmelCase = model(lowercase )[0]
UpperCAmelCase = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , lowercase )
UpperCAmelCase = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase , atol=1E-4 ) )
@slow
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = '''the [MASK] of Belgium is Brussels'''
UpperCAmelCase = AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = tokenizer(lowercase , return_tensors='''pt''' )
with torch.no_grad():
UpperCAmelCase = model(encoding.input_ids ).logits
UpperCAmelCase = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(lowercase ) , '''capital''' )
| 34
|
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
A =soup.find('meta', {'property': 'og:image'})['content']
A =requests.get(image_url).content
A =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(f"""Done. Image saved to disk as {file_name}.""")
| 34
| 1
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
def snake_case_ (_a : List[str] ):
UpperCAmelCase = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
UpperCAmelCase = 1_2_8
elif "12-12" in model_name:
UpperCAmelCase = 1_2
UpperCAmelCase = 1_2
elif "14-14" in model_name:
UpperCAmelCase = 1_4
UpperCAmelCase = 1_4
elif "16-16" in model_name:
UpperCAmelCase = 1_6
UpperCAmelCase = 1_6
else:
raise ValueError('''Model not supported''' )
UpperCAmelCase = '''huggingface/label-files'''
if "speech-commands" in model_name:
UpperCAmelCase = 3_5
UpperCAmelCase = '''speech-commands-v2-id2label.json'''
else:
UpperCAmelCase = 5_2_7
UpperCAmelCase = '''audioset-id2label.json'''
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ (_a : Tuple ):
if "module.v" in name:
UpperCAmelCase = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
UpperCAmelCase = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
UpperCAmelCase = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
UpperCAmelCase = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
UpperCAmelCase = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
UpperCAmelCase = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
UpperCAmelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
UpperCAmelCase = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
UpperCAmelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
UpperCAmelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
UpperCAmelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
UpperCAmelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
UpperCAmelCase = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
UpperCAmelCase = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
UpperCAmelCase = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def snake_case_ (_a : Dict , _a : List[Any] ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(_a )
if "qkv" in key:
UpperCAmelCase = key.split('''.''' )
UpperCAmelCase = int(key_split[3] )
UpperCAmelCase = config.hidden_size
if "weight" in key:
UpperCAmelCase = val[:dim, :]
UpperCAmelCase = val[dim : dim * 2, :]
UpperCAmelCase = val[-dim:, :]
else:
UpperCAmelCase = val[:dim]
UpperCAmelCase = val[dim : dim * 2]
UpperCAmelCase = val[-dim:]
else:
UpperCAmelCase = val
return orig_state_dict
def snake_case_ (_a : Tuple ):
UpperCAmelCase = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(_a , _a )
@torch.no_grad()
def snake_case_ (_a : int , _a : Union[str, Any] , _a : Dict=False ):
UpperCAmelCase = get_audio_spectrogram_transformer_config(_a )
UpperCAmelCase = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
UpperCAmelCase = model_name_to_url[model_name]
UpperCAmelCase = torch.hub.load_state_dict_from_url(_a , map_location='''cpu''' )
# remove some keys
remove_keys(_a )
# rename some keys
UpperCAmelCase = convert_state_dict(_a , _a )
# load 🤗 model
UpperCAmelCase = ASTForAudioClassification(_a )
model.eval()
model.load_state_dict(_a )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
UpperCAmelCase = -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
UpperCAmelCase = 4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
UpperCAmelCase = 1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
UpperCAmelCase = ASTFeatureExtractor(mean=_a , std=_a , max_length=_a )
if "speech-commands" in model_name:
UpperCAmelCase = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
UpperCAmelCase = dataset[0]['''audio''']['''array''']
else:
UpperCAmelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
UpperCAmelCase , UpperCAmelCase = torchaudio.load(_a )
UpperCAmelCase = waveform.squeeze().numpy()
UpperCAmelCase = feature_extractor(_a , sampling_rate=1_6_0_0_0 , return_tensors='''pt''' )
# forward pass
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
UpperCAmelCase = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
UpperCAmelCase = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
UpperCAmelCase = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
UpperCAmelCase = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
UpperCAmelCase = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
UpperCAmelCase = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
UpperCAmelCase = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
UpperCAmelCase = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , _a , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(F"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_a )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(F"MIT/{model_name}" )
feature_extractor.push_to_hub(F"MIT/{model_name}" )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub.'
)
A =parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 34
|
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _a ( __a ):
__a : str = ["""vqvae"""]
def __init__( self : str , lowercase : AutoencoderKL , lowercase : UNetaDConditionModel , lowercase : Mel , lowercase : Union[DDIMScheduler, DDPMScheduler] , ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowercase , scheduler=lowercase , mel=lowercase , vqvae=lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
return 50 if isinstance(self.scheduler , lowercase ) else 1_000
@torch.no_grad()
def __call__( self : Optional[Any] , lowercase : int = 1 , lowercase : str = None , lowercase : np.ndarray = None , lowercase : int = 0 , lowercase : int = 0 , lowercase : int = None , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : float = 0 , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : torch.Tensor = None , lowercase : torch.Tensor = None , lowercase : Tuple=True , ):
'''simple docstring'''
UpperCAmelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowercase , device=self.device , )
UpperCAmelCase = noise
UpperCAmelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowercase , lowercase )
UpperCAmelCase = self.mel.audio_slice_to_image(lowercase )
UpperCAmelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase = (input_image / 255) * 2 - 1
UpperCAmelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase = self.vqvae.encode(torch.unsqueeze(lowercase , 0 ) ).latent_dist.sample(
generator=lowercase )[0]
UpperCAmelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase = int(mask_start_secs * pixels_per_second )
UpperCAmelCase = int(mask_end_secs * pixels_per_second )
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowercase ):
UpperCAmelCase = self.unet(lowercase , lowercase , lowercase )['''sample''']
else:
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
if isinstance(self.scheduler , lowercase ):
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , eta=lowercase , generator=lowercase , )['''prev_sample''']
else:
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , generator=lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
UpperCAmelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase = self.vqvae.decode(lowercase )['''sample''']
UpperCAmelCase = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase = (images * 255).round().astype('''uint8''' )
UpperCAmelCase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
UpperCAmelCase = [self.mel.image_to_audio(lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowercase ) )
@torch.no_grad()
def A ( self : Dict , lowercase : List[Image.Image] , lowercase : int = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler , lowercase )
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase = (sample / 255) * 2 - 1
UpperCAmelCase = torch.Tensor(lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase = self.scheduler.alphas_cumprod[t]
UpperCAmelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase = 1 - alpha_prod_t
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def A ( lowercase : torch.Tensor , lowercase : torch.Tensor , lowercase : float ):
'''simple docstring'''
UpperCAmelCase = acos(torch.dot(torch.flatten(lowercase ) , torch.flatten(lowercase ) ) / torch.norm(lowercase ) / torch.norm(lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(lowercase ) + sin(alpha * theta ) * xa / sin(lowercase )
| 34
| 1
|
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : dict , _a : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
UpperCAmelCase = cst_fwd.get(_a , np.inf )
UpperCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
UpperCAmelCase = new_cost_f
UpperCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
UpperCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def snake_case_ (_a : str , _a : str , _a : dict , _a : dict ):
UpperCAmelCase = -1
UpperCAmelCase = set()
UpperCAmelCase = set()
UpperCAmelCase = {source: 0}
UpperCAmelCase = {destination: 0}
UpperCAmelCase = {source: None}
UpperCAmelCase = {destination: None}
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
UpperCAmelCase , UpperCAmelCase = queue_forward.get()
visited_forward.add(_a )
UpperCAmelCase , UpperCAmelCase = queue_backward.get()
visited_backward.add(_a )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
UpperCAmelCase = shortest_distance
return shortest_path_distance
A ={
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
A ={
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
|
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
A =logging.get_logger(__name__)
A =TypeVar('DatasetType', Dataset, IterableDataset)
def snake_case_ (_a : List[DatasetType] , _a : Optional[List[float]] = None , _a : Optional[int] = None , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
else:
return _interleave_iterable_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
def snake_case_ (_a : List[DatasetType] , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : int = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_a , info=_a , split=_a , axis=_a )
else:
return _concatenate_iterable_datasets(_a , info=_a , split=_a , axis=_a )
| 34
| 1
|
'''simple docstring'''
from numpy import exp, pi, sqrt
def snake_case_ (_a : str , _a : float = 0.0 , _a : float = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
|
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def snake_case_ (_a : Tuple ):
return {key.lstrip('''-''' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def snake_case_ ():
UpperCAmelCase = ArgumentParser(
'''HuggingFace Datasets CLI tool''' , usage='''datasets-cli <command> [<args>]''' , allow_abbrev=_a )
UpperCAmelCase = parser.add_subparsers(help='''datasets-cli command helpers''' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(_a )
EnvironmentCommand.register_subcommand(_a )
TestCommand.register_subcommand(_a )
RunBeamCommand.register_subcommand(_a )
DummyDataCommand.register_subcommand(_a )
# Parse args
UpperCAmelCase , UpperCAmelCase = parser.parse_known_args()
if not hasattr(_a , '''func''' ):
parser.print_help()
exit(1 )
UpperCAmelCase = parse_unknown_args(_a )
# Run
UpperCAmelCase = args.func(_a , **_a )
service.run()
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( __a ):
__a : Any = (PNDMScheduler,)
__a : Any = (("""num_inference_steps""", 50),)
def A ( self : Dict , **lowercase : Tuple ):
'''simple docstring'''
UpperCAmelCase = {
'''num_train_timesteps''': 1_000,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**lowercase )
return config
def A ( self : Dict , lowercase : Dict=0 , **lowercase : str ):
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config(**lowercase )
UpperCAmelCase = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# copy over dummy past residuals
UpperCAmelCase = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase )
UpperCAmelCase = scheduler_class.from_pretrained(lowercase )
new_scheduler.set_timesteps(lowercase )
# copy over dummy past residuals
UpperCAmelCase = dummy_past_residuals[:]
UpperCAmelCase = scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample
UpperCAmelCase = new_scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase = scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample
UpperCAmelCase = new_scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : Tuple ):
'''simple docstring'''
pass
def A ( self : Any , lowercase : Optional[Any]=0 , **lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config()
UpperCAmelCase = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase )
UpperCAmelCase = scheduler_class.from_pretrained(lowercase )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase = dummy_past_residuals[:]
UpperCAmelCase = scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample
UpperCAmelCase = new_scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase = scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample
UpperCAmelCase = new_scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : Optional[Any] , **lowercase : Any ):
'''simple docstring'''
UpperCAmelCase = self.scheduler_classes[0]
UpperCAmelCase = self.get_scheduler_config(**lowercase )
UpperCAmelCase = scheduler_class(**lowercase )
UpperCAmelCase = 10
UpperCAmelCase = self.dummy_model()
UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(lowercase )
for i, t in enumerate(scheduler.prk_timesteps ):
UpperCAmelCase = model(lowercase , lowercase )
UpperCAmelCase = scheduler.step_prk(lowercase , lowercase , lowercase ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
UpperCAmelCase = model(lowercase , lowercase )
UpperCAmelCase = scheduler.step_plms(lowercase , lowercase , lowercase ).prev_sample
return sample
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config()
UpperCAmelCase = scheduler_class(**lowercase )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase , '''set_timesteps''' ):
scheduler.set_timesteps(lowercase )
elif num_inference_steps is not None and not hasattr(lowercase , '''set_timesteps''' ):
UpperCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCAmelCase = dummy_past_residuals[:]
UpperCAmelCase = scheduler.step_prk(lowercase , 0 , lowercase , **lowercase ).prev_sample
UpperCAmelCase = scheduler.step_prk(lowercase , 1 , lowercase , **lowercase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
UpperCAmelCase = scheduler.step_plms(lowercase , 0 , lowercase , **lowercase ).prev_sample
UpperCAmelCase = scheduler.step_plms(lowercase , 1 , lowercase , **lowercase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ):
'''simple docstring'''
for timesteps in [100, 1_000]:
self.check_over_configs(num_train_timesteps=lowercase )
def A ( self : Any ):
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowercase )
UpperCAmelCase = self.scheduler_classes[0]
UpperCAmelCase = self.get_scheduler_config(steps_offset=1 )
UpperCAmelCase = scheduler_class(**lowercase )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def A ( self : Any ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=lowercase , beta_end=lowercase )
def A ( self : List[str] ):
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowercase )
def A ( self : str ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase )
def A ( self : Tuple ):
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowercase )
def A ( self : int ):
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = 27
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
UpperCAmelCase = self.get_scheduler_config()
UpperCAmelCase = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
UpperCAmelCase = scheduler.step_prk(lowercase , lowercase , lowercase ).prev_sample
def A ( self : Optional[Any] ):
'''simple docstring'''
with self.assertRaises(lowercase ):
UpperCAmelCase = self.scheduler_classes[0]
UpperCAmelCase = self.get_scheduler_config()
UpperCAmelCase = scheduler_class(**lowercase )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.full_loop()
UpperCAmelCase = torch.sum(torch.abs(lowercase ) )
UpperCAmelCase = torch.mean(torch.abs(lowercase ) )
assert abs(result_sum.item() - 198.1318 ) < 1E-2
assert abs(result_mean.item() - 0.2580 ) < 1E-3
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' )
UpperCAmelCase = torch.sum(torch.abs(lowercase ) )
UpperCAmelCase = torch.mean(torch.abs(lowercase ) )
assert abs(result_sum.item() - 67.3986 ) < 1E-2
assert abs(result_mean.item() - 0.0878 ) < 1E-3
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01 )
UpperCAmelCase = torch.sum(torch.abs(lowercase ) )
UpperCAmelCase = torch.mean(torch.abs(lowercase ) )
assert abs(result_sum.item() - 230.0399 ) < 1E-2
assert abs(result_mean.item() - 0.2995 ) < 1E-3
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01 )
UpperCAmelCase = torch.sum(torch.abs(lowercase ) )
UpperCAmelCase = torch.mean(torch.abs(lowercase ) )
assert abs(result_sum.item() - 186.9482 ) < 1E-2
assert abs(result_mean.item() - 0.2434 ) < 1E-3
| 34
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
A =logging.getLogger()
def snake_case_ ():
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
UpperCAmelCase = parser.parse_args()
return args.f
def snake_case_ (_a : List[str] , _a : Union[str, Any]="eval" ):
UpperCAmelCase = os.path.join(_a , F"{split}_results.json" )
if os.path.exists(_a ):
with open(_a , '''r''' ) as f:
return json.load(_a )
raise ValueError(F"can't find {path}" )
A =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _a ( __a ):
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_glue.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
@slow
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_clm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 100 )
@slow
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_summarization_flax.main()
UpperCAmelCase = get_results(lowercase , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_ta_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = 7 if get_gpu_count() > 1 else 2
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_ner.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_qa.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 34
| 1
|
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _a ( __a ):
__a : List[Any] = CustomTokenizer
pass
| 34
|
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[str] ):
'''simple docstring'''
for word in words:
self.insert(lowercase )
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
UpperCAmelCase = TrieNode()
UpperCAmelCase = curr.nodes[char]
UpperCAmelCase = True
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
return False
UpperCAmelCase = curr.nodes[char]
return curr.is_leaf
def A ( self : str , lowercase : str ):
'''simple docstring'''
def _delete(lowercase : TrieNode , lowercase : str , lowercase : int ) -> bool:
if index == len(lowercase ):
# If word does not exist
if not curr.is_leaf:
return False
UpperCAmelCase = False
return len(curr.nodes ) == 0
UpperCAmelCase = word[index]
UpperCAmelCase = curr.nodes.get(lowercase )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
UpperCAmelCase = _delete(lowercase , lowercase , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , lowercase , 0 )
def snake_case_ (_a : TrieNode , _a : str ):
if node.is_leaf:
print(_a , end=''' ''' )
for key, value in node.nodes.items():
print_words(_a , word + key )
def snake_case_ ():
UpperCAmelCase = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase = TrieNode()
root.insert_many(_a )
# print_words(root, "")
assert all(root.find(_a ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def snake_case_ (_a : str , _a : bool ):
print(str(_a ) , '''works!''' if passes else '''doesn\'t work :(''' )
def snake_case_ ():
assert test_trie()
def snake_case_ ():
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case_ ():
UpperCAmelCase = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch '''
'''helper utility that will spawn up '''
'''multiple distributed processes'''
) )
# Optional arguments for the launch helper
parser.add_argument('''--num_cores''' , type=_a , default=1 , help='''Number of TPU cores to use (1 or 8).''' )
# positional
parser.add_argument(
'''training_script''' , type=_a , help=(
'''The full path to the single TPU training '''
'''program/script to be launched in parallel, '''
'''followed by all the arguments for the '''
'''training script'''
) , )
# rest from the training program
parser.add_argument('''training_script_args''' , nargs=_a )
return parser.parse_args()
def snake_case_ ():
UpperCAmelCase = parse_args()
# Import training_script as a module.
UpperCAmelCase = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
UpperCAmelCase = script_fpath.stem
UpperCAmelCase = importlib.import_module(_a )
# Patch sys.argv
UpperCAmelCase = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 34
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}
class _a ( __a ):
__a : Union[str, Any] = """encodec"""
def __init__( self : Tuple , lowercase : List[str]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowercase : Any=24_000 , lowercase : str=1 , lowercase : Optional[int]=False , lowercase : Optional[Any]=None , lowercase : str=None , lowercase : Tuple=128 , lowercase : Union[str, Any]=32 , lowercase : Union[str, Any]=1 , lowercase : Optional[Any]=[8, 5, 4, 2] , lowercase : Any="weight_norm" , lowercase : Tuple=7 , lowercase : int=7 , lowercase : Dict=3 , lowercase : List[Any]=2 , lowercase : str=True , lowercase : List[str]="reflect" , lowercase : List[Any]=2 , lowercase : Optional[Any]=2 , lowercase : int=1.0 , lowercase : Dict=1_024 , lowercase : str=None , lowercase : Union[str, Any]=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = target_bandwidths
UpperCAmelCase = sampling_rate
UpperCAmelCase = audio_channels
UpperCAmelCase = normalize
UpperCAmelCase = chunk_length_s
UpperCAmelCase = overlap
UpperCAmelCase = hidden_size
UpperCAmelCase = num_filters
UpperCAmelCase = num_residual_layers
UpperCAmelCase = upsampling_ratios
UpperCAmelCase = norm_type
UpperCAmelCase = kernel_size
UpperCAmelCase = last_kernel_size
UpperCAmelCase = residual_kernel_size
UpperCAmelCase = dilation_growth_rate
UpperCAmelCase = use_causal_conv
UpperCAmelCase = pad_mode
UpperCAmelCase = compress
UpperCAmelCase = num_lstm_layers
UpperCAmelCase = trim_right_ratio
UpperCAmelCase = codebook_size
UpperCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}" )
super().__init__(**lowercase )
@property
def A ( self : Dict ):
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 34
| 1
|
'''simple docstring'''
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
A =True
from torch.cuda.amp import autocast
A =logging.getLogger(__name__)
def snake_case_ (_a : str=None , _a : List[Any]=None ):
return field(default_factory=lambda: default , metadata=_a )
@dataclass
class _a :
__a : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__a : Optional[bool] = field(
default=__a , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
__a : Optional[float] = field(
default=0.1 , metadata={"""help""": """The dropout ratio for the attention probabilities."""} )
__a : Optional[float] = field(
default=0.1 , metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""} )
__a : Optional[float] = field(
default=0.1 , metadata={
"""help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
} , )
__a : Optional[float] = field(
default=0.1 , metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""} , )
__a : Optional[float] = field(
default=0.05 , metadata={
"""help""": (
"""Propability of each feature vector along the time axis to be chosen as the start of the vector"""
"""span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
"""vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
)
} , )
__a : Optional[float] = field(default=0.0 , metadata={"""help""": """The LayerDrop probability."""} )
@dataclass
class _a :
__a : Optional[str] = field(
default=__a , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
__a : Optional[str] = field(
default="""train+validation""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
__a : bool = field(
default=__a , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
__a : Optional[int] = field(
default=__a , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__a : Optional[int] = field(
default=__a , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__a : Optional[int] = field(
default=__a , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of validation examples to this """
"""value if set."""
)
} , )
__a : List[str] = list_field(
default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """'""", """\"""", """�"""] , metadata={"""help""": """A list of characters to remove from the transcripts."""} , )
@dataclass
class _a :
__a : WavaVecaProcessor
__a : Union[bool, str] = True
__a : Optional[int] = None
__a : Optional[int] = None
__a : Optional[int] = None
__a : Optional[int] = None
def __call__( self : int , lowercase : List[Dict[str, Union[List[int], torch.Tensor]]] ):
'''simple docstring'''
UpperCAmelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
UpperCAmelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
UpperCAmelCase = self.processor.pad(
lowercase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
UpperCAmelCase = self.processor.pad(
labels=lowercase , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
UpperCAmelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
UpperCAmelCase = labels
return batch
class _a ( __a ):
def A ( self : Union[str, Any] , lowercase : nn.Module , lowercase : Dict[str, Union[torch.Tensor, Any]] ):
'''simple docstring'''
model.train()
UpperCAmelCase = self._prepare_inputs(lowercase )
if self.use_amp:
with autocast():
UpperCAmelCase = self.compute_loss(lowercase , lowercase )
else:
UpperCAmelCase = self.compute_loss(lowercase , lowercase )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
UpperCAmelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
UpperCAmelCase = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']" )
if self.args.gradient_accumulation_steps > 1:
UpperCAmelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowercase ).backward()
elif self.use_apex:
with amp.scale_loss(lowercase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowercase )
else:
loss.backward()
return loss.detach()
def snake_case_ ():
# 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 = 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 , UpperCAmelCase , UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase = 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()
logger.info('''Training/evaluation parameters %s''' , _a )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
UpperCAmelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
UpperCAmelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
UpperCAmelCase = F"[{''.join(data_args.chars_to_ignore )}]"
def remove_special_characters(_a : str ):
UpperCAmelCase = re.sub(_a , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
UpperCAmelCase = train_dataset.map(_a , remove_columns=['''sentence'''] )
UpperCAmelCase = eval_dataset.map(_a , remove_columns=['''sentence'''] )
def extract_all_chars(_a : int ):
UpperCAmelCase = ''' '''.join(batch['''text'''] )
UpperCAmelCase = list(set(_a ) )
return {"vocab": [vocab], "all_text": [all_text]}
UpperCAmelCase = train_dataset.map(
_a , batched=_a , batch_size=-1 , keep_in_memory=_a , remove_columns=train_dataset.column_names , )
UpperCAmelCase = train_dataset.map(
_a , batched=_a , batch_size=-1 , keep_in_memory=_a , remove_columns=eval_dataset.column_names , )
UpperCAmelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
UpperCAmelCase = {v: k for k, v in enumerate(_a )}
UpperCAmelCase = vocab_dict[''' ''']
del vocab_dict[" "]
UpperCAmelCase = len(_a )
UpperCAmelCase = len(_a )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(_a , _a )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
UpperCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0.0 , do_normalize=_a , return_attention_mask=_a )
UpperCAmelCase = WavaVecaProcessor(feature_extractor=_a , tokenizer=_a )
UpperCAmelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
UpperCAmelCase = min(len(_a ) , data_args.max_train_samples )
UpperCAmelCase = train_dataset.select(range(_a ) )
if data_args.max_val_samples is not None:
UpperCAmelCase = eval_dataset.select(range(data_args.max_val_samples ) )
UpperCAmelCase = torchaudio.transforms.Resample(4_8_0_0_0 , 1_6_0_0_0 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(_a : Optional[Any] ):
UpperCAmelCase , UpperCAmelCase = torchaudio.load(batch['''path'''] )
UpperCAmelCase = resampler(_a ).squeeze().numpy()
UpperCAmelCase = 1_6_0_0_0
UpperCAmelCase = batch['''text''']
return batch
UpperCAmelCase = train_dataset.map(
_a , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
UpperCAmelCase = eval_dataset.map(
_a , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(_a : int ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), F"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."
UpperCAmelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(_a )
return batch
UpperCAmelCase = train_dataset.map(
_a , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=_a , num_proc=data_args.preprocessing_num_workers , )
UpperCAmelCase = eval_dataset.map(
_a , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=_a , num_proc=data_args.preprocessing_num_workers , )
# Metric
UpperCAmelCase = datasets.load_metric('''wer''' )
def compute_metrics(_a : Tuple ):
UpperCAmelCase = pred.predictions
UpperCAmelCase = np.argmax(_a , axis=-1 )
UpperCAmelCase = processor.tokenizer.pad_token_id
UpperCAmelCase = processor.batch_decode(_a )
# we do not want to group tokens when computing the metrics
UpperCAmelCase = processor.batch_decode(pred.label_ids , group_tokens=_a )
UpperCAmelCase = wer_metric.compute(predictions=_a , references=_a )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
UpperCAmelCase = DataCollatorCTCWithPadding(processor=_a , padding=_a )
# Initialize our Trainer
UpperCAmelCase = CTCTrainer(
model=_a , data_collator=_a , args=_a , compute_metrics=_a , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
UpperCAmelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
UpperCAmelCase = model_args.model_name_or_path
else:
UpperCAmelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
UpperCAmelCase = trainer.train(resume_from_checkpoint=_a )
trainer.save_model()
UpperCAmelCase = train_result.metrics
UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_a )
)
UpperCAmelCase = min(_a , len(_a ) )
trainer.log_metrics('''train''' , _a )
trainer.save_metrics('''train''' , _a )
trainer.save_state()
# Evaluation
UpperCAmelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase = trainer.evaluate()
UpperCAmelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(_a )
UpperCAmelCase = min(_a , len(_a ) )
trainer.log_metrics('''eval''' , _a )
trainer.save_metrics('''eval''' , _a )
return results
if __name__ == "__main__":
main()
| 34
|
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A =logging.get_logger(__name__)
class _a ( __a ):
__a : str = ["""pixel_values"""]
def __init__( self : Optional[int] , lowercase : bool = True , lowercase : Optional[Dict[str, int]] = None , lowercase : PILImageResampling = PILImageResampling.BILINEAR , lowercase : bool = True , lowercase : Dict[str, int] = None , lowercase : bool = True , lowercase : Union[int, float] = 1 / 255 , lowercase : bool = True , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = size if size is not None else {'''shortest_edge''': 256}
UpperCAmelCase = get_size_dict(lowercase , default_to_square=lowercase )
UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = resample
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : PILImageResampling = PILImageResampling.BICUBIC , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Any , ):
'''simple docstring'''
UpperCAmelCase = 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()}" )
UpperCAmelCase = 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 A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : int , ):
'''simple docstring'''
UpperCAmelCase = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(lowercase , size=(size['''height'''], size['''width''']) , data_format=lowercase , **lowercase )
def A ( self : Tuple , lowercase : np.ndarray , lowercase : float , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : List[str] ):
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[int] , lowercase : np.ndarray , lowercase : Union[float, List[float]] , lowercase : Union[float, List[float]] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[int] , lowercase : ImageInput , lowercase : Optional[bool] = None , lowercase : Dict[str, int] = None , lowercase : PILImageResampling = None , lowercase : bool = None , lowercase : Dict[str, int] = None , lowercase : Optional[bool] = None , lowercase : Optional[float] = None , lowercase : Optional[bool] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[str, TensorType]] = None , lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **lowercase : Dict , ):
'''simple docstring'''
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(lowercase , default_to_square=lowercase )
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = 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.
UpperCAmelCase = [to_numpy_array(lowercase ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_center_crop:
UpperCAmelCase = [self.center_crop(image=lowercase , size=lowercase ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=lowercase , tensor_type=lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[Tuple] = None ):
'''simple docstring'''
UpperCAmelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowercase ) != len(lowercase ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(lowercase ):
UpperCAmelCase = target_sizes.numpy()
UpperCAmelCase = []
for idx in range(len(lowercase ) ):
UpperCAmelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=lowercase )
UpperCAmelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowercase )
else:
UpperCAmelCase = logits.argmax(dim=1 )
UpperCAmelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 34
| 1
|
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
A =logging.get_logger(__name__)
def snake_case_ (_a : Union[str, Any] , _a : int ):
try:
with open(_a , '''rb''' ) as flax_state_f:
UpperCAmelCase = from_bytes(_a , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(_a ) as f:
if f.read().startswith('''version''' ):
raise OSError(
'''You seem to have cloned a repository without having git-lfs installed. Please'''
''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
''' folder you cloned.''' )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. " )
return load_flax_weights_in_pytorch_model(_a , _a )
def snake_case_ (_a : List[str] , _a : List[Any] ):
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
# check if we have bf16 weights
UpperCAmelCase = flatten_dict(jax.tree_util.tree_map(lambda _a : x.dtype == jnp.bfloataa , _a ) ).values()
if any(_a ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
'''before loading those in PyTorch model.''' )
UpperCAmelCase = jax.tree_util.tree_map(
lambda _a : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , _a )
UpperCAmelCase = ''''''
UpperCAmelCase = flatten_dict(_a , sep='''.''' )
UpperCAmelCase = pt_model.state_dict()
# keep track of unexpected & missing keys
UpperCAmelCase = []
UpperCAmelCase = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
UpperCAmelCase = flax_key_tuple.split('''.''' )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
UpperCAmelCase = flax_key_tuple_array[:-1] + ['''weight''']
UpperCAmelCase = jnp.transpose(_a , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
UpperCAmelCase = flax_key_tuple_array[:-1] + ['''weight''']
UpperCAmelCase = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
UpperCAmelCase = flax_key_tuple_array[:-1] + ['''weight''']
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(_a ):
UpperCAmelCase = (
flax_key_tuple_string.replace('''_0''' , '''.0''' )
.replace('''_1''' , '''.1''' )
.replace('''_2''' , '''.2''' )
.replace('''_3''' , '''.3''' )
.replace('''_4''' , '''.4''' )
.replace('''_5''' , '''.5''' )
.replace('''_6''' , '''.6''' )
.replace('''_7''' , '''.7''' )
.replace('''_8''' , '''.8''' )
.replace('''_9''' , '''.9''' )
)
UpperCAmelCase = '''.'''.join(_a )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." )
else:
# add weight to pytorch dict
UpperCAmelCase = np.asarray(_a ) if not isinstance(_a , np.ndarray ) else flax_tensor
UpperCAmelCase = torch.from_numpy(_a )
# remove from missing keys
missing_keys.remove(_a )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(_a )
pt_model.load_state_dict(_a )
# re-transform missing_keys to list
UpperCAmelCase = list(_a )
if len(_a ) > 0:
logger.warning(
'''Some weights of the Flax model were not used when initializing the PyTorch model'''
F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
''' FlaxBertForSequenceClassification model).''' )
if len(_a ) > 0:
logger.warning(
F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
''' use it for predictions and inference.''' )
return pt_model
| 34
|
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A =logging.getLogger(__name__)
def snake_case_ (_a : Dict , _a : Union[str, Any] ):
return (preds == labels).mean()
@dataclass
class _a :
__a : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class _a :
__a : str = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
__a : str = field(metadata={"""help""": """Should contain the data files for the task."""} )
__a : int = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__a : bool = field(
default=__a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def snake_case_ ():
# 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 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , 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
set_seed(training_args.seed )
try:
UpperCAmelCase = processors[data_args.task_name]()
UpperCAmelCase = processor.get_labels()
UpperCAmelCase = len(_a )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_a , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoModelForMultipleChoice.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 , )
# Get datasets
UpperCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
UpperCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_a : EvalPrediction ) -> Dict:
UpperCAmelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(_a , p.label_ids )}
# Data collator
UpperCAmelCase = DataCollatorWithPadding(_a , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
UpperCAmelCase = Trainer(
model=_a , args=_a , train_dataset=_a , eval_dataset=_a , compute_metrics=_a , data_collator=_a , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCAmelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase = trainer.evaluate()
UpperCAmelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(_a , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , _a , _a )
writer.write('''%s = %s\n''' % (key, value) )
results.update(_a )
return results
def snake_case_ (_a : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 34
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImageProcessor"""
__a : List[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Dict , lowercase : Union[str, Any]=None , lowercase : Dict=None , **lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowercase , )
UpperCAmelCase = kwargs.pop('''feature_extractor''' )
UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowercase , lowercase )
UpperCAmelCase = self.image_processor
def __call__( self : Tuple , lowercase : Optional[Any]=None , lowercase : Union[str, Any]=None , lowercase : int=None , **lowercase : Dict ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCAmelCase = self.tokenizer(lowercase , return_tensors=lowercase , **lowercase )
if images is not None:
UpperCAmelCase = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def A ( self : int , *lowercase : Tuple , **lowercase : List[str] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A ( self : Optional[Any] , *lowercase : int , **lowercase : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer.model_input_names
UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A ( self : List[Any] ):
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowercase , )
return self.image_processor_class
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A ={
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A =logging.get_logger(__name__)
A ={
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class _a ( __a ):
__a : List[Any] = """marian"""
__a : Union[str, Any] = ["""past_key_values"""]
__a : List[str] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[Any] , lowercase : Union[str, Any]=58_101 , lowercase : Tuple=None , lowercase : str=1_024 , lowercase : Optional[int]=12 , lowercase : Optional[int]=4_096 , lowercase : int=16 , lowercase : List[Any]=12 , lowercase : int=4_096 , lowercase : Optional[int]=16 , lowercase : int=0.0 , lowercase : Tuple=0.0 , lowercase : Tuple=True , lowercase : Union[str, Any]=True , lowercase : List[Any]="gelu" , lowercase : Tuple=1_024 , lowercase : str=0.1 , lowercase : str=0.0 , lowercase : Optional[int]=0.0 , lowercase : Dict=0.02 , lowercase : Union[str, Any]=58_100 , lowercase : List[str]=False , lowercase : str=58_100 , lowercase : Any=0 , lowercase : Optional[Any]=0 , lowercase : Tuple=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = vocab_size
UpperCAmelCase = decoder_vocab_size or vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = d_model
UpperCAmelCase = encoder_ffn_dim
UpperCAmelCase = encoder_layers
UpperCAmelCase = encoder_attention_heads
UpperCAmelCase = decoder_ffn_dim
UpperCAmelCase = decoder_layers
UpperCAmelCase = decoder_attention_heads
UpperCAmelCase = dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation_dropout
UpperCAmelCase = activation_function
UpperCAmelCase = init_std
UpperCAmelCase = encoder_layerdrop
UpperCAmelCase = decoder_layerdrop
UpperCAmelCase = use_cache
UpperCAmelCase = encoder_layers
UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , forced_eos_token_id=lowercase , **lowercase , )
class _a ( __a ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A ( self : int ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase = {0: '''batch'''}
UpperCAmelCase = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A ( self : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super().outputs
else:
UpperCAmelCase = super(lowercase , self ).outputs
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A ( self : Dict , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Generate decoder inputs
UpperCAmelCase = seq_length if not self.use_past else 1
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
UpperCAmelCase = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
UpperCAmelCase = dict(**lowercase , **lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
UpperCAmelCase = common_inputs['''decoder_input_ids'''].shape[1]
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = decoder_seq_length + 3
UpperCAmelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
UpperCAmelCase = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase , lowercase )] , dim=1 )
UpperCAmelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase = min(lowercase , lowercase )
UpperCAmelCase = max(lowercase , lowercase ) - min_num_layers
UpperCAmelCase = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
) )
# TODO: test this.
UpperCAmelCase = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase , lowercase ):
common_inputs["past_key_values"].append((torch.zeros(lowercase ), torch.zeros(lowercase )) )
return common_inputs
def A ( self : int , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase = seqlen + 2
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = common_inputs['''attention_mask'''].dtype
UpperCAmelCase = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase , lowercase , dtype=lowercase )] , dim=1 )
UpperCAmelCase = [
(torch.zeros(lowercase ), torch.zeros(lowercase )) for _ in range(lowercase )
]
return common_inputs
def A ( self : str , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase = tokenizer.num_special_tokens_to_add(lowercase )
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
UpperCAmelCase = dict(tokenizer(lowercase , return_tensors=lowercase ) )
return common_inputs
def A ( self : List[str] , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
else:
UpperCAmelCase = self._generate_dummy_inputs_for_causal_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
return common_inputs
def A ( self : List[Any] , lowercase : Any , lowercase : Tuple , lowercase : Any , lowercase : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super()._flatten_past_key_values_(lowercase , lowercase , lowercase , lowercase )
else:
UpperCAmelCase = super(lowercase , self )._flatten_past_key_values_(
lowercase , lowercase , lowercase , lowercase )
@property
def A ( self : Any ):
'''simple docstring'''
return 1E-4
| 34
| 1
|
'''simple docstring'''
import logging
from transformers import PretrainedConfig
A =logging.getLogger(__name__)
A ={
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class _a ( __a ):
__a : List[Any] = """bertabs"""
def __init__( self : str , lowercase : Tuple=30_522 , lowercase : Any=512 , lowercase : int=6 , lowercase : int=512 , lowercase : Any=8 , lowercase : Tuple=512 , lowercase : List[str]=0.2 , lowercase : List[Any]=6 , lowercase : Any=768 , lowercase : List[str]=8 , lowercase : Union[str, Any]=2_048 , lowercase : Union[str, Any]=0.2 , **lowercase : List[str] , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = vocab_size
UpperCAmelCase = max_pos
UpperCAmelCase = enc_layers
UpperCAmelCase = enc_hidden_size
UpperCAmelCase = enc_heads
UpperCAmelCase = enc_ff_size
UpperCAmelCase = enc_dropout
UpperCAmelCase = dec_layers
UpperCAmelCase = dec_hidden_size
UpperCAmelCase = dec_heads
UpperCAmelCase = dec_ff_size
UpperCAmelCase = dec_dropout
| 34
|
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
print(solution())
| 34
| 1
|
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def snake_case_ (_a : str = "https://www.worldometers.info/coronavirus" ):
UpperCAmelCase = BeautifulSoup(requests.get(_a ).text , '''html.parser''' )
UpperCAmelCase = soup.findAll('''h1''' )
UpperCAmelCase = soup.findAll('''div''' , {'''class''': '''maincounter-number'''} )
keys += soup.findAll('''span''' , {'''class''': '''panel-title'''} )
values += soup.findAll('''div''' , {'''class''': '''number-table-main'''} )
return {key.text.strip(): value.text.strip() for key, value in zip(_a , _a )}
if __name__ == "__main__":
print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
for key, value in world_covidaa_stats().items():
print(f"""{key}\n{value}\n""")
| 34
|
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
A =logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
A ={
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'summarization': AutoModelForSeqaSeqLM,
'translation': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
A ={
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
A =sorted(arg_to_scheduler.keys())
A ='{' + ', '.join(arg_to_scheduler_choices) + '}'
class _a ( pl.LightningModule ):
def __init__( self : List[str] , lowercase : argparse.Namespace , lowercase : List[Any]=None , lowercase : Dict="base" , lowercase : Optional[int]=None , lowercase : Dict=None , lowercase : Tuple=None , **lowercase : Optional[int] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowercase )
UpperCAmelCase = 0
UpperCAmelCase = Path(self.hparams.output_dir )
UpperCAmelCase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
UpperCAmelCase = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'''num_labels''': num_labels} if num_labels is not None else {}) , cache_dir=lowercase , **lowercase , )
else:
UpperCAmelCase = config
UpperCAmelCase = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , lowercase , lowercase ):
assert hasattr(self.config , lowercase ), f"model config doesn't have a `{p}` attribute"
setattr(self.config , lowercase , getattr(self.hparams , lowercase ) )
if tokenizer is None:
UpperCAmelCase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowercase , )
else:
UpperCAmelCase = tokenizer
UpperCAmelCase = MODEL_MODES[mode]
if model is None:
UpperCAmelCase = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=lowercase , )
else:
UpperCAmelCase = model
def A ( self : List[Any] , *lowercase : List[str] , **lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.model_type.from_pretrained(*lowercase , **lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.hparams.lr_scheduler]
UpperCAmelCase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
UpperCAmelCase = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''params''': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'''weight_decay''': self.hparams.weight_decay,
},
{
'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
if self.hparams.adafactor:
UpperCAmelCase = Adafactor(
lowercase , lr=self.hparams.learning_rate , scale_parameter=lowercase , relative_step=lowercase )
else:
UpperCAmelCase = AdamW(
lowercase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
UpperCAmelCase = optimizer
UpperCAmelCase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A ( self : List[Any] , lowercase : int , lowercase : List[str] ):
'''simple docstring'''
return self.validation_step(lowercase , lowercase )
def A ( self : List[Any] , lowercase : Tuple ):
'''simple docstring'''
return self.validation_end(lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
UpperCAmelCase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A ( self : List[str] , lowercase : Any ):
'''simple docstring'''
if stage == "test":
UpperCAmelCase = len(self.test_dataloader().dataset )
else:
UpperCAmelCase = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=lowercase )
UpperCAmelCase = len(self.train_dataloader().dataset )
def A ( self : List[str] , lowercase : str , lowercase : int , lowercase : bool = False ):
'''simple docstring'''
raise NotImplementedError('''You must implement this for your task''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
return self.train_loader
def A ( self : Optional[Any] ):
'''simple docstring'''
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
lowercase , list(filter(lowercase , self.hparams.model_name_or_path.split('''/''' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A ( self : List[str] , lowercase : Dict[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.output_dir.joinpath('''best_tfmr''' )
UpperCAmelCase = self.step_count
self.model.save_pretrained(lowercase )
self.tokenizer.save_pretrained(lowercase )
@staticmethod
def A ( lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
parser.add_argument(
'''--model_name_or_path''' , default=lowercase , type=lowercase , required=lowercase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=lowercase , help='''Pretrained config name or path if not the same as model_name''' )
parser.add_argument(
'''--tokenizer_name''' , default=lowercase , type=lowercase , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(lowercase ).parent / '''test_run''' / '''cache''' ) , type=lowercase , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=lowercase , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=lowercase , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=lowercase , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=lowercase , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5E-5 , type=lowercase , help='''The initial learning rate for Adam.''' )
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=lowercase , metavar=lowercase , type=lowercase , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=lowercase , help='''Weight decay if we apply some.''' )
parser.add_argument('''--adam_epsilon''' , default=1E-8 , type=lowercase , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--warmup_steps''' , default=0 , type=lowercase , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--num_workers''' , default=4 , type=lowercase , help='''kwarg passed to DataLoader''' )
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=lowercase )
parser.add_argument('''--train_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--eval_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--adafactor''' , action='''store_true''' )
class _a ( pl.Callback ):
def A ( self : Dict , lowercase : Optional[Any] , lowercase : List[Any] ):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Union[str, Any] , lowercase : Any ):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowercase )
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Optional[int] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = trainer.lr_schedulers[0]['''scheduler''']
UpperCAmelCase = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowercase )
def A ( self : Tuple , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Validation results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log results
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def A ( self : Dict , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Test results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log and save results to file
UpperCAmelCase = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''' )
with open(lowercase , '''w''' ) as writer:
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
writer.write('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def snake_case_ (_a : int , _a : Optional[Any] ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'''--output_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_a , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument(
'''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , )
parser.add_argument(
'''--fp16_opt_level''' , type=_a , default='''O2''' , help=(
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
'''See details at https://nvidia.github.io/apex/amp.html'''
) , )
parser.add_argument('''--n_tpu_cores''' , dest='''tpu_cores''' , type=_a )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_a , help='''Max gradient norm''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_predict''' , action='''store_true''' , help='''Whether to run predictions on the test set.''' )
parser.add_argument(
'''--gradient_accumulation_steps''' , dest='''accumulate_grad_batches''' , type=_a , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_a , default=4_2 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_a , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def snake_case_ (_a : BaseTransformer , _a : argparse.Namespace , _a : List[Any]=None , _a : Tuple=True , _a : int=[] , _a : Any=None , _a : int=None , **_a : Optional[Any] , ):
pl.seed_everything(args.seed )
# init model
UpperCAmelCase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_a )
# add custom checkpoints
if checkpoint_callback is None:
UpperCAmelCase = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='''checkpoint''' , monitor='''val_loss''' , mode='''min''' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_a )
if logging_callback is None:
UpperCAmelCase = LoggingCallback()
UpperCAmelCase = {}
if args.fpaa:
UpperCAmelCase = 1_6
if args.gpus > 1:
UpperCAmelCase = '''auto'''
UpperCAmelCase = '''ddp'''
UpperCAmelCase = args.accumulate_grad_batches
UpperCAmelCase = None
UpperCAmelCase = '''auto'''
UpperCAmelCase = pl.Trainer.from_argparse_args(
_a , weights_summary=_a , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_a , val_check_interval=1 , num_sanity_val_steps=2 , **_a , )
if args.do_train:
trainer.fit(_a )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 34
| 1
|
'''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 snake_case_ (_a : Dict , _a : Tuple ):
# Load checkpoint
UpperCAmelCase = torch.load(_a , map_location='''cpu''' )
UpperCAmelCase = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
UpperCAmelCase = {}
for k, v in state_dict.items():
if "pred_layer" in k:
UpperCAmelCase = v
else:
UpperCAmelCase = v
UpperCAmelCase = chkpt['''params''']
UpperCAmelCase = {n: v for n, v in config.items() if not isinstance(_a , (torch.FloatTensor, numpy.ndarray) )}
UpperCAmelCase = chkpt['''dico_word2id''']
UpperCAmelCase = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 1_3 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
UpperCAmelCase = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
UpperCAmelCase = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
UpperCAmelCase = 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__":
A =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.'
)
A =parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 34
|
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : dict , _a : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
UpperCAmelCase = cst_fwd.get(_a , np.inf )
UpperCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
UpperCAmelCase = new_cost_f
UpperCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
UpperCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def snake_case_ (_a : str , _a : str , _a : dict , _a : dict ):
UpperCAmelCase = -1
UpperCAmelCase = set()
UpperCAmelCase = set()
UpperCAmelCase = {source: 0}
UpperCAmelCase = {destination: 0}
UpperCAmelCase = {source: None}
UpperCAmelCase = {destination: None}
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
UpperCAmelCase , UpperCAmelCase = queue_forward.get()
visited_forward.add(_a )
UpperCAmelCase , UpperCAmelCase = queue_backward.get()
visited_backward.add(_a )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
UpperCAmelCase = shortest_distance
return shortest_path_distance
A ={
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
A ={
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class _a :
def __init__( self : List[str] , lowercase : Optional[Any] , lowercase : Union[str, Any]=13 , lowercase : Optional[int]=7 , lowercase : Optional[int]=True , lowercase : Tuple=True , lowercase : Dict=True , lowercase : str=True , lowercase : int=99 , lowercase : List[Any]=32 , lowercase : Optional[Any]=5 , lowercase : str=4 , lowercase : str=4 , lowercase : List[str]="gelu" , lowercase : Tuple=0.0 , lowercase : List[Any]=0.1 , lowercase : str=True , lowercase : List[Any]=512 , lowercase : Dict=16 , lowercase : Tuple=2 , lowercase : Tuple=0.02 , lowercase : List[str]=3 , lowercase : List[str]=4 , lowercase : Any=None , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_input_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_multiple_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = weight_tying
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = scope
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase = self.get_config()
return config, input_ids, input_mask, token_labels
def A ( self : List[str] ):
'''simple docstring'''
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase , initializer_range=self.initializer_range , )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase = True
return config, input_ids, input_mask, token_labels
def A ( self : Optional[Any] , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = GPTNeoXJapaneseModel(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase )
UpperCAmelCase = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Tuple , lowercase : List[Any] , lowercase : str , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = True
UpperCAmelCase = GPTNeoXJapaneseModel(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Any , lowercase : Optional[Any] , lowercase : Union[str, Any] , lowercase : str , lowercase : Any ):
'''simple docstring'''
UpperCAmelCase = GPTNeoXJapaneseForCausalLM(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Tuple , lowercase : Dict , lowercase : Tuple , lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = True
UpperCAmelCase = GPTNeoXJapaneseForCausalLM(config=lowercase )
model.to(lowercase )
model.eval()
# first forward pass
UpperCAmelCase = model(lowercase , attention_mask=lowercase , use_cache=lowercase )
UpperCAmelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCAmelCase = model(lowercase , attention_mask=lowercase , output_hidden_states=lowercase )
UpperCAmelCase = output_from_no_past['''hidden_states'''][0]
UpperCAmelCase = model(
lowercase , attention_mask=lowercase , past_key_values=lowercase , output_hidden_states=lowercase , )['''hidden_states'''][0]
# select random slice
UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowercase , lowercase , atol=1E-3 ) )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _a ( __a , __a , unittest.TestCase ):
__a : List[Any] = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
__a : Tuple = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
__a : Optional[Any] = (
{"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
__a : Union[str, Any] = False
__a : Optional[int] = False
__a : List[Any] = False
__a : Dict = False
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = GPTNeoXJapaneseModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase , hidden_size=37 )
def A ( self : Optional[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowercase , lowercase , lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowercase , lowercase , lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCAmelCase = None
self.model_tester.create_and_check_model_as_decoder(lowercase , lowercase , lowercase )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase , lowercase , lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowercase )
@slow
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = '''abeja/gpt-neox-japanese-2.7b'''
UpperCAmelCase = ['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']
UpperCAmelCase = [
'''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
'''100年後に必要とされる会社は、「人」が中心の会社です。''',
'''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
'''国境の長いトンネルを抜けると、そこは雪国だった。''',
'''美味しい日本食といえば、やっぱりお寿司ですよね。''',
]
UpperCAmelCase = GPTNeoXJapaneseTokenizer.from_pretrained(lowercase )
UpperCAmelCase = GPTNeoXJapaneseForCausalLM.from_pretrained(lowercase )
UpperCAmelCase = []
for prompt in prompts:
UpperCAmelCase = tokenizer(lowercase , return_tensors='''pt''' ).input_ids
UpperCAmelCase = model.generate(lowercase , max_length=50 )
UpperCAmelCase = tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
predicted_outputs += generated_string
self.assertListEqual(lowercase , lowercase )
| 34
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
def snake_case_ (_a : List[str] ):
UpperCAmelCase = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
UpperCAmelCase = 1_2_8
elif "12-12" in model_name:
UpperCAmelCase = 1_2
UpperCAmelCase = 1_2
elif "14-14" in model_name:
UpperCAmelCase = 1_4
UpperCAmelCase = 1_4
elif "16-16" in model_name:
UpperCAmelCase = 1_6
UpperCAmelCase = 1_6
else:
raise ValueError('''Model not supported''' )
UpperCAmelCase = '''huggingface/label-files'''
if "speech-commands" in model_name:
UpperCAmelCase = 3_5
UpperCAmelCase = '''speech-commands-v2-id2label.json'''
else:
UpperCAmelCase = 5_2_7
UpperCAmelCase = '''audioset-id2label.json'''
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ (_a : Tuple ):
if "module.v" in name:
UpperCAmelCase = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
UpperCAmelCase = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
UpperCAmelCase = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
UpperCAmelCase = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
UpperCAmelCase = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
UpperCAmelCase = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
UpperCAmelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
UpperCAmelCase = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
UpperCAmelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
UpperCAmelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
UpperCAmelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
UpperCAmelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
UpperCAmelCase = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
UpperCAmelCase = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
UpperCAmelCase = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def snake_case_ (_a : Dict , _a : List[Any] ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(_a )
if "qkv" in key:
UpperCAmelCase = key.split('''.''' )
UpperCAmelCase = int(key_split[3] )
UpperCAmelCase = config.hidden_size
if "weight" in key:
UpperCAmelCase = val[:dim, :]
UpperCAmelCase = val[dim : dim * 2, :]
UpperCAmelCase = val[-dim:, :]
else:
UpperCAmelCase = val[:dim]
UpperCAmelCase = val[dim : dim * 2]
UpperCAmelCase = val[-dim:]
else:
UpperCAmelCase = val
return orig_state_dict
def snake_case_ (_a : Tuple ):
UpperCAmelCase = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(_a , _a )
@torch.no_grad()
def snake_case_ (_a : int , _a : Union[str, Any] , _a : Dict=False ):
UpperCAmelCase = get_audio_spectrogram_transformer_config(_a )
UpperCAmelCase = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
UpperCAmelCase = model_name_to_url[model_name]
UpperCAmelCase = torch.hub.load_state_dict_from_url(_a , map_location='''cpu''' )
# remove some keys
remove_keys(_a )
# rename some keys
UpperCAmelCase = convert_state_dict(_a , _a )
# load 🤗 model
UpperCAmelCase = ASTForAudioClassification(_a )
model.eval()
model.load_state_dict(_a )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
UpperCAmelCase = -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
UpperCAmelCase = 4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
UpperCAmelCase = 1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
UpperCAmelCase = ASTFeatureExtractor(mean=_a , std=_a , max_length=_a )
if "speech-commands" in model_name:
UpperCAmelCase = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
UpperCAmelCase = dataset[0]['''audio''']['''array''']
else:
UpperCAmelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
UpperCAmelCase , UpperCAmelCase = torchaudio.load(_a )
UpperCAmelCase = waveform.squeeze().numpy()
UpperCAmelCase = feature_extractor(_a , sampling_rate=1_6_0_0_0 , return_tensors='''pt''' )
# forward pass
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
UpperCAmelCase = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
UpperCAmelCase = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
UpperCAmelCase = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
UpperCAmelCase = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
UpperCAmelCase = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
UpperCAmelCase = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
UpperCAmelCase = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
UpperCAmelCase = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , _a , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(F"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_a )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(F"MIT/{model_name}" )
feature_extractor.push_to_hub(F"MIT/{model_name}" )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub.'
)
A =parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class _a ( unittest.TestCase ):
def __init__( self : int , lowercase : Union[str, Any] , lowercase : Dict=7 , lowercase : Union[str, Any]=3 , lowercase : int=30 , lowercase : List[str]=400 , lowercase : List[Any]=True , lowercase : Union[str, Any]=None , lowercase : Union[str, Any]=True , lowercase : Any=[0.5, 0.5, 0.5] , lowercase : int=[0.5, 0.5, 0.5] , lowercase : List[Any]=True , lowercase : Tuple=1 / 255 , lowercase : List[str]=True , ):
'''simple docstring'''
UpperCAmelCase = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = num_channels
UpperCAmelCase = min_resolution
UpperCAmelCase = max_resolution
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean
UpperCAmelCase = image_std
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_pad
def A ( self : List[str] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def A ( self : Union[str, Any] , lowercase : Dict , lowercase : Optional[Any]=False ):
'''simple docstring'''
if not batched:
UpperCAmelCase = image_inputs[0]
if isinstance(lowercase , Image.Image ):
UpperCAmelCase , UpperCAmelCase = image.size
else:
UpperCAmelCase , UpperCAmelCase = image.shape[1], image.shape[2]
if w < h:
UpperCAmelCase = int(self.size['''shortest_edge'''] * h / w )
UpperCAmelCase = self.size['''shortest_edge''']
elif w > h:
UpperCAmelCase = self.size['''shortest_edge''']
UpperCAmelCase = int(self.size['''shortest_edge'''] * w / h )
else:
UpperCAmelCase = self.size['''shortest_edge''']
UpperCAmelCase = self.size['''shortest_edge''']
else:
UpperCAmelCase = []
for image in image_inputs:
UpperCAmelCase , UpperCAmelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCAmelCase = max(lowercase , key=lambda lowercase : item[0] )[0]
UpperCAmelCase = max(lowercase , key=lambda lowercase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _a ( __a , unittest.TestCase ):
__a : List[Any] = ConditionalDetrImageProcessor if is_vision_available() else None
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = ConditionalDetrImageProcessingTester(self )
@property
def A ( self : int ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase , '''image_mean''' ) )
self.assertTrue(hasattr(lowercase , '''image_std''' ) )
self.assertTrue(hasattr(lowercase , '''do_normalize''' ) )
self.assertTrue(hasattr(lowercase , '''do_resize''' ) )
self.assertTrue(hasattr(lowercase , '''size''' ) )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1_333} )
self.assertEqual(image_processor.do_pad , lowercase )
UpperCAmelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowercase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , lowercase )
def A ( self : Union[str, Any] ):
'''simple docstring'''
pass
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , Image.Image )
# Test not batched input
UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCAmelCase , UpperCAmelCase = self.image_processor_tester.get_expected_values(lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase , UpperCAmelCase = self.image_processor_tester.get_expected_values(lowercase , batched=lowercase )
UpperCAmelCase = image_processing(lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase , numpify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , np.ndarray )
# Test not batched input
UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCAmelCase , UpperCAmelCase = self.image_processor_tester.get_expected_values(lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase = image_processing(lowercase , return_tensors='''pt''' ).pixel_values
UpperCAmelCase , UpperCAmelCase = self.image_processor_tester.get_expected_values(lowercase , batched=lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase , torchify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , torch.Tensor )
# Test not batched input
UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCAmelCase , UpperCAmelCase = self.image_processor_tester.get_expected_values(lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase = image_processing(lowercase , return_tensors='''pt''' ).pixel_values
UpperCAmelCase , UpperCAmelCase = self.image_processor_tester.get_expected_values(lowercase , batched=lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
UpperCAmelCase = json.loads(f.read() )
UpperCAmelCase = {'''image_id''': 39_769, '''annotations''': target}
# encode them
UpperCAmelCase = ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''' )
UpperCAmelCase = image_processing(images=lowercase , annotations=lowercase , return_tensors='''pt''' )
# verify pixel values
UpperCAmelCase = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['''pixel_values'''].shape , lowercase )
UpperCAmelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , lowercase , atol=1E-4 ) )
# verify area
UpperCAmelCase = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , lowercase ) )
# verify boxes
UpperCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , lowercase )
UpperCAmelCase = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , lowercase , atol=1E-3 ) )
# verify image_id
UpperCAmelCase = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , lowercase ) )
# verify is_crowd
UpperCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , lowercase ) )
# verify class_labels
UpperCAmelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , lowercase ) )
# verify orig_size
UpperCAmelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , lowercase ) )
# verify size
UpperCAmelCase = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , lowercase ) )
@slow
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
UpperCAmelCase = json.loads(f.read() )
UpperCAmelCase = {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}
UpperCAmelCase = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
UpperCAmelCase = ConditionalDetrImageProcessor(format='''coco_panoptic''' )
UpperCAmelCase = image_processing(images=lowercase , annotations=lowercase , masks_path=lowercase , return_tensors='''pt''' )
# verify pixel values
UpperCAmelCase = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['''pixel_values'''].shape , lowercase )
UpperCAmelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , lowercase , atol=1E-4 ) )
# verify area
UpperCAmelCase = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , lowercase ) )
# verify boxes
UpperCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , lowercase )
UpperCAmelCase = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , lowercase , atol=1E-3 ) )
# verify image_id
UpperCAmelCase = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , lowercase ) )
# verify is_crowd
UpperCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , lowercase ) )
# verify class_labels
UpperCAmelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , lowercase ) )
# verify masks
UpperCAmelCase = 822_873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , lowercase )
# verify orig_size
UpperCAmelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , lowercase ) )
# verify size
UpperCAmelCase = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , lowercase ) )
| 34
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
UpperCAmelCase = m
else:
UpperCAmelCase = m # noqa: E741
return r
def snake_case_ (_a : list[int] ):
if len(_a ) == 0:
return 0
UpperCAmelCase = [0] * len(_a )
UpperCAmelCase = 1
UpperCAmelCase = v[0]
for i in range(1 , len(_a ) ):
if v[i] < tail[0]:
UpperCAmelCase = v[i]
elif v[i] > tail[length - 1]:
UpperCAmelCase = v[i]
length += 1
else:
UpperCAmelCase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : dict , _a : str ):
UpperCAmelCase , UpperCAmelCase = set(_a ), [start]
while stack:
UpperCAmelCase = stack.pop()
explored.add(_a )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(_a )
return explored
A ={
'A': ['B', 'C', 'D'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B', 'D'],
'E': ['B', 'F'],
'F': ['C', 'E', 'G'],
'G': ['F'],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, 'A'))
| 34
|
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase = [[0 for i in range(_a )] for j in range(_a )]
# since string of zero length match pattern of zero length
UpperCAmelCase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _a ):
UpperCAmelCase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _a ):
UpperCAmelCase = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _a ):
for j in range(1 , _a ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase = dp[i - 1][j]
else:
UpperCAmelCase = 0
else:
UpperCAmelCase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A ='aab'
A ='c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"""{input_string} matches the given pattern {pattern}""")
else:
print(f"""{input_string} does not match with the given pattern {pattern}""")
| 34
| 1
|
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
A =[
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
A ='UperNetConfig'
class _a ( nn.Module ):
def __init__( self : Union[str, Any] , lowercase : int , lowercase : int , lowercase : Union[int, Tuple[int, int]] , lowercase : Union[int, Tuple[int, int], str] = 0 , lowercase : bool = False , lowercase : Union[int, Tuple[int, int]] = 1 , ):
'''simple docstring'''
super().__init__()
UpperCAmelCase = nn.Convad(
in_channels=lowercase , out_channels=lowercase , kernel_size=lowercase , padding=lowercase , bias=lowercase , dilation=lowercase , )
UpperCAmelCase = nn.BatchNormad(lowercase )
UpperCAmelCase = nn.ReLU()
def A ( self : Dict , lowercase : torch.Tensor ):
'''simple docstring'''
UpperCAmelCase = self.conv(lowercase )
UpperCAmelCase = self.batch_norm(lowercase )
UpperCAmelCase = self.activation(lowercase )
return output
class _a ( nn.Module ):
def __init__( self : Dict , lowercase : int , lowercase : int , lowercase : int ):
'''simple docstring'''
super().__init__()
UpperCAmelCase = [
nn.AdaptiveAvgPoolad(lowercase ),
UperNetConvModule(lowercase , lowercase , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(lowercase ) , lowercase )
def A ( self : Any , lowercase : torch.Tensor ):
'''simple docstring'''
UpperCAmelCase = input
for layer in self.layers:
UpperCAmelCase = layer(lowercase )
return hidden_state
class _a ( nn.Module ):
def __init__( self : List[Any] , lowercase : Tuple[int, ...] , lowercase : int , lowercase : int , lowercase : bool ):
'''simple docstring'''
super().__init__()
UpperCAmelCase = pool_scales
UpperCAmelCase = align_corners
UpperCAmelCase = in_channels
UpperCAmelCase = channels
UpperCAmelCase = []
for i, pool_scale in enumerate(lowercase ):
UpperCAmelCase = UperNetPyramidPoolingBlock(pool_scale=lowercase , in_channels=lowercase , channels=lowercase )
self.blocks.append(lowercase )
self.add_module(str(lowercase ) , lowercase )
def A ( self : Union[str, Any] , lowercase : torch.Tensor ):
'''simple docstring'''
UpperCAmelCase = []
for ppm in self.blocks:
UpperCAmelCase = ppm(lowercase )
UpperCAmelCase = nn.functional.interpolate(
lowercase , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(lowercase )
return ppm_outs
class _a ( nn.Module ):
def __init__( self : Union[str, Any] , lowercase : List[Any] , lowercase : Dict ):
'''simple docstring'''
super().__init__()
UpperCAmelCase = config
UpperCAmelCase = config.pool_scales # e.g. (1, 2, 3, 6)
UpperCAmelCase = in_channels
UpperCAmelCase = config.hidden_size
UpperCAmelCase = False
UpperCAmelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
UpperCAmelCase = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
UpperCAmelCase = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
UpperCAmelCase = nn.ModuleList()
UpperCAmelCase = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
UpperCAmelCase = UperNetConvModule(lowercase , self.channels , kernel_size=1 )
UpperCAmelCase = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(lowercase )
self.fpn_convs.append(lowercase )
UpperCAmelCase = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def A ( self : Optional[int] ):
'''simple docstring'''
self.apply(self._init_weights )
def A ( self : List[Any] , lowercase : Optional[int] ):
'''simple docstring'''
if isinstance(lowercase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def A ( self : str , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = inputs[-1]
UpperCAmelCase = [x]
psp_outs.extend(self.psp_modules(lowercase ) )
UpperCAmelCase = torch.cat(lowercase , dim=1 )
UpperCAmelCase = self.bottleneck(lowercase )
return output
def A ( self : Optional[int] , lowercase : torch.Tensor ):
'''simple docstring'''
UpperCAmelCase = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(lowercase ) )
# build top-down path
UpperCAmelCase = len(lowercase )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCAmelCase = laterals[i - 1].shape[2:]
UpperCAmelCase = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=lowercase , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
UpperCAmelCase = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCAmelCase = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
UpperCAmelCase = torch.cat(lowercase , dim=1 )
UpperCAmelCase = self.fpn_bottleneck(lowercase )
UpperCAmelCase = self.classifier(lowercase )
return output
class _a ( nn.Module ):
def __init__( self : Optional[Any] , lowercase : List[Any] , lowercase : int = 2 , lowercase : int = 3 , lowercase : Union[int, Tuple[int, int]] = 1 ):
'''simple docstring'''
super().__init__()
UpperCAmelCase = config
UpperCAmelCase = config.auxiliary_in_channels
UpperCAmelCase = config.auxiliary_channels
UpperCAmelCase = config.auxiliary_num_convs
UpperCAmelCase = config.auxiliary_concat_input
UpperCAmelCase = in_index
UpperCAmelCase = (kernel_size // 2) * dilation
UpperCAmelCase = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=lowercase , padding=lowercase , dilation=lowercase ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=lowercase , padding=lowercase , dilation=lowercase ) )
if self.num_convs == 0:
UpperCAmelCase = nn.Identity()
else:
UpperCAmelCase = nn.Sequential(*lowercase )
if self.concat_input:
UpperCAmelCase = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=lowercase , padding=kernel_size // 2 )
UpperCAmelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def A ( self : List[str] ):
'''simple docstring'''
self.apply(self._init_weights )
def A ( self : List[Any] , lowercase : List[str] ):
'''simple docstring'''
if isinstance(lowercase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def A ( self : Any , lowercase : torch.Tensor ):
'''simple docstring'''
UpperCAmelCase = encoder_hidden_states[self.in_index]
UpperCAmelCase = self.convs(lowercase )
if self.concat_input:
UpperCAmelCase = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
UpperCAmelCase = self.classifier(lowercase )
return output
class _a ( __a ):
__a : int = UperNetConfig
__a : Optional[int] = """pixel_values"""
__a : Optional[int] = True
def A ( self : int , lowercase : str ):
'''simple docstring'''
if isinstance(lowercase , lowercase ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def A ( self : List[Any] ):
'''simple docstring'''
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def A ( self : int , lowercase : Any , lowercase : List[str]=False ):
'''simple docstring'''
if isinstance(lowercase , lowercase ):
UpperCAmelCase = value
A =r'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
A =r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"""UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.""" , __a , )
class _a ( __a ):
def __init__( self : List[Any] , lowercase : Dict ):
'''simple docstring'''
super().__init__(lowercase )
UpperCAmelCase = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
UpperCAmelCase = UperNetHead(lowercase , in_channels=self.backbone.channels )
UpperCAmelCase = UperNetFCNHead(lowercase ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=lowercase , config_class=_CONFIG_FOR_DOC )
def A ( self : Any , lowercase : Optional[torch.Tensor] = None , lowercase : Optional[bool] = None , lowercase : Optional[bool] = None , lowercase : Optional[torch.Tensor] = None , lowercase : Optional[bool] = None , ):
'''simple docstring'''
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase = output_attentions if output_attentions is not None else self.config.output_attentions
UpperCAmelCase = self.backbone.forward_with_filtered_kwargs(
lowercase , output_hidden_states=lowercase , output_attentions=lowercase )
UpperCAmelCase = outputs.feature_maps
UpperCAmelCase = self.decode_head(lowercase )
UpperCAmelCase = nn.functional.interpolate(lowercase , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=lowercase )
UpperCAmelCase = None
if self.auxiliary_head is not None:
UpperCAmelCase = self.auxiliary_head(lowercase )
UpperCAmelCase = nn.functional.interpolate(
lowercase , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=lowercase )
UpperCAmelCase = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
UpperCAmelCase = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
UpperCAmelCase = loss_fct(lowercase , lowercase )
UpperCAmelCase = loss_fct(lowercase , lowercase )
UpperCAmelCase = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
UpperCAmelCase = (logits,) + outputs[1:]
else:
UpperCAmelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=lowercase , logits=lowercase , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 34
|
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A ='pt'
elif is_tf_available():
A ='tf'
else:
A ='jax'
class _a ( __a , unittest.TestCase ):
__a : Optional[Any] = PerceiverTokenizer
__a : str = False
def A ( self : Union[str, Any] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A ( self : Optional[int] ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def A ( self : Union[str, Any] , **lowercase : int ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[str]=False , lowercase : Union[str, Any]=20 , lowercase : Union[str, Any]=5 ):
'''simple docstring'''
UpperCAmelCase = []
for i in range(len(lowercase ) ):
try:
UpperCAmelCase = tokenizer.decode([i] , clean_up_tokenization_spaces=lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
UpperCAmelCase = list(filter(lambda lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , lowercase ) )
UpperCAmelCase = list(filter(lambda lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=lowercase ) , lowercase ) )
if max_length is not None and len(lowercase ) > max_length:
UpperCAmelCase = toks[:max_length]
if min_length is not None and len(lowercase ) < min_length and len(lowercase ) > 0:
while len(lowercase ) < min_length:
UpperCAmelCase = toks + toks
# toks_str = [t[1] for t in toks]
UpperCAmelCase = [t[0] for t in toks]
# Ensure consistency
UpperCAmelCase = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
if " " not in output_txt and len(lowercase ) > 1:
UpperCAmelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=lowercase )
)
if with_prefix_space:
UpperCAmelCase = ''' ''' + output_txt
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
return output_txt, output_ids
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = '''Unicode €.'''
UpperCAmelCase = tokenizer(lowercase )
UpperCAmelCase = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]Unicode €.[SEP]''' )
UpperCAmelCase = tokenizer('''e è é ê ë''' )
UpperCAmelCase = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
UpperCAmelCase = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
self.assertIsInstance(lowercase , lowercase )
if FRAMEWORK != "jax":
UpperCAmelCase = list(batch.input_ids.numpy()[0] )
else:
UpperCAmelCase = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowercase , lowercase )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , lowercase )
self.assertIn('''attention_mask''' , lowercase )
self.assertNotIn('''decoder_input_ids''' , lowercase )
self.assertNotIn('''decoder_attention_mask''' , lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = [
'''Summary of the text.''',
'''Another summary.''',
]
UpperCAmelCase = tokenizer(
text_target=lowercase , max_length=32 , padding='''max_length''' , truncation=lowercase , return_tensors=lowercase )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
shutil.rmtree(lowercase )
UpperCAmelCase = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
UpperCAmelCase = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowercase )
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
UpperCAmelCase = [f"<extra_id_{i}>" for i in range(125 )]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['''an_additional_special_token'''] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
UpperCAmelCase = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=lowercase )]
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , additional_special_tokens=lowercase , )
self.assertIn('''a_new_additional_special_token''' , tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ) , )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ) , '''�''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
pass
def A ( self : Any ):
'''simple docstring'''
pass
def A ( self : Dict ):
'''simple docstring'''
pass
def A ( self : str ):
'''simple docstring'''
pass
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers(fast=lowercase , do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
UpperCAmelCase = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
UpperCAmelCase = tokenizer.convert_tokens_to_string(lowercase )
self.assertIsInstance(lowercase , lowercase )
| 34
| 1
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : list , _a : int | None = None , _a : int | None = None ):
if start is None:
UpperCAmelCase = 0
if end is None:
UpperCAmelCase = len(_a ) - 1
if start >= end:
return
UpperCAmelCase = (start + end) // 2
slowsort(_a , _a , _a )
slowsort(_a , mid + 1 , _a )
if sequence[end] < sequence[mid]:
UpperCAmelCase , UpperCAmelCase = sequence[mid], sequence[end]
slowsort(_a , _a , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 34
|
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return val
return default
def snake_case_ (_a : Dict , _a : Any=False ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return strtobool(_a ) == 1 # As its name indicates `strtobool` actually returns an int...
def snake_case_ (_a : str , _a : Optional[Any]="no" ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return value
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A ={
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
'processing_mctct': ['MCTCTProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MCTCTForCTC',
'MCTCTModel',
'MCTCTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A =logging.getLogger(__name__)
A ='Hello world! cécé herlolip'
A =namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def snake_case_ (_a : List[Any] , _a : Any ):
UpperCAmelCase = BertAbsConfig(
temp_dir='''.''' , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder='''bert''' , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
UpperCAmelCase = torch.load(_a , lambda _a , _a : storage )
UpperCAmelCase = AbsSummarizer(_a , torch.device('''cpu''' ) , _a )
original.eval()
UpperCAmelCase = BertAbsSummarizer(_a , torch.device('''cpu''' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('''convert the model''' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('''Make sure that the models\' outputs are identical''' )
UpperCAmelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
# prepare the model inputs
UpperCAmelCase = tokenizer.encode('''This is sample éàalj\'-.''' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
UpperCAmelCase = tokenizer.encode('''This is sample 3 éàalj\'-.''' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
UpperCAmelCase = encoder_input_ids
UpperCAmelCase = decoder_input_ids
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
UpperCAmelCase = original(_a , _a , _a , _a , _a , _a , _a )[0]
UpperCAmelCase = original.generator(_a )
UpperCAmelCase = new_model(
_a , _a , _a , _a , _a )[0]
UpperCAmelCase = new_model.generator(_a )
UpperCAmelCase = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.allclose(_a , _a , atol=1E-3 )
if are_identical:
logging.info('''all weights are equal up to 1e-3''' )
else:
raise ValueError('''the weights are different. The new model is likely different from the original one.''' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('''saving the model\'s state dictionary''' )
torch.save(
new_model.state_dict() , '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''' )
if __name__ == "__main__":
A =argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
A =parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 34
| 1
|
'''simple docstring'''
class _a :
def __init__( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = ''''''
UpperCAmelCase = ''''''
UpperCAmelCase = []
def A ( self : List[Any] , lowercase : int , lowercase : int ):
'''simple docstring'''
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
UpperCAmelCase = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
UpperCAmelCase = self.__min_dist_top_down_dp(lowercase , n - 1 )
UpperCAmelCase = self.__min_dist_top_down_dp(m - 1 , lowercase )
UpperCAmelCase = self.__min_dist_top_down_dp(m - 1 , n - 1 )
UpperCAmelCase = 1 + min(lowercase , lowercase , lowercase )
return self.dp[m][n]
def A ( self : Optional[int] , lowercase : str , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = worda
UpperCAmelCase = worda
UpperCAmelCase = [[-1 for _ in range(len(lowercase ) )] for _ in range(len(lowercase ) )]
return self.__min_dist_top_down_dp(len(lowercase ) - 1 , len(lowercase ) - 1 )
def A ( self : Union[str, Any] , lowercase : str , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = worda
UpperCAmelCase = worda
UpperCAmelCase = len(lowercase )
UpperCAmelCase = len(lowercase )
UpperCAmelCase = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
UpperCAmelCase = j
elif j == 0: # second string is empty
UpperCAmelCase = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
UpperCAmelCase = self.dp[i - 1][j - 1]
else:
UpperCAmelCase = self.dp[i][j - 1]
UpperCAmelCase = self.dp[i - 1][j]
UpperCAmelCase = self.dp[i - 1][j - 1]
UpperCAmelCase = 1 + min(lowercase , lowercase , lowercase )
return self.dp[m][n]
if __name__ == "__main__":
A =EditDistance()
print('****************** Testing Edit Distance DP Algorithm ******************')
print()
A =input('Enter the first string: ').strip()
A =input('Enter the second string: ').strip()
print()
print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
print()
print('*************** End of Testing Edit Distance DP Algorithm ***************')
| 34
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : List[Any] , **lowercase : List[str] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : int ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : int , *lowercase : Tuple , **lowercase : Dict ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : List[str] , *lowercase : Optional[int] , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Union[str, Any] , **lowercase : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[int] , *lowercase : Union[str, Any] , **lowercase : Any ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Any , *lowercase : Dict , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : Any = ["""flax""", """transformers"""]
def __init__( self : Any , *lowercase : Optional[Any] , **lowercase : Optional[int] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Optional[Any] , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : str , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
| 34
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _a ( __a ):
__a : Optional[int] = """gptj"""
__a : List[str] = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : int , lowercase : str=50_400 , lowercase : Optional[Any]=2_048 , lowercase : Dict=4_096 , lowercase : int=28 , lowercase : Optional[Any]=16 , lowercase : Union[str, Any]=64 , lowercase : Optional[int]=None , lowercase : List[str]="gelu_new" , lowercase : Optional[int]=0.0 , lowercase : Tuple=0.0 , lowercase : Dict=0.0 , lowercase : int=1E-5 , lowercase : List[str]=0.02 , lowercase : Optional[Any]=True , lowercase : Any=50_256 , lowercase : Tuple=50_256 , lowercase : Optional[int]=False , **lowercase : Dict , ):
'''simple docstring'''
UpperCAmelCase = vocab_size
UpperCAmelCase = n_positions
UpperCAmelCase = n_embd
UpperCAmelCase = n_layer
UpperCAmelCase = n_head
UpperCAmelCase = n_inner
UpperCAmelCase = rotary_dim
UpperCAmelCase = activation_function
UpperCAmelCase = resid_pdrop
UpperCAmelCase = embd_pdrop
UpperCAmelCase = attn_pdrop
UpperCAmelCase = layer_norm_epsilon
UpperCAmelCase = initializer_range
UpperCAmelCase = use_cache
UpperCAmelCase = bos_token_id
UpperCAmelCase = eos_token_id
super().__init__(
bos_token_id=lowercase , eos_token_id=lowercase , tie_word_embeddings=lowercase , **lowercase )
class _a ( __a ):
def __init__( self : Any , lowercase : PretrainedConfig , lowercase : str = "default" , lowercase : List[PatchingSpec] = None , lowercase : bool = False , ):
'''simple docstring'''
super().__init__(lowercase , task=lowercase , patching_specs=lowercase , use_past=lowercase )
if not getattr(self._config , '''pad_token_id''' , lowercase ):
# TODO: how to do that better?
UpperCAmelCase = 0
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(lowercase , direction='''inputs''' )
UpperCAmelCase = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def A ( self : str ):
'''simple docstring'''
return self._config.n_layer
@property
def A ( self : List[Any] ):
'''simple docstring'''
return self._config.n_head
def A ( self : List[str] , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = super(lowercase , self ).generate_dummy_inputs(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase = seqlen + 2
UpperCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase = [
(torch.zeros(lowercase ), torch.zeros(lowercase )) for _ in range(self.num_layers )
]
UpperCAmelCase = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(lowercase , lowercase , dtype=lowercase )] , dim=1 )
return ordered_inputs
@property
def A ( self : Dict ):
'''simple docstring'''
return 13
| 34
|
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
A ={
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
A ={
'b0': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 2_24,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 2_40,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 14_08,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 2_60,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 15_36,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 3_00,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 17_92,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 3_80,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 20_48,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 4_56,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 23_04,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 5_28,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 25_60,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 6_00,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def snake_case_ (_a : List[str] ):
UpperCAmelCase = EfficientNetConfig()
UpperCAmelCase = CONFIG_MAP[model_name]['''hidden_dim''']
UpperCAmelCase = CONFIG_MAP[model_name]['''width_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''depth_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dropout_rate''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dw_padding''']
UpperCAmelCase = '''huggingface/label-files'''
UpperCAmelCase = '''imagenet-1k-id2label.json'''
UpperCAmelCase = 1_0_0_0
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ():
UpperCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase = Image.open(requests.get(_a , stream=_a ).raw )
return im
def snake_case_ (_a : str ):
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163] , do_center_crop=_a , )
return preprocessor
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
UpperCAmelCase = sorted(set(_a ) )
UpperCAmelCase = len(_a )
UpperCAmelCase = {b: str(_a ) for b, i in zip(_a , range(_a ) )}
UpperCAmelCase = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
UpperCAmelCase = block_name_mapping[b]
rename_keys.append((F"block{b}_expand_conv/kernel:0", F"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((F"block{b}_expand_bn/gamma:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((F"block{b}_expand_bn/beta:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(F"block{b}_expand_bn/moving_mean:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(F"block{b}_expand_bn/moving_variance:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(F"block{b}_dwconv/depthwise_kernel:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((F"block{b}_bn/gamma:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((F"block{b}_bn/beta:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(F"block{b}_bn/moving_mean:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(F"block{b}_bn/moving_variance:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((F"block{b}_se_reduce/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((F"block{b}_se_reduce/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((F"block{b}_se_expand/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((F"block{b}_se_expand/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(F"block{b}_project_conv/kernel:0", F"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((F"block{b}_project_bn/gamma:0", F"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((F"block{b}_project_bn/beta:0", F"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(F"block{b}_project_bn/moving_mean:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(F"block{b}_project_bn/moving_variance:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
UpperCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCAmelCase = '''efficientnet.''' + item[1]
UpperCAmelCase = '''classifier.weight'''
UpperCAmelCase = '''classifier.bias'''
return key_mapping
def snake_case_ (_a : Dict , _a : List[str] , _a : Dict ):
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
UpperCAmelCase = torch.from_numpy(np.transpose(_a ) )
else:
UpperCAmelCase = torch.from_numpy(_a )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_a )
@torch.no_grad()
def snake_case_ (_a : Optional[Any] , _a : List[str] , _a : Optional[int] , _a : Dict ):
UpperCAmelCase = model_classes[model_name](
include_top=_a , weights='''imagenet''' , input_tensor=_a , input_shape=_a , pooling=_a , classes=1_0_0_0 , classifier_activation='''softmax''' , )
UpperCAmelCase = original_model.trainable_variables
UpperCAmelCase = original_model.non_trainable_variables
UpperCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCAmelCase = param.numpy()
UpperCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
UpperCAmelCase = get_efficientnet_config(_a )
UpperCAmelCase = EfficientNetForImageClassification(_a ).eval()
UpperCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
UpperCAmelCase = rename_keys(_a )
replace_params(_a , _a , _a )
# Initialize preprocessor and preprocess input image
UpperCAmelCase = convert_image_processor(_a )
UpperCAmelCase = preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCAmelCase = hf_model(**_a )
UpperCAmelCase = outputs.logits.detach().numpy()
# Original model inference
UpperCAmelCase = False
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
UpperCAmelCase = image.img_to_array(_a )
UpperCAmelCase = np.expand_dims(_a , axis=0 )
UpperCAmelCase = original_model.predict(_a )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_a , _a , atol=1E-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(_a ):
os.mkdir(_a )
# Save converted model and image processor
hf_model.save_pretrained(_a )
preprocessor.save_pretrained(_a )
if push_to_hub:
# Push model and image processor to hub
print(F"Pushing converted {model_name} to the hub..." )
UpperCAmelCase = F"efficientnet-{model_name}"
preprocessor.push_to_hub(_a )
hf_model.push_to_hub(_a )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
A =parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A ={
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'GraphormerForGraphClassification',
'GraphormerModel',
'GraphormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
A =soup.find('meta', {'property': 'og:image'})['content']
A =requests.get(image_url).content
A =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(f"""Done. Image saved to disk as {file_name}.""")
| 34
| 1
|
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 34
|
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _a ( __a ):
__a : str = ["""vqvae"""]
def __init__( self : str , lowercase : AutoencoderKL , lowercase : UNetaDConditionModel , lowercase : Mel , lowercase : Union[DDIMScheduler, DDPMScheduler] , ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowercase , scheduler=lowercase , mel=lowercase , vqvae=lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
return 50 if isinstance(self.scheduler , lowercase ) else 1_000
@torch.no_grad()
def __call__( self : Optional[Any] , lowercase : int = 1 , lowercase : str = None , lowercase : np.ndarray = None , lowercase : int = 0 , lowercase : int = 0 , lowercase : int = None , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : float = 0 , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : torch.Tensor = None , lowercase : torch.Tensor = None , lowercase : Tuple=True , ):
'''simple docstring'''
UpperCAmelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowercase , device=self.device , )
UpperCAmelCase = noise
UpperCAmelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowercase , lowercase )
UpperCAmelCase = self.mel.audio_slice_to_image(lowercase )
UpperCAmelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase = (input_image / 255) * 2 - 1
UpperCAmelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase = self.vqvae.encode(torch.unsqueeze(lowercase , 0 ) ).latent_dist.sample(
generator=lowercase )[0]
UpperCAmelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase = int(mask_start_secs * pixels_per_second )
UpperCAmelCase = int(mask_end_secs * pixels_per_second )
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowercase ):
UpperCAmelCase = self.unet(lowercase , lowercase , lowercase )['''sample''']
else:
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
if isinstance(self.scheduler , lowercase ):
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , eta=lowercase , generator=lowercase , )['''prev_sample''']
else:
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , generator=lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
UpperCAmelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase = self.vqvae.decode(lowercase )['''sample''']
UpperCAmelCase = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase = (images * 255).round().astype('''uint8''' )
UpperCAmelCase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
UpperCAmelCase = [self.mel.image_to_audio(lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowercase ) )
@torch.no_grad()
def A ( self : Dict , lowercase : List[Image.Image] , lowercase : int = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler , lowercase )
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase = (sample / 255) * 2 - 1
UpperCAmelCase = torch.Tensor(lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase = self.scheduler.alphas_cumprod[t]
UpperCAmelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase = 1 - alpha_prod_t
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def A ( lowercase : torch.Tensor , lowercase : torch.Tensor , lowercase : float ):
'''simple docstring'''
UpperCAmelCase = acos(torch.dot(torch.flatten(lowercase ) , torch.flatten(lowercase ) ) / torch.norm(lowercase ) / torch.norm(lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(lowercase ) + sin(alpha * theta ) * xa / sin(lowercase )
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A ={
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
'tokenization_perceiver': ['PerceiverTokenizer'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['PerceiverFeatureExtractor']
A =['PerceiverImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PerceiverForImageClassificationConvProcessing',
'PerceiverForImageClassificationFourier',
'PerceiverForImageClassificationLearned',
'PerceiverForMaskedLM',
'PerceiverForMultimodalAutoencoding',
'PerceiverForOpticalFlow',
'PerceiverForSequenceClassification',
'PerceiverLayer',
'PerceiverModel',
'PerceiverPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
A =logging.get_logger(__name__)
A =TypeVar('DatasetType', Dataset, IterableDataset)
def snake_case_ (_a : List[DatasetType] , _a : Optional[List[float]] = None , _a : Optional[int] = None , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
else:
return _interleave_iterable_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
def snake_case_ (_a : List[DatasetType] , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : int = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_a , info=_a , split=_a , axis=_a )
else:
return _concatenate_iterable_datasets(_a , info=_a , split=_a , axis=_a )
| 34
| 1
|
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _a ( __a ):
@staticmethod
@abstractmethod
def A ( lowercase : ArgumentParser ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def A ( self : Optional[Any] ):
'''simple docstring'''
raise NotImplementedError()
| 34
|
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def snake_case_ (_a : Tuple ):
return {key.lstrip('''-''' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def snake_case_ ():
UpperCAmelCase = ArgumentParser(
'''HuggingFace Datasets CLI tool''' , usage='''datasets-cli <command> [<args>]''' , allow_abbrev=_a )
UpperCAmelCase = parser.add_subparsers(help='''datasets-cli command helpers''' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(_a )
EnvironmentCommand.register_subcommand(_a )
TestCommand.register_subcommand(_a )
RunBeamCommand.register_subcommand(_a )
DummyDataCommand.register_subcommand(_a )
# Parse args
UpperCAmelCase , UpperCAmelCase = parser.parse_known_args()
if not hasattr(_a , '''func''' ):
parser.print_help()
exit(1 )
UpperCAmelCase = parse_unknown_args(_a )
# Run
UpperCAmelCase = args.func(_a , **_a )
service.run()
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A =16
A =32
def snake_case_ (_a : Accelerator , _a : DatasetDict , _a : List[int] , _a : List[int] , _a : int = 1_6 ):
UpperCAmelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
UpperCAmelCase = DatasetDict(
{
'''train''': dataset['''train'''].select(_a ),
'''validation''': dataset['''train'''].select(_a ),
'''test''': dataset['''validation'''],
} )
def tokenize_function(_a : int ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase = 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():
UpperCAmelCase = 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
UpperCAmelCase = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_a : List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCAmelCase = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCAmelCase = 1_6
elif accelerator.mixed_precision != "no":
UpperCAmelCase = 8
else:
UpperCAmelCase = None
return tokenizer.pad(
_a , padding='''longest''' , max_length=_a , pad_to_multiple_of=_a , return_tensors='''pt''' , )
# Instantiate dataloaders.
UpperCAmelCase = DataLoader(
tokenized_datasets['''train'''] , shuffle=_a , collate_fn=_a , batch_size=_a )
UpperCAmelCase = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_a , collate_fn=_a , batch_size=_a )
UpperCAmelCase = DataLoader(
tokenized_datasets['''test'''] , shuffle=_a , collate_fn=_a , batch_size=_a )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case_ (_a : Any , _a : Union[str, Any] ):
# New Code #
UpperCAmelCase = []
# Download the dataset
UpperCAmelCase = load_dataset('''glue''' , '''mrpc''' )
# Create our splits
UpperCAmelCase = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase = config['''lr''']
UpperCAmelCase = int(config['''num_epochs'''] )
UpperCAmelCase = int(config['''seed'''] )
UpperCAmelCase = int(config['''batch_size'''] )
UpperCAmelCase = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
UpperCAmelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
UpperCAmelCase = batch_size // MAX_GPU_BATCH_SIZE
UpperCAmelCase = MAX_GPU_BATCH_SIZE
set_seed(_a )
# New Code #
# Create our folds:
UpperCAmelCase = kfold.split(np.zeros(datasets['''train'''].num_rows ) , datasets['''train''']['''label'''] )
UpperCAmelCase = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(_a ):
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = get_fold_dataloaders(
_a , _a , _a , _a , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase = 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).
UpperCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
UpperCAmelCase = AdamW(params=model.parameters() , lr=_a )
# Instantiate scheduler
UpperCAmelCase = get_linear_schedule_with_warmup(
optimizer=_a , num_warmup_steps=1_0_0 , num_training_steps=(len(_a ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = accelerator.prepare(
_a , _a , _a , _a , _a )
# Now we train the model
for epoch in range(_a ):
model.train()
for step, batch in enumerate(_a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.loss
UpperCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(_a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
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():
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits.argmax(dim=-1 )
UpperCAmelCase , UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_a , references=_a , )
UpperCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" , _a )
# New Code #
# We also run predictions on the test set at the very end
UpperCAmelCase = []
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():
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits
UpperCAmelCase , UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(_a , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
UpperCAmelCase = torch.cat(_a , dim=0 )
UpperCAmelCase = torch.stack(_a , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
UpperCAmelCase = metric.compute(predictions=_a , references=_a )
accelerator.print('''Average test metrics from all folds:''' , _a )
def snake_case_ ():
UpperCAmelCase = 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.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
# New Code #
parser.add_argument('''--num_folds''' , type=_a , default=3 , help='''The number of splits to perform across the dataset''' )
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
training_function(_a , _a )
if __name__ == "__main__":
main()
| 34
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
A =logging.getLogger()
def snake_case_ ():
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
UpperCAmelCase = parser.parse_args()
return args.f
def snake_case_ (_a : List[str] , _a : Union[str, Any]="eval" ):
UpperCAmelCase = os.path.join(_a , F"{split}_results.json" )
if os.path.exists(_a ):
with open(_a , '''r''' ) as f:
return json.load(_a )
raise ValueError(F"can't find {path}" )
A =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _a ( __a ):
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_glue.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
@slow
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_clm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 100 )
@slow
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_summarization_flax.main()
UpperCAmelCase = get_results(lowercase , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_ta_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = 7 if get_gpu_count() > 1 else 2
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_ner.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_qa.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 34
| 1
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
A =logging.get_logger(__name__)
class _a ( __a ):
__a : Union[str, Any] = ["""pixel_values"""]
def __init__( self : Any , lowercase : bool = True , lowercase : Dict[str, int] = None , lowercase : PILImageResampling = PIL.Image.BICUBIC , lowercase : bool = True , lowercase : Dict[str, int] = None , lowercase : Union[int, float] = 1 / 255 , lowercase : bool = True , lowercase : bool = True , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , **lowercase : Optional[Any] , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = size if size is not None else {'''height''': 256, '''width''': 256}
UpperCAmelCase = get_size_dict(lowercase )
UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = resample
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def A ( self : Optional[Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : PILImageResampling = PIL.Image.BICUBIC , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : List[Any] , ):
'''simple docstring'''
UpperCAmelCase = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}" )
return resize(
lowercase , size=(size['''height'''], size['''width''']) , resample=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Any , ):
'''simple docstring'''
UpperCAmelCase = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}" )
return center_crop(lowercase , size=(size['''height'''], size['''width''']) , data_format=lowercase , **lowercase )
def A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Union[int, float] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Optional[int] , ):
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def A ( self : Any , lowercase : np.ndarray , lowercase : Union[float, List[float]] , lowercase : Union[float, List[float]] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : List[str] , ):
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def A ( self : Any , lowercase : ImageInput , lowercase : bool = None , lowercase : Dict[str, int] = None , lowercase : Dict=None , lowercase : bool = None , lowercase : Dict[str, int] = None , lowercase : bool = None , lowercase : float = None , lowercase : bool = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[str, TensorType]] = None , lowercase : ChannelDimension = ChannelDimension.FIRST , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(lowercase )
UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = 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_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
UpperCAmelCase = [to_numpy_array(lowercase ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_center_crop:
UpperCAmelCase = [self.center_crop(image=lowercase , size=lowercase ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=lowercase , tensor_type=lowercase )
| 34
|
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[str] ):
'''simple docstring'''
for word in words:
self.insert(lowercase )
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
UpperCAmelCase = TrieNode()
UpperCAmelCase = curr.nodes[char]
UpperCAmelCase = True
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
return False
UpperCAmelCase = curr.nodes[char]
return curr.is_leaf
def A ( self : str , lowercase : str ):
'''simple docstring'''
def _delete(lowercase : TrieNode , lowercase : str , lowercase : int ) -> bool:
if index == len(lowercase ):
# If word does not exist
if not curr.is_leaf:
return False
UpperCAmelCase = False
return len(curr.nodes ) == 0
UpperCAmelCase = word[index]
UpperCAmelCase = curr.nodes.get(lowercase )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
UpperCAmelCase = _delete(lowercase , lowercase , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , lowercase , 0 )
def snake_case_ (_a : TrieNode , _a : str ):
if node.is_leaf:
print(_a , end=''' ''' )
for key, value in node.nodes.items():
print_words(_a , word + key )
def snake_case_ ():
UpperCAmelCase = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase = TrieNode()
root.insert_many(_a )
# print_words(root, "")
assert all(root.find(_a ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def snake_case_ (_a : str , _a : bool ):
print(str(_a ) , '''works!''' if passes else '''doesn\'t work :(''' )
def snake_case_ ():
assert test_trie()
def snake_case_ ():
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A =logging.get_logger(__name__)
A ={
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class _a ( __a , __a ):
__a : List[str] = """nat"""
__a : Optional[int] = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : Union[str, Any] , lowercase : Optional[int]=4 , lowercase : List[str]=3 , lowercase : int=64 , lowercase : Union[str, Any]=[3, 4, 6, 5] , lowercase : Optional[int]=[2, 4, 8, 16] , lowercase : Any=7 , lowercase : List[Any]=3.0 , lowercase : str=True , lowercase : Tuple=0.0 , lowercase : Any=0.0 , lowercase : Dict=0.1 , lowercase : str="gelu" , lowercase : List[str]=0.02 , lowercase : List[Any]=1E-5 , lowercase : Optional[int]=0.0 , lowercase : Optional[int]=None , lowercase : int=None , **lowercase : Optional[Any] , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = patch_size
UpperCAmelCase = num_channels
UpperCAmelCase = embed_dim
UpperCAmelCase = depths
UpperCAmelCase = len(lowercase )
UpperCAmelCase = num_heads
UpperCAmelCase = kernel_size
UpperCAmelCase = mlp_ratio
UpperCAmelCase = qkv_bias
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = drop_path_rate
UpperCAmelCase = hidden_act
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
UpperCAmelCase = int(embed_dim * 2 ** (len(lowercase ) - 1) )
UpperCAmelCase = layer_scale_init_value
UpperCAmelCase = ['''stem'''] + [f"stage{idx}" for idx in range(1 , len(lowercase ) + 1 )]
UpperCAmelCase , UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=lowercase , out_indices=lowercase , stage_names=self.stage_names )
| 34
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}
class _a ( __a ):
__a : Union[str, Any] = """encodec"""
def __init__( self : Tuple , lowercase : List[str]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowercase : Any=24_000 , lowercase : str=1 , lowercase : Optional[int]=False , lowercase : Optional[Any]=None , lowercase : str=None , lowercase : Tuple=128 , lowercase : Union[str, Any]=32 , lowercase : Union[str, Any]=1 , lowercase : Optional[Any]=[8, 5, 4, 2] , lowercase : Any="weight_norm" , lowercase : Tuple=7 , lowercase : int=7 , lowercase : Dict=3 , lowercase : List[Any]=2 , lowercase : str=True , lowercase : List[str]="reflect" , lowercase : List[Any]=2 , lowercase : Optional[Any]=2 , lowercase : int=1.0 , lowercase : Dict=1_024 , lowercase : str=None , lowercase : Union[str, Any]=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = target_bandwidths
UpperCAmelCase = sampling_rate
UpperCAmelCase = audio_channels
UpperCAmelCase = normalize
UpperCAmelCase = chunk_length_s
UpperCAmelCase = overlap
UpperCAmelCase = hidden_size
UpperCAmelCase = num_filters
UpperCAmelCase = num_residual_layers
UpperCAmelCase = upsampling_ratios
UpperCAmelCase = norm_type
UpperCAmelCase = kernel_size
UpperCAmelCase = last_kernel_size
UpperCAmelCase = residual_kernel_size
UpperCAmelCase = dilation_growth_rate
UpperCAmelCase = use_causal_conv
UpperCAmelCase = pad_mode
UpperCAmelCase = compress
UpperCAmelCase = num_lstm_layers
UpperCAmelCase = trim_right_ratio
UpperCAmelCase = codebook_size
UpperCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}" )
super().__init__(**lowercase )
@property
def A ( self : Dict ):
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 34
| 1
|
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _a :
def __init__( self : Any , lowercase : Any , lowercase : int , lowercase : int ):
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
UpperCAmelCase = img
UpperCAmelCase = img.shape[1]
UpperCAmelCase = img.shape[0]
UpperCAmelCase = dst_width
UpperCAmelCase = dst_height
UpperCAmelCase = self.src_w / self.dst_w
UpperCAmelCase = self.src_h / self.dst_h
UpperCAmelCase = UpperCAmelCase = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def A ( self : Union[str, Any] ):
'''simple docstring'''
for i in range(self.dst_h ):
for j in range(self.dst_w ):
UpperCAmelCase = self.img[self.get_y(lowercase )][self.get_x(lowercase )]
def A ( self : Dict , lowercase : int ):
'''simple docstring'''
return int(self.ratio_x * x )
def A ( self : List[Any] , lowercase : int ):
'''simple docstring'''
return int(self.ratio_y * y )
if __name__ == "__main__":
A , A =8_00, 6_00
A =imread('image_data/lena.jpg', 1)
A =NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
)
waitKey(0)
destroyAllWindows()
| 34
|
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A =logging.get_logger(__name__)
class _a ( __a ):
__a : str = ["""pixel_values"""]
def __init__( self : Optional[int] , lowercase : bool = True , lowercase : Optional[Dict[str, int]] = None , lowercase : PILImageResampling = PILImageResampling.BILINEAR , lowercase : bool = True , lowercase : Dict[str, int] = None , lowercase : bool = True , lowercase : Union[int, float] = 1 / 255 , lowercase : bool = True , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = size if size is not None else {'''shortest_edge''': 256}
UpperCAmelCase = get_size_dict(lowercase , default_to_square=lowercase )
UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = resample
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : PILImageResampling = PILImageResampling.BICUBIC , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Any , ):
'''simple docstring'''
UpperCAmelCase = 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()}" )
UpperCAmelCase = 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 A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : int , ):
'''simple docstring'''
UpperCAmelCase = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(lowercase , size=(size['''height'''], size['''width''']) , data_format=lowercase , **lowercase )
def A ( self : Tuple , lowercase : np.ndarray , lowercase : float , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : List[str] ):
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[int] , lowercase : np.ndarray , lowercase : Union[float, List[float]] , lowercase : Union[float, List[float]] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[int] , lowercase : ImageInput , lowercase : Optional[bool] = None , lowercase : Dict[str, int] = None , lowercase : PILImageResampling = None , lowercase : bool = None , lowercase : Dict[str, int] = None , lowercase : Optional[bool] = None , lowercase : Optional[float] = None , lowercase : Optional[bool] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[str, TensorType]] = None , lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **lowercase : Dict , ):
'''simple docstring'''
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(lowercase , default_to_square=lowercase )
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = 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.
UpperCAmelCase = [to_numpy_array(lowercase ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_center_crop:
UpperCAmelCase = [self.center_crop(image=lowercase , size=lowercase ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=lowercase , tensor_type=lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[Tuple] = None ):
'''simple docstring'''
UpperCAmelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowercase ) != len(lowercase ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(lowercase ):
UpperCAmelCase = target_sizes.numpy()
UpperCAmelCase = []
for idx in range(len(lowercase ) ):
UpperCAmelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=lowercase )
UpperCAmelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowercase )
else:
UpperCAmelCase = logits.argmax(dim=1 )
UpperCAmelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A ={
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
'convert_funnel_original_tf_checkpoint_to_pytorch': [],
'tokenization_funnel': ['FunnelTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['FunnelTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'FunnelBaseModel',
'FunnelForMaskedLM',
'FunnelForMultipleChoice',
'FunnelForPreTraining',
'FunnelForQuestionAnswering',
'FunnelForSequenceClassification',
'FunnelForTokenClassification',
'FunnelModel',
'FunnelPreTrainedModel',
'load_tf_weights_in_funnel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFFunnelBaseModel',
'TFFunnelForMaskedLM',
'TFFunnelForMultipleChoice',
'TFFunnelForPreTraining',
'TFFunnelForQuestionAnswering',
'TFFunnelForSequenceClassification',
'TFFunnelForTokenClassification',
'TFFunnelModel',
'TFFunnelPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A =logging.getLogger(__name__)
def snake_case_ (_a : Dict , _a : Union[str, Any] ):
return (preds == labels).mean()
@dataclass
class _a :
__a : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class _a :
__a : str = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
__a : str = field(metadata={"""help""": """Should contain the data files for the task."""} )
__a : int = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__a : bool = field(
default=__a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def snake_case_ ():
# 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 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , 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
set_seed(training_args.seed )
try:
UpperCAmelCase = processors[data_args.task_name]()
UpperCAmelCase = processor.get_labels()
UpperCAmelCase = len(_a )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_a , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoModelForMultipleChoice.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 , )
# Get datasets
UpperCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
UpperCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_a : EvalPrediction ) -> Dict:
UpperCAmelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(_a , p.label_ids )}
# Data collator
UpperCAmelCase = DataCollatorWithPadding(_a , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
UpperCAmelCase = Trainer(
model=_a , args=_a , train_dataset=_a , eval_dataset=_a , compute_metrics=_a , data_collator=_a , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCAmelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase = trainer.evaluate()
UpperCAmelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(_a , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , _a , _a )
writer.write('''%s = %s\n''' % (key, value) )
results.update(_a )
return results
def snake_case_ (_a : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class _a :
def __init__( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = [2, 1, 2, -1]
UpperCAmelCase = [1, 2, 3, 4]
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = len(self.first_signal )
UpperCAmelCase = len(self.second_signal )
UpperCAmelCase = max(lowercase , lowercase )
# create a zero matrix of max_length x max_length
UpperCAmelCase = [[0] * max_length for i in range(lowercase )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(lowercase ):
UpperCAmelCase = deque(self.second_signal )
rotated_signal.rotate(lowercase )
for j, item in enumerate(lowercase ):
matrix[i][j] += item
# multiply the matrix with the first signal
UpperCAmelCase = np.matmul(np.transpose(lowercase ) , np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(lowercase , 2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 34
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImageProcessor"""
__a : List[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Dict , lowercase : Union[str, Any]=None , lowercase : Dict=None , **lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowercase , )
UpperCAmelCase = kwargs.pop('''feature_extractor''' )
UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowercase , lowercase )
UpperCAmelCase = self.image_processor
def __call__( self : Tuple , lowercase : Optional[Any]=None , lowercase : Union[str, Any]=None , lowercase : int=None , **lowercase : Dict ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCAmelCase = self.tokenizer(lowercase , return_tensors=lowercase , **lowercase )
if images is not None:
UpperCAmelCase = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def A ( self : int , *lowercase : Tuple , **lowercase : List[str] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A ( self : Optional[Any] , *lowercase : int , **lowercase : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer.model_input_names
UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A ( self : List[Any] ):
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowercase , )
return self.image_processor_class
| 34
| 1
|
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def snake_case_ (_a : Optional[int] , _a : List[Any] , _a : int ):
UpperCAmelCase = AutoConfig.from_pretrained(_a )
UpperCAmelCase = FlaxAutoModelForSeqaSeqLM.from_config(config=_a )
UpperCAmelCase = checkpoints.load_tax_checkpoint(_a )
UpperCAmelCase = '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
if config.model_type == "t5":
UpperCAmelCase = '''SelfAttention'''
if config.model_type == "longt5" and config.encoder_attention_type == "local":
UpperCAmelCase = '''LocalSelfAttention'''
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
UpperCAmelCase = '''TransientGlobalSelfAttention'''
else:
raise ValueError(
'''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
''' attribute with a value from [\'local\', \'transient-global].''' )
# Encoder
for layer_index in range(config.num_layers ):
UpperCAmelCase = F"layers_{str(_a )}"
# Self-Attention
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']
# Layer Normalization
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
if split_mlp_wi:
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
UpperCAmelCase = tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
UpperCAmelCase = flax_model.params['''encoder''']['''block'''][str(_a )]['''layer''']
UpperCAmelCase = tax_attention_key
UpperCAmelCase = tax_attention_out
UpperCAmelCase = tax_attention_query
UpperCAmelCase = tax_attention_value
UpperCAmelCase = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
UpperCAmelCase = tax_global_layer_norm
if split_mlp_wi:
UpperCAmelCase = tax_mlp_wi_a
UpperCAmelCase = tax_mlp_wi_a
else:
UpperCAmelCase = tax_mlp_wi
UpperCAmelCase = tax_mlp_wo
UpperCAmelCase = tax_mlp_layer_norm
UpperCAmelCase = flax_model_encoder_layer_block
# Only for layer 0:
UpperCAmelCase = tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
UpperCAmelCase = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
UpperCAmelCase = tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
UpperCAmelCase = tax_encoder_global_rel_embedding
# Assigning
UpperCAmelCase = tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
UpperCAmelCase = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
UpperCAmelCase = F"layers_{str(_a )}"
# Self-Attention
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
# Layer Normalization
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
'''scale'''
]
# Encoder-Decoder-Attention
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
UpperCAmelCase = tax_enc_dec_attention_module['''key''']['''kernel''']
UpperCAmelCase = tax_enc_dec_attention_module['''out''']['''kernel''']
UpperCAmelCase = tax_enc_dec_attention_module['''query''']['''kernel''']
UpperCAmelCase = tax_enc_dec_attention_module['''value''']['''kernel''']
# Layer Normalization
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
# MLP
if split_mlp_wi:
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
UpperCAmelCase = tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
UpperCAmelCase = flax_model.params['''decoder''']['''block'''][str(_a )]['''layer''']
UpperCAmelCase = tax_attention_key
UpperCAmelCase = tax_attention_out
UpperCAmelCase = tax_attention_query
UpperCAmelCase = tax_attention_value
UpperCAmelCase = tax_pre_attention_layer_norm
UpperCAmelCase = tax_enc_dec_attention_key
UpperCAmelCase = tax_enc_dec_attention_out
UpperCAmelCase = tax_enc_dec_attention_query
UpperCAmelCase = tax_enc_dec_attention_value
UpperCAmelCase = tax_cross_layer_norm
if split_mlp_wi:
UpperCAmelCase = tax_mlp_wi_a
UpperCAmelCase = tax_mlp_wi_a
else:
UpperCAmelCase = tax_mlp_wi
UpperCAmelCase = tax_mlp_wo
UpperCAmelCase = txa_mlp_layer_norm
UpperCAmelCase = flax_model_decoder_layer_block
# Decoder Normalization
UpperCAmelCase = tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
UpperCAmelCase = txa_decoder_norm
# Only for layer 0:
UpperCAmelCase = tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
UpperCAmelCase = tax_decoder_rel_embedding
# Token Embeddings
UpperCAmelCase = tax_model['''target''']['''token_embedder''']['''embedding''']
UpperCAmelCase = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
UpperCAmelCase = tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
flax_model.save_pretrained(_a )
print('''T5X Model was sucessfully converted!''' )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
)
parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
parser.add_argument(
'--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
)
A =parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 34
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A =logging.get_logger(__name__)
A ={
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class _a ( __a ):
__a : List[Any] = """marian"""
__a : Union[str, Any] = ["""past_key_values"""]
__a : List[str] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[Any] , lowercase : Union[str, Any]=58_101 , lowercase : Tuple=None , lowercase : str=1_024 , lowercase : Optional[int]=12 , lowercase : Optional[int]=4_096 , lowercase : int=16 , lowercase : List[Any]=12 , lowercase : int=4_096 , lowercase : Optional[int]=16 , lowercase : int=0.0 , lowercase : Tuple=0.0 , lowercase : Tuple=True , lowercase : Union[str, Any]=True , lowercase : List[Any]="gelu" , lowercase : Tuple=1_024 , lowercase : str=0.1 , lowercase : str=0.0 , lowercase : Optional[int]=0.0 , lowercase : Dict=0.02 , lowercase : Union[str, Any]=58_100 , lowercase : List[str]=False , lowercase : str=58_100 , lowercase : Any=0 , lowercase : Optional[Any]=0 , lowercase : Tuple=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = vocab_size
UpperCAmelCase = decoder_vocab_size or vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = d_model
UpperCAmelCase = encoder_ffn_dim
UpperCAmelCase = encoder_layers
UpperCAmelCase = encoder_attention_heads
UpperCAmelCase = decoder_ffn_dim
UpperCAmelCase = decoder_layers
UpperCAmelCase = decoder_attention_heads
UpperCAmelCase = dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation_dropout
UpperCAmelCase = activation_function
UpperCAmelCase = init_std
UpperCAmelCase = encoder_layerdrop
UpperCAmelCase = decoder_layerdrop
UpperCAmelCase = use_cache
UpperCAmelCase = encoder_layers
UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , forced_eos_token_id=lowercase , **lowercase , )
class _a ( __a ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A ( self : int ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase = {0: '''batch'''}
UpperCAmelCase = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A ( self : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super().outputs
else:
UpperCAmelCase = super(lowercase , self ).outputs
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A ( self : Dict , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Generate decoder inputs
UpperCAmelCase = seq_length if not self.use_past else 1
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
UpperCAmelCase = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
UpperCAmelCase = dict(**lowercase , **lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
UpperCAmelCase = common_inputs['''decoder_input_ids'''].shape[1]
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = decoder_seq_length + 3
UpperCAmelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
UpperCAmelCase = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase , lowercase )] , dim=1 )
UpperCAmelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase = min(lowercase , lowercase )
UpperCAmelCase = max(lowercase , lowercase ) - min_num_layers
UpperCAmelCase = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
) )
# TODO: test this.
UpperCAmelCase = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase , lowercase ):
common_inputs["past_key_values"].append((torch.zeros(lowercase ), torch.zeros(lowercase )) )
return common_inputs
def A ( self : int , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase = seqlen + 2
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = common_inputs['''attention_mask'''].dtype
UpperCAmelCase = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase , lowercase , dtype=lowercase )] , dim=1 )
UpperCAmelCase = [
(torch.zeros(lowercase ), torch.zeros(lowercase )) for _ in range(lowercase )
]
return common_inputs
def A ( self : str , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase = tokenizer.num_special_tokens_to_add(lowercase )
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
UpperCAmelCase = dict(tokenizer(lowercase , return_tensors=lowercase ) )
return common_inputs
def A ( self : List[str] , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
else:
UpperCAmelCase = self._generate_dummy_inputs_for_causal_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
return common_inputs
def A ( self : List[Any] , lowercase : Any , lowercase : Tuple , lowercase : Any , lowercase : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super()._flatten_past_key_values_(lowercase , lowercase , lowercase , lowercase )
else:
UpperCAmelCase = super(lowercase , self )._flatten_past_key_values_(
lowercase , lowercase , lowercase , lowercase )
@property
def A ( self : Any ):
'''simple docstring'''
return 1E-4
| 34
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A =logging.get_logger(__name__)
A ={
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class _a ( __a ):
__a : List[Any] = """marian"""
__a : Union[str, Any] = ["""past_key_values"""]
__a : List[str] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[Any] , lowercase : Union[str, Any]=58_101 , lowercase : Tuple=None , lowercase : str=1_024 , lowercase : Optional[int]=12 , lowercase : Optional[int]=4_096 , lowercase : int=16 , lowercase : List[Any]=12 , lowercase : int=4_096 , lowercase : Optional[int]=16 , lowercase : int=0.0 , lowercase : Tuple=0.0 , lowercase : Tuple=True , lowercase : Union[str, Any]=True , lowercase : List[Any]="gelu" , lowercase : Tuple=1_024 , lowercase : str=0.1 , lowercase : str=0.0 , lowercase : Optional[int]=0.0 , lowercase : Dict=0.02 , lowercase : Union[str, Any]=58_100 , lowercase : List[str]=False , lowercase : str=58_100 , lowercase : Any=0 , lowercase : Optional[Any]=0 , lowercase : Tuple=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = vocab_size
UpperCAmelCase = decoder_vocab_size or vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = d_model
UpperCAmelCase = encoder_ffn_dim
UpperCAmelCase = encoder_layers
UpperCAmelCase = encoder_attention_heads
UpperCAmelCase = decoder_ffn_dim
UpperCAmelCase = decoder_layers
UpperCAmelCase = decoder_attention_heads
UpperCAmelCase = dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation_dropout
UpperCAmelCase = activation_function
UpperCAmelCase = init_std
UpperCAmelCase = encoder_layerdrop
UpperCAmelCase = decoder_layerdrop
UpperCAmelCase = use_cache
UpperCAmelCase = encoder_layers
UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , forced_eos_token_id=lowercase , **lowercase , )
class _a ( __a ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A ( self : int ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase = {0: '''batch'''}
UpperCAmelCase = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A ( self : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super().outputs
else:
UpperCAmelCase = super(lowercase , self ).outputs
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A ( self : Dict , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Generate decoder inputs
UpperCAmelCase = seq_length if not self.use_past else 1
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
UpperCAmelCase = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
UpperCAmelCase = dict(**lowercase , **lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
UpperCAmelCase = common_inputs['''decoder_input_ids'''].shape[1]
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = decoder_seq_length + 3
UpperCAmelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
UpperCAmelCase = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase , lowercase )] , dim=1 )
UpperCAmelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase = min(lowercase , lowercase )
UpperCAmelCase = max(lowercase , lowercase ) - min_num_layers
UpperCAmelCase = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
) )
# TODO: test this.
UpperCAmelCase = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase , lowercase ):
common_inputs["past_key_values"].append((torch.zeros(lowercase ), torch.zeros(lowercase )) )
return common_inputs
def A ( self : int , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase = seqlen + 2
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = common_inputs['''attention_mask'''].dtype
UpperCAmelCase = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase , lowercase , dtype=lowercase )] , dim=1 )
UpperCAmelCase = [
(torch.zeros(lowercase ), torch.zeros(lowercase )) for _ in range(lowercase )
]
return common_inputs
def A ( self : str , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase = tokenizer.num_special_tokens_to_add(lowercase )
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
UpperCAmelCase = dict(tokenizer(lowercase , return_tensors=lowercase ) )
return common_inputs
def A ( self : List[str] , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
else:
UpperCAmelCase = self._generate_dummy_inputs_for_causal_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
return common_inputs
def A ( self : List[Any] , lowercase : Any , lowercase : Tuple , lowercase : Any , lowercase : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super()._flatten_past_key_values_(lowercase , lowercase , lowercase , lowercase )
else:
UpperCAmelCase = super(lowercase , self )._flatten_past_key_values_(
lowercase , lowercase , lowercase , lowercase )
@property
def A ( self : Any ):
'''simple docstring'''
return 1E-4
| 34
|
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
print(solution())
| 34
| 1
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _a ( __a , unittest.TestCase ):
__a : Optional[int] = DDIMPipeline
__a : Any = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
__a : int = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
__a : Optional[Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
__a : Tuple = False
def A ( self : str ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
UpperCAmelCase = DDIMScheduler()
UpperCAmelCase = {'''unet''': unet, '''scheduler''': scheduler}
return components
def A ( self : Optional[Any] , lowercase : int , lowercase : Dict=0 ):
'''simple docstring'''
if str(lowercase ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(lowercase )
else:
UpperCAmelCase = torch.Generator(device=lowercase ).manual_seed(lowercase )
UpperCAmelCase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = '''cpu'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = pipe(**lowercase ).images
UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
UpperCAmelCase = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase , 1E-3 )
def A ( self : Union[str, Any] ):
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def A ( self : Optional[int] ):
'''simple docstring'''
super().test_save_load_local(expected_max_difference=3E-3 )
def A ( self : Optional[Any] ):
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def A ( self : Optional[Any] ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = '''google/ddpm-cifar10-32'''
UpperCAmelCase = UNetaDModel.from_pretrained(lowercase )
UpperCAmelCase = DDIMScheduler()
UpperCAmelCase = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddim.to(lowercase )
ddim.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = ddim(generator=lowercase , eta=0.0 , output_type='''numpy''' ).images
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = '''google/ddpm-ema-bedroom-256'''
UpperCAmelCase = UNetaDModel.from_pretrained(lowercase )
UpperCAmelCase = DDIMScheduler.from_pretrained(lowercase )
UpperCAmelCase = DDIMPipeline(unet=lowercase , scheduler=lowercase )
ddpm.to(lowercase )
ddpm.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = ddpm(generator=lowercase , output_type='''numpy''' ).images
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 34
|
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
A =logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
A ={
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'summarization': AutoModelForSeqaSeqLM,
'translation': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
A ={
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
A =sorted(arg_to_scheduler.keys())
A ='{' + ', '.join(arg_to_scheduler_choices) + '}'
class _a ( pl.LightningModule ):
def __init__( self : List[str] , lowercase : argparse.Namespace , lowercase : List[Any]=None , lowercase : Dict="base" , lowercase : Optional[int]=None , lowercase : Dict=None , lowercase : Tuple=None , **lowercase : Optional[int] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowercase )
UpperCAmelCase = 0
UpperCAmelCase = Path(self.hparams.output_dir )
UpperCAmelCase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
UpperCAmelCase = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'''num_labels''': num_labels} if num_labels is not None else {}) , cache_dir=lowercase , **lowercase , )
else:
UpperCAmelCase = config
UpperCAmelCase = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , lowercase , lowercase ):
assert hasattr(self.config , lowercase ), f"model config doesn't have a `{p}` attribute"
setattr(self.config , lowercase , getattr(self.hparams , lowercase ) )
if tokenizer is None:
UpperCAmelCase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowercase , )
else:
UpperCAmelCase = tokenizer
UpperCAmelCase = MODEL_MODES[mode]
if model is None:
UpperCAmelCase = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=lowercase , )
else:
UpperCAmelCase = model
def A ( self : List[Any] , *lowercase : List[str] , **lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.model_type.from_pretrained(*lowercase , **lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.hparams.lr_scheduler]
UpperCAmelCase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
UpperCAmelCase = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''params''': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'''weight_decay''': self.hparams.weight_decay,
},
{
'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
if self.hparams.adafactor:
UpperCAmelCase = Adafactor(
lowercase , lr=self.hparams.learning_rate , scale_parameter=lowercase , relative_step=lowercase )
else:
UpperCAmelCase = AdamW(
lowercase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
UpperCAmelCase = optimizer
UpperCAmelCase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A ( self : List[Any] , lowercase : int , lowercase : List[str] ):
'''simple docstring'''
return self.validation_step(lowercase , lowercase )
def A ( self : List[Any] , lowercase : Tuple ):
'''simple docstring'''
return self.validation_end(lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
UpperCAmelCase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A ( self : List[str] , lowercase : Any ):
'''simple docstring'''
if stage == "test":
UpperCAmelCase = len(self.test_dataloader().dataset )
else:
UpperCAmelCase = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=lowercase )
UpperCAmelCase = len(self.train_dataloader().dataset )
def A ( self : List[str] , lowercase : str , lowercase : int , lowercase : bool = False ):
'''simple docstring'''
raise NotImplementedError('''You must implement this for your task''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
return self.train_loader
def A ( self : Optional[Any] ):
'''simple docstring'''
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
lowercase , list(filter(lowercase , self.hparams.model_name_or_path.split('''/''' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A ( self : List[str] , lowercase : Dict[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.output_dir.joinpath('''best_tfmr''' )
UpperCAmelCase = self.step_count
self.model.save_pretrained(lowercase )
self.tokenizer.save_pretrained(lowercase )
@staticmethod
def A ( lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
parser.add_argument(
'''--model_name_or_path''' , default=lowercase , type=lowercase , required=lowercase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=lowercase , help='''Pretrained config name or path if not the same as model_name''' )
parser.add_argument(
'''--tokenizer_name''' , default=lowercase , type=lowercase , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(lowercase ).parent / '''test_run''' / '''cache''' ) , type=lowercase , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=lowercase , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=lowercase , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=lowercase , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=lowercase , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5E-5 , type=lowercase , help='''The initial learning rate for Adam.''' )
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=lowercase , metavar=lowercase , type=lowercase , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=lowercase , help='''Weight decay if we apply some.''' )
parser.add_argument('''--adam_epsilon''' , default=1E-8 , type=lowercase , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--warmup_steps''' , default=0 , type=lowercase , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--num_workers''' , default=4 , type=lowercase , help='''kwarg passed to DataLoader''' )
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=lowercase )
parser.add_argument('''--train_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--eval_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--adafactor''' , action='''store_true''' )
class _a ( pl.Callback ):
def A ( self : Dict , lowercase : Optional[Any] , lowercase : List[Any] ):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Union[str, Any] , lowercase : Any ):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowercase )
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Optional[int] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = trainer.lr_schedulers[0]['''scheduler''']
UpperCAmelCase = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowercase )
def A ( self : Tuple , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Validation results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log results
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def A ( self : Dict , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Test results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log and save results to file
UpperCAmelCase = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''' )
with open(lowercase , '''w''' ) as writer:
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
writer.write('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def snake_case_ (_a : int , _a : Optional[Any] ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'''--output_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_a , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument(
'''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , )
parser.add_argument(
'''--fp16_opt_level''' , type=_a , default='''O2''' , help=(
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
'''See details at https://nvidia.github.io/apex/amp.html'''
) , )
parser.add_argument('''--n_tpu_cores''' , dest='''tpu_cores''' , type=_a )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_a , help='''Max gradient norm''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_predict''' , action='''store_true''' , help='''Whether to run predictions on the test set.''' )
parser.add_argument(
'''--gradient_accumulation_steps''' , dest='''accumulate_grad_batches''' , type=_a , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_a , default=4_2 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_a , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def snake_case_ (_a : BaseTransformer , _a : argparse.Namespace , _a : List[Any]=None , _a : Tuple=True , _a : int=[] , _a : Any=None , _a : int=None , **_a : Optional[Any] , ):
pl.seed_everything(args.seed )
# init model
UpperCAmelCase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_a )
# add custom checkpoints
if checkpoint_callback is None:
UpperCAmelCase = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='''checkpoint''' , monitor='''val_loss''' , mode='''min''' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_a )
if logging_callback is None:
UpperCAmelCase = LoggingCallback()
UpperCAmelCase = {}
if args.fpaa:
UpperCAmelCase = 1_6
if args.gpus > 1:
UpperCAmelCase = '''auto'''
UpperCAmelCase = '''ddp'''
UpperCAmelCase = args.accumulate_grad_batches
UpperCAmelCase = None
UpperCAmelCase = '''auto'''
UpperCAmelCase = pl.Trainer.from_argparse_args(
_a , weights_summary=_a , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_a , val_check_interval=1 , num_sanity_val_steps=2 , **_a , )
if args.do_train:
trainer.fit(_a )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 34
| 1
|
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
A ='0.12' # assumed parallelism: 8
if is_torch_available():
import torch
def snake_case_ (_a : str , _a : Optional[Any] , _a : Dict=None ):
if rng is None:
UpperCAmelCase = random.Random()
UpperCAmelCase = 1
for dim in shape:
total_dims *= dim
UpperCAmelCase = []
for _ in range(_a ):
values.append(rng.randint(0 , vocab_size - 1 ) )
UpperCAmelCase = np.array(_a , dtype=jnp.intaa ).reshape(_a )
return output
def snake_case_ (_a : Any , _a : int=None ):
UpperCAmelCase = ids_tensor(_a , vocab_size=2 , rng=_a )
# make sure that at least one token is attended to for each batch
UpperCAmelCase = 1
return attn_mask
@require_flax
class _a :
__a : Dict = None
__a : List[Any] = ()
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
UpperCAmelCase = 2
UpperCAmelCase = inputs['''input_ids'''].shape[-1] // 2
UpperCAmelCase = inputs['''input_ids'''][:max_batch_size, :sequence_length]
UpperCAmelCase = jnp.ones_like(lowercase )
UpperCAmelCase = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
UpperCAmelCase = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
UpperCAmelCase = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 0
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase = getattr(lowercase , lowercase )
UpperCAmelCase = pt_model_class(lowercase ).eval()
UpperCAmelCase = load_flax_weights_in_pytorch_model(lowercase , flax_model.params )
UpperCAmelCase = flax_model.generate(lowercase ).sequences
UpperCAmelCase = pt_model.generate(torch.tensor(lowercase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
UpperCAmelCase = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = True
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 2
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 2
UpperCAmelCase = 2
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = True
UpperCAmelCase = max_length
UpperCAmelCase = 0.8
UpperCAmelCase = 10
UpperCAmelCase = 0.3
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = max_length
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = max_length
UpperCAmelCase = 2
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = False
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase , attention_mask=lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase , attention_mask=lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = True
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase , attention_mask=lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase , attention_mask=lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = 2
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase , attention_mask=lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase , attention_mask=lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class _a ( unittest.TestCase ):
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' )
UpperCAmelCase = FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
UpperCAmelCase = '''Hello world'''
UpperCAmelCase = tokenizer(lowercase , return_tensors='''np''' ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(lowercase , '''do_samples''' ):
model.generate(lowercase , do_samples=lowercase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(lowercase , '''foo''' ):
UpperCAmelCase = {'''foo''': '''bar'''}
model.generate(lowercase , **lowercase )
| 34
|
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : dict , _a : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
UpperCAmelCase = cst_fwd.get(_a , np.inf )
UpperCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
UpperCAmelCase = new_cost_f
UpperCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
UpperCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def snake_case_ (_a : str , _a : str , _a : dict , _a : dict ):
UpperCAmelCase = -1
UpperCAmelCase = set()
UpperCAmelCase = set()
UpperCAmelCase = {source: 0}
UpperCAmelCase = {destination: 0}
UpperCAmelCase = {source: None}
UpperCAmelCase = {destination: None}
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
UpperCAmelCase , UpperCAmelCase = queue_forward.get()
visited_forward.add(_a )
UpperCAmelCase , UpperCAmelCase = queue_backward.get()
visited_backward.add(_a )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
UpperCAmelCase = shortest_distance
return shortest_path_distance
A ={
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
A ={
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
A =2
class _a :
def __init__( self : Any , *, # begin keyword-only arguments
lowercase : str="<s>" , lowercase : Optional[Any]="<pad>" , lowercase : str="</s>" , lowercase : str="<unk>" , lowercase : List[str]=None , ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = bos, unk, pad, eos
UpperCAmelCase = []
UpperCAmelCase = []
UpperCAmelCase = {}
UpperCAmelCase = self.add_symbol(lowercase )
UpperCAmelCase = self.add_symbol(lowercase )
UpperCAmelCase = self.add_symbol(lowercase )
UpperCAmelCase = self.add_symbol(lowercase )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(lowercase )
UpperCAmelCase = len(self.symbols )
def __eq__( self : Optional[Any] , lowercase : str ):
'''simple docstring'''
return self.indices == other.indices
def __getitem__( self : Tuple , lowercase : Union[str, Any] ):
'''simple docstring'''
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : int ):
'''simple docstring'''
return len(self.symbols )
def __contains__( self : List[Any] , lowercase : List[str] ):
'''simple docstring'''
return sym in self.indices
@classmethod
def A ( cls : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = cls()
d.add_from_file(lowercase )
return d
def A ( self : int , lowercase : Union[str, Any] , lowercase : str=1 , lowercase : List[Any]=False ):
'''simple docstring'''
if word in self.indices and not overwrite:
UpperCAmelCase = self.indices[word]
UpperCAmelCase = self.count[idx] + n
return idx
else:
UpperCAmelCase = len(self.symbols )
UpperCAmelCase = idx
self.symbols.append(lowercase )
self.count.append(lowercase )
return idx
def A ( self : Optional[int] , lowercase : int ):
'''simple docstring'''
return 0
def A ( self : Tuple , lowercase : Optional[int] ):
'''simple docstring'''
if isinstance(lowercase , lowercase ):
try:
with open(lowercase , '''r''' , encoding='''utf-8''' ) as fd:
self.add_from_file(lowercase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(lowercase ) )
return
UpperCAmelCase = f.readlines()
UpperCAmelCase = self._load_meta(lowercase )
for line in lines[indices_start_line:]:
try:
UpperCAmelCase , UpperCAmelCase = line.rstrip().rsplit(''' ''' , 1 )
if field == "#fairseq:overwrite":
UpperCAmelCase = True
UpperCAmelCase , UpperCAmelCase = line.rsplit(''' ''' , 1 )
else:
UpperCAmelCase = False
UpperCAmelCase = int(lowercase )
UpperCAmelCase = line
if word in self and not overwrite:
raise RuntimeError(
'''Duplicate word found when loading Dictionary: \'{}\'. '''
'''Duplicate words can overwrite earlier ones by adding the '''
'''#fairseq:overwrite flag at the end of the corresponding row '''
'''in the dictionary file. If using the Camembert model, please '''
'''download an updated copy of the model file.'''.format(lowercase ) )
self.add_symbol(lowercase , n=lowercase , overwrite=lowercase )
except ValueError:
raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\'''' )
def snake_case_ (_a : int ):
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
UpperCAmelCase = dict((re.sub(R'''@@$''' , '''''' , _a ), v) if k.endswith('''@@''' ) else (re.sub(R'''$''' , '''</w>''' , _a ), v) for k, v in d.items() )
UpperCAmelCase = '''<s> <pad> </s> <unk>'''.split()
# restore the special tokens
for k in keep_keys:
del da[F"{k}</w>"]
UpperCAmelCase = d[k] # restore
return da
def snake_case_ (_a : str , _a : str ):
# prep
if not os.path.exists(_a ):
raise ValueError(F"path {biogpt_checkpoint_path} does not exist!" )
os.makedirs(_a , exist_ok=_a )
print(F"Writing results to {pytorch_dump_folder_path}" )
# handle various types of models
UpperCAmelCase = os.path.join(_a , '''checkpoint.pt''' )
if not os.path.isfile(_a ):
raise ValueError(F"path to the file {checkpoint_file} does not exist!" )
UpperCAmelCase = torch.load(_a , map_location='''cpu''' )
UpperCAmelCase = chkpt['''cfg''']['''model''']
# dicts
UpperCAmelCase = os.path.join(_a , '''dict.txt''' )
if not os.path.isfile(_a ):
raise ValueError(F"path to the file {dict_file} does not exist!" )
UpperCAmelCase = Dictionary.load(_a )
UpperCAmelCase = rewrite_dict_keys(src_dict.indices )
UpperCAmelCase = len(_a )
UpperCAmelCase = os.path.join(_a , VOCAB_FILES_NAMES['''vocab_file'''] )
print(F"Generating {src_vocab_file} of {src_vocab_size} records" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , ensure_ascii=_a , indent=_a ) )
# merges_file (bpecodes)
UpperCAmelCase = os.path.join(_a , '''bpecodes''' )
if not os.path.isfile(_a ):
raise ValueError(F"path to the file {bpecodes_file} does not exist!" )
UpperCAmelCase = os.path.join(_a , VOCAB_FILES_NAMES['''merges_file'''] )
shutil.copyfile(_a , _a )
# model config
UpperCAmelCase = os.path.join(_a , '''config.json''' )
UpperCAmelCase = {
'''activation_dropout''': args['''activation_dropout'''],
'''architectures''': ['''BioGptForCausalLM'''],
'''attention_probs_dropout_prob''': args['''attention_dropout'''],
'''bos_token_id''': 0,
'''eos_token_id''': 2,
'''hidden_act''': args['''activation_fn'''],
'''hidden_dropout_prob''': args['''dropout'''],
'''hidden_size''': args['''decoder_embed_dim'''],
'''initializer_range''': 0.02,
'''intermediate_size''': args['''decoder_ffn_embed_dim'''],
'''layer_norm_eps''': 1E-12,
'''layerdrop''': args['''decoder_layerdrop'''],
'''max_position_embeddings''': args['''max_target_positions'''],
'''model_type''': '''biogpt''',
'''num_attention_heads''': args['''decoder_attention_heads'''],
'''num_hidden_layers''': args['''decoder_layers'''],
'''pad_token_id''': 1,
'''scale_embedding''': not args['''no_scale_embedding'''],
'''tie_word_embeddings''': args['''share_decoder_input_output_embed'''],
'''vocab_size''': src_vocab_size,
}
# good hparam defaults to start with
print(F"Generating {biogpt_model_config_file}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , ensure_ascii=_a , indent=_a ) )
# tokenizer config
UpperCAmelCase = os.path.join(_a , _a )
UpperCAmelCase = {
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
'''model_max_length''': 1_0_2_4,
'''pad_token''': '''<pad>''',
'''special_tokens_map_file''': None,
'''tokenizer_class''': '''BioGptTokenizer''',
'''unk_token''': '''<unk>''',
}
print(F"Generating {biogpt_tokenizer_config_file}" )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(_a , ensure_ascii=_a , indent=_a ) )
# model
UpperCAmelCase = chkpt['''model''']
# remove unneeded keys
UpperCAmelCase = [
'''decoder.version''',
]
for k in ignore_keys:
model_state_dict.pop(_a , _a )
UpperCAmelCase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith('''output_projection.weight''' ):
UpperCAmelCase = model_state_dict.pop(_a )
else:
UpperCAmelCase = model_state_dict.pop(_a )
UpperCAmelCase = BioGptConfig.from_pretrained(_a )
UpperCAmelCase = BioGptForCausalLM(_a )
# check that it loads ok
model_new.load_state_dict(_a )
# save
UpperCAmelCase = os.path.join(_a , _a )
print(F"Generating {pytorch_weights_dump_path}" )
torch.save(_a , _a )
print('''Conversion is done!''' )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--biogpt_checkpoint_path',
default=None,
type=str,
required=True,
help=(
'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
' bpecodes, etc.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A =parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 34
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
def snake_case_ (_a : List[str] ):
UpperCAmelCase = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
UpperCAmelCase = 1_2_8
elif "12-12" in model_name:
UpperCAmelCase = 1_2
UpperCAmelCase = 1_2
elif "14-14" in model_name:
UpperCAmelCase = 1_4
UpperCAmelCase = 1_4
elif "16-16" in model_name:
UpperCAmelCase = 1_6
UpperCAmelCase = 1_6
else:
raise ValueError('''Model not supported''' )
UpperCAmelCase = '''huggingface/label-files'''
if "speech-commands" in model_name:
UpperCAmelCase = 3_5
UpperCAmelCase = '''speech-commands-v2-id2label.json'''
else:
UpperCAmelCase = 5_2_7
UpperCAmelCase = '''audioset-id2label.json'''
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ (_a : Tuple ):
if "module.v" in name:
UpperCAmelCase = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
UpperCAmelCase = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
UpperCAmelCase = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
UpperCAmelCase = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
UpperCAmelCase = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
UpperCAmelCase = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
UpperCAmelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
UpperCAmelCase = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
UpperCAmelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
UpperCAmelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
UpperCAmelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
UpperCAmelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
UpperCAmelCase = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
UpperCAmelCase = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
UpperCAmelCase = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def snake_case_ (_a : Dict , _a : List[Any] ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(_a )
if "qkv" in key:
UpperCAmelCase = key.split('''.''' )
UpperCAmelCase = int(key_split[3] )
UpperCAmelCase = config.hidden_size
if "weight" in key:
UpperCAmelCase = val[:dim, :]
UpperCAmelCase = val[dim : dim * 2, :]
UpperCAmelCase = val[-dim:, :]
else:
UpperCAmelCase = val[:dim]
UpperCAmelCase = val[dim : dim * 2]
UpperCAmelCase = val[-dim:]
else:
UpperCAmelCase = val
return orig_state_dict
def snake_case_ (_a : Tuple ):
UpperCAmelCase = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(_a , _a )
@torch.no_grad()
def snake_case_ (_a : int , _a : Union[str, Any] , _a : Dict=False ):
UpperCAmelCase = get_audio_spectrogram_transformer_config(_a )
UpperCAmelCase = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
UpperCAmelCase = model_name_to_url[model_name]
UpperCAmelCase = torch.hub.load_state_dict_from_url(_a , map_location='''cpu''' )
# remove some keys
remove_keys(_a )
# rename some keys
UpperCAmelCase = convert_state_dict(_a , _a )
# load 🤗 model
UpperCAmelCase = ASTForAudioClassification(_a )
model.eval()
model.load_state_dict(_a )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
UpperCAmelCase = -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
UpperCAmelCase = 4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
UpperCAmelCase = 1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
UpperCAmelCase = ASTFeatureExtractor(mean=_a , std=_a , max_length=_a )
if "speech-commands" in model_name:
UpperCAmelCase = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
UpperCAmelCase = dataset[0]['''audio''']['''array''']
else:
UpperCAmelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
UpperCAmelCase , UpperCAmelCase = torchaudio.load(_a )
UpperCAmelCase = waveform.squeeze().numpy()
UpperCAmelCase = feature_extractor(_a , sampling_rate=1_6_0_0_0 , return_tensors='''pt''' )
# forward pass
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
UpperCAmelCase = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
UpperCAmelCase = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
UpperCAmelCase = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
UpperCAmelCase = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
UpperCAmelCase = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
UpperCAmelCase = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
UpperCAmelCase = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
UpperCAmelCase = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , _a , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(F"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_a )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(F"MIT/{model_name}" )
feature_extractor.push_to_hub(F"MIT/{model_name}" )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub.'
)
A =parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'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 ( __a ):
__a : List[str] = """gpt_neox"""
def __init__( self : Any , lowercase : Any=50_432 , lowercase : List[Any]=6_144 , lowercase : List[Any]=44 , lowercase : Dict=64 , lowercase : Any=24_576 , lowercase : str="gelu" , lowercase : Union[str, Any]=0.25 , lowercase : List[str]=10_000 , lowercase : Optional[int]=0.0 , lowercase : Optional[int]=0.0 , lowercase : int=0.1 , lowercase : Optional[int]=2_048 , lowercase : str=0.02 , lowercase : int=1E-5 , lowercase : List[str]=True , lowercase : int=0 , lowercase : Tuple=2 , lowercase : List[Any]=False , lowercase : Tuple=True , lowercase : Any=None , **lowercase : Optional[Any] , ):
'''simple docstring'''
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
UpperCAmelCase = vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = rotary_pct
UpperCAmelCase = rotary_emb_base
UpperCAmelCase = attention_dropout
UpperCAmelCase = hidden_dropout
UpperCAmelCase = classifier_dropout
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = use_cache
UpperCAmelCase = tie_word_embeddings
UpperCAmelCase = use_parallel_residual
UpperCAmelCase = 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 A ( self : Union[str, Any] ):
'''simple docstring'''
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}" )
UpperCAmelCase = self.rope_scaling.get('''type''' , lowercase )
UpperCAmelCase = 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}" )
| 34
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
UpperCAmelCase = m
else:
UpperCAmelCase = m # noqa: E741
return r
def snake_case_ (_a : list[int] ):
if len(_a ) == 0:
return 0
UpperCAmelCase = [0] * len(_a )
UpperCAmelCase = 1
UpperCAmelCase = v[0]
for i in range(1 , len(_a ) ):
if v[i] < tail[0]:
UpperCAmelCase = v[i]
elif v[i] > tail[length - 1]:
UpperCAmelCase = v[i]
length += 1
else:
UpperCAmelCase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : int , _a : int ):
return base * power(_a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
A =int(input('Enter the base: ').strip())
A =int(input('Enter the exponent: ').strip())
A =power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
A =1 / result
print(f"""{base} to the power of {exponent} is {result}""")
| 34
|
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase = [[0 for i in range(_a )] for j in range(_a )]
# since string of zero length match pattern of zero length
UpperCAmelCase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _a ):
UpperCAmelCase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _a ):
UpperCAmelCase = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _a ):
for j in range(1 , _a ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase = dp[i - 1][j]
else:
UpperCAmelCase = 0
else:
UpperCAmelCase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A ='aab'
A ='c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"""{input_string} matches the given pattern {pattern}""")
else:
print(f"""{input_string} does not match with the given pattern {pattern}""")
| 34
| 1
|
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
A =get_tests_dir('fixtures/test_sentencepiece_bpe.model')
class _a ( __a , unittest.TestCase ):
__a : List[Any] = BartphoTokenizer
__a : str = False
__a : str = True
def A ( self : Any ):
'''simple docstring'''
super().setUp()
UpperCAmelCase = ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
UpperCAmelCase = dict(zip(lowercase , range(len(lowercase ) ) ) )
UpperCAmelCase = {'''unk_token''': '''<unk>'''}
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''monolingual_vocab_file'''] )
with open(self.monolingual_vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n" )
UpperCAmelCase = BartphoTokenizer(lowercase , self.monolingual_vocab_file , **self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def A ( self : Tuple , **lowercase : List[str] ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname , **lowercase )
def A ( self : Dict , lowercase : int ):
'''simple docstring'''
UpperCAmelCase = '''This is a là test'''
UpperCAmelCase = '''This is a<unk><unk> test'''
return input_text, output_text
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = BartphoTokenizer(lowercase , self.monolingual_vocab_file , **self.special_tokens_map )
UpperCAmelCase = '''This is a là test'''
UpperCAmelCase = '''▁This ▁is ▁a ▁l à ▁t est'''.split()
UpperCAmelCase = tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
UpperCAmelCase = tokens + [tokenizer.unk_token]
UpperCAmelCase = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , lowercase )
| 34
|
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A ='pt'
elif is_tf_available():
A ='tf'
else:
A ='jax'
class _a ( __a , unittest.TestCase ):
__a : Optional[Any] = PerceiverTokenizer
__a : str = False
def A ( self : Union[str, Any] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A ( self : Optional[int] ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def A ( self : Union[str, Any] , **lowercase : int ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[str]=False , lowercase : Union[str, Any]=20 , lowercase : Union[str, Any]=5 ):
'''simple docstring'''
UpperCAmelCase = []
for i in range(len(lowercase ) ):
try:
UpperCAmelCase = tokenizer.decode([i] , clean_up_tokenization_spaces=lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
UpperCAmelCase = list(filter(lambda lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , lowercase ) )
UpperCAmelCase = list(filter(lambda lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=lowercase ) , lowercase ) )
if max_length is not None and len(lowercase ) > max_length:
UpperCAmelCase = toks[:max_length]
if min_length is not None and len(lowercase ) < min_length and len(lowercase ) > 0:
while len(lowercase ) < min_length:
UpperCAmelCase = toks + toks
# toks_str = [t[1] for t in toks]
UpperCAmelCase = [t[0] for t in toks]
# Ensure consistency
UpperCAmelCase = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
if " " not in output_txt and len(lowercase ) > 1:
UpperCAmelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=lowercase )
)
if with_prefix_space:
UpperCAmelCase = ''' ''' + output_txt
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
return output_txt, output_ids
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = '''Unicode €.'''
UpperCAmelCase = tokenizer(lowercase )
UpperCAmelCase = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]Unicode €.[SEP]''' )
UpperCAmelCase = tokenizer('''e è é ê ë''' )
UpperCAmelCase = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
UpperCAmelCase = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
self.assertIsInstance(lowercase , lowercase )
if FRAMEWORK != "jax":
UpperCAmelCase = list(batch.input_ids.numpy()[0] )
else:
UpperCAmelCase = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowercase , lowercase )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , lowercase )
self.assertIn('''attention_mask''' , lowercase )
self.assertNotIn('''decoder_input_ids''' , lowercase )
self.assertNotIn('''decoder_attention_mask''' , lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = [
'''Summary of the text.''',
'''Another summary.''',
]
UpperCAmelCase = tokenizer(
text_target=lowercase , max_length=32 , padding='''max_length''' , truncation=lowercase , return_tensors=lowercase )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
shutil.rmtree(lowercase )
UpperCAmelCase = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
UpperCAmelCase = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowercase )
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
UpperCAmelCase = [f"<extra_id_{i}>" for i in range(125 )]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['''an_additional_special_token'''] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
UpperCAmelCase = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=lowercase )]
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , additional_special_tokens=lowercase , )
self.assertIn('''a_new_additional_special_token''' , tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ) , )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ) , '''�''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
pass
def A ( self : Any ):
'''simple docstring'''
pass
def A ( self : Dict ):
'''simple docstring'''
pass
def A ( self : str ):
'''simple docstring'''
pass
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers(fast=lowercase , do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
UpperCAmelCase = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
UpperCAmelCase = tokenizer.convert_tokens_to_string(lowercase )
self.assertIsInstance(lowercase , lowercase )
| 34
| 1
|
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A =pd.read_csv('sample_data.csv', header=None)
A =df.shape[:1][0]
# If you're using some other dataset input the target column
A =df.iloc[:, 1:2]
A =actual_data.values.reshape(len_data, 1)
A =MinMaxScaler().fit_transform(actual_data)
A =10
A =5
A =20
A =len_data - periods * look_back
A =actual_data[:division]
A =actual_data[division - look_back :]
A , A =[], []
A , A =[], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
A =np.array(train_x)
A =np.array(test_x)
A =np.array([list(i.ravel()) for i in train_y])
A =np.array([list(i.ravel()) for i in test_y])
A =Sequential()
model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(1_28, 1)))
model.add(Dense(forward_days))
model.compile(loss='mean_squared_error', optimizer='adam')
A =model.fit(
x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
)
A =model.predict(x_test)
| 34
|
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return val
return default
def snake_case_ (_a : Dict , _a : Any=False ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return strtobool(_a ) == 1 # As its name indicates `strtobool` actually returns an int...
def snake_case_ (_a : str , _a : Optional[Any]="no" ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return value
| 34
| 1
|
'''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 _a ( __a ):
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = 5
# Realm tok
UpperCAmelCase = [
'''[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 = os.path.join(self.tmpdirname , '''realm_tokenizer''' )
os.makedirs(lowercase , exist_ok=lowercase )
UpperCAmelCase = 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 = os.path.join(self.tmpdirname , '''realm_block_records''' )
os.makedirs(lowercase , exist_ok=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''realm_tokenizer''' ) )
def A ( self : int ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = RealmConfig(num_block_records=self.num_block_records )
return config
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''question''': ['''foo''', '''bar'''],
'''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
} )
return dataset
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = 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 A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = RealmRetriever(
block_records=self.get_dummy_block_records() , tokenizer=self.get_tokenizer() , )
return retriever
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.get_config()
UpperCAmelCase = self.get_dummy_retriever()
UpperCAmelCase = retriever.tokenizer
UpperCAmelCase = np.array([0, 3] , dtype='''long''' )
UpperCAmelCase = tokenizer(['''Test question'''] ).input_ids
UpperCAmelCase = tokenizer(
['''the fourth'''] , add_special_tokens=lowercase , return_token_type_ids=lowercase , return_attention_mask=lowercase , ).input_ids
UpperCAmelCase = config.reader_seq_len
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 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 A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.get_config()
UpperCAmelCase = self.get_dummy_retriever()
UpperCAmelCase = retriever.tokenizer
UpperCAmelCase = np.array([0, 3, 5] , dtype='''long''' )
UpperCAmelCase = tokenizer(['''Test question'''] ).input_ids
UpperCAmelCase = tokenizer(
['''the fourth''', '''longer longer'''] , add_special_tokens=lowercase , return_token_type_ids=lowercase , return_attention_mask=lowercase , ).input_ids
UpperCAmelCase = config.reader_seq_len
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 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 A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname , '''realm_block_records''' ) )
# Test local path
UpperCAmelCase = 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 = os.path.join(
os.path.join(self.tmpdirname , '''realm_block_records''' ) , _REALM_BLOCK_RECORDS_FILENAME )
UpperCAmelCase = RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa''' )
self.assertEqual(retriever.block_records[0] , B'''This is the first record''' )
| 34
|
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A =logging.getLogger(__name__)
A ='Hello world! cécé herlolip'
A =namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def snake_case_ (_a : List[Any] , _a : Any ):
UpperCAmelCase = BertAbsConfig(
temp_dir='''.''' , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder='''bert''' , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
UpperCAmelCase = torch.load(_a , lambda _a , _a : storage )
UpperCAmelCase = AbsSummarizer(_a , torch.device('''cpu''' ) , _a )
original.eval()
UpperCAmelCase = BertAbsSummarizer(_a , torch.device('''cpu''' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('''convert the model''' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('''Make sure that the models\' outputs are identical''' )
UpperCAmelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
# prepare the model inputs
UpperCAmelCase = tokenizer.encode('''This is sample éàalj\'-.''' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
UpperCAmelCase = tokenizer.encode('''This is sample 3 éàalj\'-.''' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
UpperCAmelCase = encoder_input_ids
UpperCAmelCase = decoder_input_ids
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
UpperCAmelCase = original(_a , _a , _a , _a , _a , _a , _a )[0]
UpperCAmelCase = original.generator(_a )
UpperCAmelCase = new_model(
_a , _a , _a , _a , _a )[0]
UpperCAmelCase = new_model.generator(_a )
UpperCAmelCase = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.allclose(_a , _a , atol=1E-3 )
if are_identical:
logging.info('''all weights are equal up to 1e-3''' )
else:
raise ValueError('''the weights are different. The new model is likely different from the original one.''' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('''saving the model\'s state dictionary''' )
torch.save(
new_model.state_dict() , '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''' )
if __name__ == "__main__":
A =argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
A =parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 34
| 1
|
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
A =logging.get_logger(__name__) # pylint: disable=invalid-name
A ='\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n >>> pipe_prior.to("cuda")\n\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n >>> pipe.to("cuda")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save("cat.png")\n ```\n'
def snake_case_ (_a : Dict , _a : Optional[int] , _a : Any=8 ):
UpperCAmelCase = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
UpperCAmelCase = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class _a ( __a ):
def __init__( self : List[Any] , lowercase : MultilingualCLIP , lowercase : XLMRobertaTokenizer , lowercase : UNetaDConditionModel , lowercase : Union[DDIMScheduler, DDPMScheduler] , lowercase : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
text_encoder=lowercase , tokenizer=lowercase , unet=lowercase , scheduler=lowercase , movq=lowercase , )
UpperCAmelCase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def A ( self : int , lowercase : List[Any] , lowercase : int , lowercase : Dict , lowercase : Optional[int] , lowercase : Optional[Any] , lowercase : str ):
'''simple docstring'''
if latents is None:
UpperCAmelCase = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase )
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}" )
UpperCAmelCase = latents.to(lowercase )
UpperCAmelCase = latents * scheduler.init_noise_sigma
return latents
def A ( self : List[str] , lowercase : Union[str, Any] , lowercase : Optional[Any] , lowercase : int , lowercase : str , lowercase : Dict=None , ):
'''simple docstring'''
UpperCAmelCase = len(lowercase ) if isinstance(lowercase , lowercase ) else 1
# get prompt text embeddings
UpperCAmelCase = self.tokenizer(
lowercase , padding='''max_length''' , truncation=lowercase , max_length=77 , return_attention_mask=lowercase , add_special_tokens=lowercase , return_tensors='''pt''' , )
UpperCAmelCase = text_inputs.input_ids
UpperCAmelCase = self.tokenizer(lowercase , padding='''longest''' , return_tensors='''pt''' ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase , lowercase ):
UpperCAmelCase = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f" {self.tokenizer.model_max_length} tokens: {removed_text}" )
UpperCAmelCase = text_input_ids.to(lowercase )
UpperCAmelCase = text_inputs.attention_mask.to(lowercase )
UpperCAmelCase , UpperCAmelCase = self.text_encoder(
input_ids=lowercase , attention_mask=lowercase )
UpperCAmelCase = prompt_embeds.repeat_interleave(lowercase , dim=0 )
UpperCAmelCase = text_encoder_hidden_states.repeat_interleave(lowercase , dim=0 )
UpperCAmelCase = text_mask.repeat_interleave(lowercase , dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase = 42
if negative_prompt is None:
UpperCAmelCase = [''''''] * batch_size
elif type(lowercase ) is not type(lowercase ):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(lowercase )} !="
f" {type(lowercase )}." )
elif isinstance(lowercase , lowercase ):
UpperCAmelCase = [negative_prompt]
elif batch_size != len(lowercase ):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(lowercase )}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
''' the batch size of `prompt`.''' )
else:
UpperCAmelCase = negative_prompt
UpperCAmelCase = self.tokenizer(
lowercase , padding='''max_length''' , max_length=77 , truncation=lowercase , return_attention_mask=lowercase , add_special_tokens=lowercase , return_tensors='''pt''' , )
UpperCAmelCase = uncond_input.input_ids.to(lowercase )
UpperCAmelCase = uncond_input.attention_mask.to(lowercase )
UpperCAmelCase , UpperCAmelCase = self.text_encoder(
input_ids=lowercase , attention_mask=lowercase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase = negative_prompt_embeds.shape[1]
UpperCAmelCase = negative_prompt_embeds.repeat(1 , lowercase )
UpperCAmelCase = negative_prompt_embeds.view(batch_size * num_images_per_prompt , lowercase )
UpperCAmelCase = uncond_text_encoder_hidden_states.shape[1]
UpperCAmelCase = uncond_text_encoder_hidden_states.repeat(1 , lowercase , 1 )
UpperCAmelCase = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , lowercase , -1 )
UpperCAmelCase = uncond_text_mask.repeat_interleave(lowercase , dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase = torch.cat([negative_prompt_embeds, prompt_embeds] )
UpperCAmelCase = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
UpperCAmelCase = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def A ( self : Any , lowercase : str=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
UpperCAmelCase = torch.device(f"cuda:{gpu_id}" )
UpperCAmelCase = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowercase , lowercase )
def A ( self : str , lowercase : str=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
UpperCAmelCase = torch.device(f"cuda:{gpu_id}" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=lowercase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
UpperCAmelCase = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
UpperCAmelCase , UpperCAmelCase = cpu_offload_with_hook(lowercase , lowercase , prev_module_hook=lowercase )
if self.safety_checker is not None:
UpperCAmelCase , UpperCAmelCase = cpu_offload_with_hook(self.safety_checker , lowercase , prev_module_hook=lowercase )
# We'll offload the last model manually.
UpperCAmelCase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A ( self : str ):
'''simple docstring'''
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowercase , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowercase )
def __call__( self : Optional[Any] , lowercase : Union[str, List[str]] , lowercase : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowercase : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowercase : Optional[Union[str, List[str]]] = None , lowercase : int = 512 , lowercase : int = 512 , lowercase : int = 100 , lowercase : float = 4.0 , lowercase : int = 1 , lowercase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase : Optional[torch.FloatTensor] = None , lowercase : Optional[str] = "pil" , lowercase : bool = True , ):
'''simple docstring'''
if isinstance(lowercase , lowercase ):
UpperCAmelCase = 1
elif isinstance(lowercase , lowercase ):
UpperCAmelCase = len(lowercase )
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(lowercase )}" )
UpperCAmelCase = self._execution_device
UpperCAmelCase = batch_size * num_images_per_prompt
UpperCAmelCase = guidance_scale > 1.0
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._encode_prompt(
lowercase , lowercase , lowercase , lowercase , lowercase )
if isinstance(lowercase , lowercase ):
UpperCAmelCase = torch.cat(lowercase , dim=0 )
if isinstance(lowercase , lowercase ):
UpperCAmelCase = torch.cat(lowercase , dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase = image_embeds.repeat_interleave(lowercase , dim=0 )
UpperCAmelCase = negative_image_embeds.repeat_interleave(lowercase , dim=0 )
UpperCAmelCase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=lowercase )
self.scheduler.set_timesteps(lowercase , device=lowercase )
UpperCAmelCase = self.scheduler.timesteps
UpperCAmelCase = self.unet.config.in_channels
UpperCAmelCase , UpperCAmelCase = get_new_h_w(lowercase , lowercase , self.movq_scale_factor )
# create initial latent
UpperCAmelCase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , lowercase , lowercase , lowercase , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowercase ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase = {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
UpperCAmelCase = self.unet(
sample=lowercase , timestep=lowercase , encoder_hidden_states=lowercase , added_cond_kwargs=lowercase , return_dict=lowercase , )[0]
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
UpperCAmelCase , UpperCAmelCase = noise_pred.chunk(2 )
UpperCAmelCase , UpperCAmelCase = variance_pred.chunk(2 )
UpperCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
UpperCAmelCase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
UpperCAmelCase , UpperCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase = self.scheduler.step(
lowercase , lowercase , lowercase , generator=lowercase , ).prev_sample
# post-processing
UpperCAmelCase = self.movq.decode(lowercase , force_not_quantize=lowercase )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )
if output_type in ["np", "pil"]:
UpperCAmelCase = image * 0.5 + 0.5
UpperCAmelCase = image.clamp(0 , 1 )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase = self.numpy_to_pil(lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase )
| 34
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : List[Any] , **lowercase : List[str] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : int ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : int , *lowercase : Tuple , **lowercase : Dict ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : List[str] , *lowercase : Optional[int] , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Union[str, Any] , **lowercase : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[int] , *lowercase : Union[str, Any] , **lowercase : Any ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Any , *lowercase : Dict , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : Any = ["""flax""", """transformers"""]
def __init__( self : Any , *lowercase : Optional[Any] , **lowercase : Optional[int] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Optional[Any] , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : str , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
| 34
| 1
|
'''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/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class _a ( __a ):
__a : List[str] = """mobilenet_v2"""
def __init__( self : List[Any] , lowercase : Any=3 , lowercase : Tuple=224 , lowercase : Dict=1.0 , lowercase : Union[str, Any]=8 , lowercase : str=8 , lowercase : Dict=6 , lowercase : Dict=32 , lowercase : Optional[int]=True , lowercase : Any=True , lowercase : List[Any]="relu6" , lowercase : Tuple=True , lowercase : Dict=0.8 , lowercase : int=0.02 , lowercase : List[Any]=0.001 , lowercase : Union[str, Any]=255 , **lowercase : List[str] , ):
'''simple docstring'''
super().__init__(**lowercase )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
UpperCAmelCase = num_channels
UpperCAmelCase = image_size
UpperCAmelCase = depth_multiplier
UpperCAmelCase = depth_divisible_by
UpperCAmelCase = min_depth
UpperCAmelCase = expand_ratio
UpperCAmelCase = output_stride
UpperCAmelCase = first_layer_is_expansion
UpperCAmelCase = finegrained_output
UpperCAmelCase = hidden_act
UpperCAmelCase = tf_padding
UpperCAmelCase = classifier_dropout_prob
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = semantic_loss_ignore_index
class _a ( __a ):
__a : List[Any] = version.parse("""1.11""" )
@property
def A ( self : str ):
'''simple docstring'''
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def A ( self : Dict ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def A ( self : str ):
'''simple docstring'''
return 1E-4
| 34
|
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
A ={
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
A ={
'b0': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 2_24,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 2_40,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 14_08,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 2_60,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 15_36,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 3_00,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 17_92,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 3_80,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 20_48,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 4_56,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 23_04,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 5_28,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 25_60,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 6_00,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def snake_case_ (_a : List[str] ):
UpperCAmelCase = EfficientNetConfig()
UpperCAmelCase = CONFIG_MAP[model_name]['''hidden_dim''']
UpperCAmelCase = CONFIG_MAP[model_name]['''width_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''depth_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dropout_rate''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dw_padding''']
UpperCAmelCase = '''huggingface/label-files'''
UpperCAmelCase = '''imagenet-1k-id2label.json'''
UpperCAmelCase = 1_0_0_0
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ():
UpperCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase = Image.open(requests.get(_a , stream=_a ).raw )
return im
def snake_case_ (_a : str ):
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163] , do_center_crop=_a , )
return preprocessor
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
UpperCAmelCase = sorted(set(_a ) )
UpperCAmelCase = len(_a )
UpperCAmelCase = {b: str(_a ) for b, i in zip(_a , range(_a ) )}
UpperCAmelCase = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
UpperCAmelCase = block_name_mapping[b]
rename_keys.append((F"block{b}_expand_conv/kernel:0", F"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((F"block{b}_expand_bn/gamma:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((F"block{b}_expand_bn/beta:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(F"block{b}_expand_bn/moving_mean:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(F"block{b}_expand_bn/moving_variance:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(F"block{b}_dwconv/depthwise_kernel:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((F"block{b}_bn/gamma:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((F"block{b}_bn/beta:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(F"block{b}_bn/moving_mean:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(F"block{b}_bn/moving_variance:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((F"block{b}_se_reduce/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((F"block{b}_se_reduce/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((F"block{b}_se_expand/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((F"block{b}_se_expand/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(F"block{b}_project_conv/kernel:0", F"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((F"block{b}_project_bn/gamma:0", F"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((F"block{b}_project_bn/beta:0", F"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(F"block{b}_project_bn/moving_mean:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(F"block{b}_project_bn/moving_variance:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
UpperCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCAmelCase = '''efficientnet.''' + item[1]
UpperCAmelCase = '''classifier.weight'''
UpperCAmelCase = '''classifier.bias'''
return key_mapping
def snake_case_ (_a : Dict , _a : List[str] , _a : Dict ):
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
UpperCAmelCase = torch.from_numpy(np.transpose(_a ) )
else:
UpperCAmelCase = torch.from_numpy(_a )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_a )
@torch.no_grad()
def snake_case_ (_a : Optional[Any] , _a : List[str] , _a : Optional[int] , _a : Dict ):
UpperCAmelCase = model_classes[model_name](
include_top=_a , weights='''imagenet''' , input_tensor=_a , input_shape=_a , pooling=_a , classes=1_0_0_0 , classifier_activation='''softmax''' , )
UpperCAmelCase = original_model.trainable_variables
UpperCAmelCase = original_model.non_trainable_variables
UpperCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCAmelCase = param.numpy()
UpperCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
UpperCAmelCase = get_efficientnet_config(_a )
UpperCAmelCase = EfficientNetForImageClassification(_a ).eval()
UpperCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
UpperCAmelCase = rename_keys(_a )
replace_params(_a , _a , _a )
# Initialize preprocessor and preprocess input image
UpperCAmelCase = convert_image_processor(_a )
UpperCAmelCase = preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCAmelCase = hf_model(**_a )
UpperCAmelCase = outputs.logits.detach().numpy()
# Original model inference
UpperCAmelCase = False
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
UpperCAmelCase = image.img_to_array(_a )
UpperCAmelCase = np.expand_dims(_a , axis=0 )
UpperCAmelCase = original_model.predict(_a )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_a , _a , atol=1E-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(_a ):
os.mkdir(_a )
# Save converted model and image processor
hf_model.save_pretrained(_a )
preprocessor.save_pretrained(_a )
if push_to_hub:
# Push model and image processor to hub
print(F"Pushing converted {model_name} to the hub..." )
UpperCAmelCase = F"efficientnet-{model_name}"
preprocessor.push_to_hub(_a )
hf_model.push_to_hub(_a )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
A =parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def snake_case_ ():
UpperCAmelCase = 1_0
UpperCAmelCase = datasets.Features(
{
'''tokens''': datasets.Sequence(datasets.Value('''string''' ) ),
'''labels''': datasets.Sequence(datasets.ClassLabel(names=['''negative''', '''positive'''] ) ),
'''answers''': datasets.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
'''id''': datasets.Value('''int64''' ),
} )
UpperCAmelCase = datasets.Dataset.from_dict(
{
'''tokens''': [['''foo'''] * 5] * n,
'''labels''': [[1] * 5] * n,
'''answers''': [{'''answer_start''': [9_7], '''text''': ['''1976''']}] * 1_0,
'''id''': list(range(_a ) ),
} , features=_a , )
return dataset
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : List[str] , _a : Optional[int] ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''file.arrow''' )
dataset.map(cache_file_name=_a )
return filename
# FILE_CONTENT + files
A ='\\n Text data.\n Second line of data.'
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.txt'''
UpperCAmelCase = FILE_CONTENT
with open(_a , '''w''' ) as f:
f.write(_a )
return filename
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : int ):
import bza
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.txt.bz2'''
UpperCAmelCase = bytes(_a , '''utf-8''' )
with bza.open(_a , '''wb''' ) as f:
f.write(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Tuple ):
import gzip
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''file.txt.gz''' )
UpperCAmelCase = bytes(_a , '''utf-8''' )
with gzip.open(_a , '''wb''' ) as f:
f.write(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Any ):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.txt.lz4'''
UpperCAmelCase = bytes(_a , '''utf-8''' )
with lza.frame.open(_a , '''wb''' ) as f:
f.write(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Union[str, Any] , _a : Optional[Any] ):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.txt.7z'''
with pyazr.SevenZipFile(_a , '''w''' ) as archive:
archive.write(_a , arcname=os.path.basename(_a ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Tuple , _a : Any ):
import tarfile
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.txt.tar'''
with tarfile.TarFile(_a , '''w''' ) as f:
f.add(_a , arcname=os.path.basename(_a ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[Any] ):
import lzma
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.txt.xz'''
UpperCAmelCase = bytes(_a , '''utf-8''' )
with lzma.open(_a , '''wb''' ) as f:
f.write(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : str , _a : Optional[int] ):
import zipfile
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.basename(_a ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Any ):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zst'''
UpperCAmelCase = bytes(_a , '''utf-8''' )
with zstd.open(_a , '''wb''' ) as f:
f.write(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Dict ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''file.xml'''
UpperCAmelCase = textwrap.dedent(
'''\
<?xml version="1.0" encoding="UTF-8" ?>
<tmx version="1.4">
<header segtype="sentence" srclang="ca" />
<body>
<tu>
<tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>
<tuv xml:lang="en"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>
<tuv xml:lang="en"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>
<tuv xml:lang="en"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>
<tuv xml:lang="en"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>
<tuv xml:lang="en"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>''' )
with open(_a , '''w''' ) as f:
f.write(_a )
return filename
A =[
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
A =[
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
A ={
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
A =[
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
A =[
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope='''session''' )
def snake_case_ ():
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Dict ):
UpperCAmelCase = datasets.Dataset.from_dict(_a )
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.arrow''' )
dataset.map(cache_file_name=_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.sqlite''' )
with contextlib.closing(sqlitea.connect(_a ) ) as con:
UpperCAmelCase = con.cursor()
cur.execute('''CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)''' )
for item in DATA:
cur.execute('''INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)''' , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Dict ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.csv''' )
with open(_a , '''w''' , newline='''''' ) as f:
UpperCAmelCase = csv.DictWriter(_a , fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Dict ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.csv''' )
with open(_a , '''w''' , newline='''''' ) as f:
UpperCAmelCase = csv.DictWriter(_a , fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Any , _a : Tuple ):
import bza
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.bz2'''
with open(_a , '''rb''' ) as f:
UpperCAmelCase = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(_a , '''wb''' ) as f:
f.write(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Tuple , _a : List[Any] , _a : int ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.basename(_a ) )
f.write(_a , arcname=os.path.basename(_a ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : str , _a : Union[str, Any] , _a : Optional[Any] ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.basename(csv_path.replace('''.csv''' , '''.CSV''' ) ) )
f.write(_a , arcname=os.path.basename(csva_path.replace('''.csv''' , '''.CSV''' ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[int] , _a : Optional[Any] , _a : Union[str, Any] ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.csv.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.join('''main_dir''' , os.path.basename(_a ) ) )
f.write(_a , arcname=os.path.join('''main_dir''' , os.path.basename(_a ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : List[str] ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.parquet''' )
UpperCAmelCase = pa.schema(
{
'''col_1''': pa.string(),
'''col_2''': pa.intaa(),
'''col_3''': pa.floataa(),
} )
with open(_a , '''wb''' ) as f:
UpperCAmelCase = pq.ParquetWriter(_a , schema=_a )
UpperCAmelCase = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_a ) )] for k in DATA[0]} , schema=_a )
writer.write_table(_a )
writer.close()
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : int ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
UpperCAmelCase = {'''data''': DATA}
with open(_a , '''w''' ) as f:
json.dump(_a , _a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Dict ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
UpperCAmelCase = {'''data''': DATA_DICT_OF_LISTS}
with open(_a , '''w''' ) as f:
json.dump(_a , _a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[int] ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl''' )
with open(_a , '''w''' ) as f:
for item in DATA:
f.write(json.dumps(_a ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.jsonl''' )
with open(_a , '''w''' ) as f:
for item in DATA:
f.write(json.dumps(_a ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : List[str] ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_312.jsonl''' )
with open(_a , '''w''' ) as f:
for item in DATA_312:
f.write(json.dumps(_a ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : str ):
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset-str.jsonl''' )
with open(_a , '''w''' ) as f:
for item in DATA_STR:
f.write(json.dumps(_a ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Dict , _a : str ):
import gzip
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt.gz''' )
with open(_a , '''rb''' ) as orig_file:
with gzip.open(_a , '''wb''' ) as zipped_file:
zipped_file.writelines(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[Any] , _a : int ):
import gzip
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.gz''' )
with open(_a , '''rb''' ) as orig_file:
with gzip.open(_a , '''wb''' ) as zipped_file:
zipped_file.writelines(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Dict , _a : Optional[int] , _a : str ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.basename(_a ) )
f.write(_a , arcname=os.path.basename(_a ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Union[str, Any] , _a : Dict , _a : Union[str, Any] , _a : Any ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.join('''nested''' , os.path.basename(_a ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Union[str, Any] , _a : List[str] , _a : int ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.jsonl.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.join('''main_dir''' , os.path.basename(_a ) ) )
f.write(_a , arcname=os.path.join('''main_dir''' , os.path.basename(_a ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Dict , _a : Dict , _a : List[str] ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.tar'''
with tarfile.TarFile(_a , '''w''' ) as f:
f.add(_a , arcname=os.path.basename(_a ) )
f.add(_a , arcname=os.path.basename(_a ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Tuple , _a : Tuple , _a : List[Any] , _a : Tuple ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.tar'''
with tarfile.TarFile(_a , '''w''' ) as f:
f.add(_a , arcname=os.path.join('''nested''' , os.path.basename(_a ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : str ):
UpperCAmelCase = ['''0''', '''1''', '''2''', '''3''']
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt''' )
with open(_a , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = ['''0''', '''1''', '''2''', '''3''']
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.txt''' )
with open(_a , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[int] ):
UpperCAmelCase = ['''0''', '''1''', '''2''', '''3''']
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.abc'''
with open(_a , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[Any] , _a : Union[str, Any] , _a : int ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.text.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.basename(_a ) )
f.write(_a , arcname=os.path.basename(_a ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : List[Any] , _a : Tuple , _a : str ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.text.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.join('''main_dir''' , os.path.basename(_a ) ) )
f.write(_a , arcname=os.path.join('''main_dir''' , os.path.basename(_a ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : str , _a : int , _a : Optional[int] ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.ext.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.basename('''unsupported.ext''' ) )
f.write(_a , arcname=os.path.basename('''unsupported_2.ext''' ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : List[str] ):
UpperCAmelCase = '''\n'''.join(['''First''', '''Second\u2029with Unicode new line''', '''Third'''] )
UpperCAmelCase = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_with_unicode_new_lines.txt''' )
with open(_a , '''w''' , encoding='''utf-8''' ) as f:
f.write(_a )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ ():
return os.path.join('''tests''' , '''features''' , '''data''' , '''test_image_rgb.jpg''' )
@pytest.fixture(scope='''session''' )
def snake_case_ ():
return os.path.join('''tests''' , '''features''' , '''data''' , '''test_audio_44100.wav''' )
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : Optional[Any] , _a : Union[str, Any] ):
UpperCAmelCase = tmp_path_factory.mktemp('''data''' ) / '''dataset.img.zip'''
with zipfile.ZipFile(_a , '''w''' ) as f:
f.write(_a , arcname=os.path.basename(_a ) )
f.write(_a , arcname=os.path.basename(_a ).replace('''.jpg''' , '''2.jpg''' ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case_ (_a : str ):
UpperCAmelCase = tmp_path_factory.mktemp('''data_dir''' )
(data_dir / "subdir").mkdir()
with open(data_dir / '''subdir''' / '''train.txt''' , '''w''' ) as f:
f.write('''foo\n''' * 1_0 )
with open(data_dir / '''subdir''' / '''test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 1_0 )
# hidden file
with open(data_dir / '''subdir''' / '''.test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '''.subdir''' / '''train.txt''' , '''w''' ) as f:
f.write('''foo\n''' * 1_0 )
with open(data_dir / '''.subdir''' / '''test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 1_0 )
return data_dir
| 34
|
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
A =soup.find('meta', {'property': 'og:image'})['content']
A =requests.get(image_url).content
A =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(f"""Done. Image saved to disk as {file_name}.""")
| 34
| 1
|
'''simple docstring'''
class _a :
def __init__( self : int ):
'''simple docstring'''
UpperCAmelCase = {}
def A ( self : Tuple ):
'''simple docstring'''
print(self.vertex )
for i in self.vertex:
print(lowercase , ''' -> ''' , ''' -> '''.join([str(lowercase ) for j in self.vertex[i]] ) )
def A ( self : str , lowercase : int , lowercase : int ):
'''simple docstring'''
if from_vertex in self.vertex:
self.vertex[from_vertex].append(lowercase )
else:
# else make a new vertex
UpperCAmelCase = [to_vertex]
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(lowercase , lowercase )
def A ( self : Dict , lowercase : int , lowercase : list ):
'''simple docstring'''
UpperCAmelCase = True
print(lowercase , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(lowercase , lowercase )
if __name__ == "__main__":
A =Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('DFS:')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 34
|
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _a ( __a ):
__a : str = ["""vqvae"""]
def __init__( self : str , lowercase : AutoencoderKL , lowercase : UNetaDConditionModel , lowercase : Mel , lowercase : Union[DDIMScheduler, DDPMScheduler] , ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowercase , scheduler=lowercase , mel=lowercase , vqvae=lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
return 50 if isinstance(self.scheduler , lowercase ) else 1_000
@torch.no_grad()
def __call__( self : Optional[Any] , lowercase : int = 1 , lowercase : str = None , lowercase : np.ndarray = None , lowercase : int = 0 , lowercase : int = 0 , lowercase : int = None , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : float = 0 , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : torch.Tensor = None , lowercase : torch.Tensor = None , lowercase : Tuple=True , ):
'''simple docstring'''
UpperCAmelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowercase , device=self.device , )
UpperCAmelCase = noise
UpperCAmelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowercase , lowercase )
UpperCAmelCase = self.mel.audio_slice_to_image(lowercase )
UpperCAmelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase = (input_image / 255) * 2 - 1
UpperCAmelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase = self.vqvae.encode(torch.unsqueeze(lowercase , 0 ) ).latent_dist.sample(
generator=lowercase )[0]
UpperCAmelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase = int(mask_start_secs * pixels_per_second )
UpperCAmelCase = int(mask_end_secs * pixels_per_second )
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowercase ):
UpperCAmelCase = self.unet(lowercase , lowercase , lowercase )['''sample''']
else:
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
if isinstance(self.scheduler , lowercase ):
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , eta=lowercase , generator=lowercase , )['''prev_sample''']
else:
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , generator=lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
UpperCAmelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase = self.vqvae.decode(lowercase )['''sample''']
UpperCAmelCase = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase = (images * 255).round().astype('''uint8''' )
UpperCAmelCase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
UpperCAmelCase = [self.mel.image_to_audio(lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowercase ) )
@torch.no_grad()
def A ( self : Dict , lowercase : List[Image.Image] , lowercase : int = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler , lowercase )
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase = (sample / 255) * 2 - 1
UpperCAmelCase = torch.Tensor(lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase = self.scheduler.alphas_cumprod[t]
UpperCAmelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase = 1 - alpha_prod_t
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def A ( lowercase : torch.Tensor , lowercase : torch.Tensor , lowercase : float ):
'''simple docstring'''
UpperCAmelCase = acos(torch.dot(torch.flatten(lowercase ) , torch.flatten(lowercase ) ) / torch.norm(lowercase ) / torch.norm(lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(lowercase ) + sin(alpha * theta ) * xa / sin(lowercase )
| 34
| 1
|
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class _a ( __a ):
__a : Optional[int] = """mvp"""
__a : Dict = ["""past_key_values"""]
__a : int = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[str] , lowercase : List[str]=50_267 , lowercase : List[str]=1_024 , lowercase : Any=12 , lowercase : Optional[int]=4_096 , lowercase : Tuple=16 , lowercase : Dict=12 , lowercase : List[str]=4_096 , lowercase : Union[str, Any]=16 , lowercase : str=0.0 , lowercase : List[str]=0.0 , lowercase : Union[str, Any]="gelu" , lowercase : int=1_024 , lowercase : Union[str, Any]=0.1 , lowercase : Union[str, Any]=0.0 , lowercase : Optional[Any]=0.0 , lowercase : Union[str, Any]=0.02 , lowercase : str=0.0 , lowercase : Optional[Any]=False , lowercase : Tuple=True , lowercase : Tuple=1 , lowercase : Optional[Any]=0 , lowercase : List[Any]=2 , lowercase : Dict=True , lowercase : int=2 , lowercase : Optional[int]=2 , lowercase : Any=False , lowercase : Tuple=100 , lowercase : int=800 , **lowercase : str , ):
'''simple docstring'''
UpperCAmelCase = vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = d_model
UpperCAmelCase = encoder_ffn_dim
UpperCAmelCase = encoder_layers
UpperCAmelCase = encoder_attention_heads
UpperCAmelCase = decoder_ffn_dim
UpperCAmelCase = decoder_layers
UpperCAmelCase = decoder_attention_heads
UpperCAmelCase = dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation_dropout
UpperCAmelCase = activation_function
UpperCAmelCase = init_std
UpperCAmelCase = encoder_layerdrop
UpperCAmelCase = decoder_layerdrop
UpperCAmelCase = classifier_dropout
UpperCAmelCase = use_cache
UpperCAmelCase = encoder_layers
UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase = use_prompt
UpperCAmelCase = prompt_length
UpperCAmelCase = prompt_mid_dim
super().__init__(
pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , forced_eos_token_id=lowercase , **lowercase , )
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , lowercase ):
UpperCAmelCase = self.bos_token_id
warnings.warn(
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
'''The config can simply be saved and uploaded again to be fixed.''' )
| 34
|
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
A =logging.get_logger(__name__)
A =TypeVar('DatasetType', Dataset, IterableDataset)
def snake_case_ (_a : List[DatasetType] , _a : Optional[List[float]] = None , _a : Optional[int] = None , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
else:
return _interleave_iterable_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
def snake_case_ (_a : List[DatasetType] , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : int = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_a , info=_a , split=_a , axis=_a )
else:
return _concatenate_iterable_datasets(_a , info=_a , split=_a , axis=_a )
| 34
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A =logging.get_logger(__name__)
A ={
'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}
class _a ( __a , __a ):
__a : Union[str, Any] = """convnextv2"""
def __init__( self : str , lowercase : Optional[Any]=3 , lowercase : Optional[Any]=4 , lowercase : Tuple=4 , lowercase : Optional[int]=None , lowercase : Optional[int]=None , lowercase : List[Any]="gelu" , lowercase : str=0.02 , lowercase : List[Any]=1E-12 , lowercase : List[str]=0.0 , lowercase : Dict=224 , lowercase : Dict=None , lowercase : List[Any]=None , **lowercase : int , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = num_channels
UpperCAmelCase = patch_size
UpperCAmelCase = num_stages
UpperCAmelCase = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
UpperCAmelCase = [3, 3, 9, 3] if depths is None else depths
UpperCAmelCase = hidden_act
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = drop_path_rate
UpperCAmelCase = image_size
UpperCAmelCase = ['''stem'''] + [f"stage{idx}" for idx in range(1 , len(self.depths ) + 1 )]
UpperCAmelCase , UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=lowercase , out_indices=lowercase , stage_names=self.stage_names )
| 34
|
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def snake_case_ (_a : Tuple ):
return {key.lstrip('''-''' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def snake_case_ ():
UpperCAmelCase = ArgumentParser(
'''HuggingFace Datasets CLI tool''' , usage='''datasets-cli <command> [<args>]''' , allow_abbrev=_a )
UpperCAmelCase = parser.add_subparsers(help='''datasets-cli command helpers''' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(_a )
EnvironmentCommand.register_subcommand(_a )
TestCommand.register_subcommand(_a )
RunBeamCommand.register_subcommand(_a )
DummyDataCommand.register_subcommand(_a )
# Parse args
UpperCAmelCase , UpperCAmelCase = parser.parse_known_args()
if not hasattr(_a , '''func''' ):
parser.print_help()
exit(1 )
UpperCAmelCase = parse_unknown_args(_a )
# Run
UpperCAmelCase = args.func(_a , **_a )
service.run()
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}
class _a ( __a ):
__a : Union[str, Any] = """encodec"""
def __init__( self : Tuple , lowercase : List[str]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowercase : Any=24_000 , lowercase : str=1 , lowercase : Optional[int]=False , lowercase : Optional[Any]=None , lowercase : str=None , lowercase : Tuple=128 , lowercase : Union[str, Any]=32 , lowercase : Union[str, Any]=1 , lowercase : Optional[Any]=[8, 5, 4, 2] , lowercase : Any="weight_norm" , lowercase : Tuple=7 , lowercase : int=7 , lowercase : Dict=3 , lowercase : List[Any]=2 , lowercase : str=True , lowercase : List[str]="reflect" , lowercase : List[Any]=2 , lowercase : Optional[Any]=2 , lowercase : int=1.0 , lowercase : Dict=1_024 , lowercase : str=None , lowercase : Union[str, Any]=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = target_bandwidths
UpperCAmelCase = sampling_rate
UpperCAmelCase = audio_channels
UpperCAmelCase = normalize
UpperCAmelCase = chunk_length_s
UpperCAmelCase = overlap
UpperCAmelCase = hidden_size
UpperCAmelCase = num_filters
UpperCAmelCase = num_residual_layers
UpperCAmelCase = upsampling_ratios
UpperCAmelCase = norm_type
UpperCAmelCase = kernel_size
UpperCAmelCase = last_kernel_size
UpperCAmelCase = residual_kernel_size
UpperCAmelCase = dilation_growth_rate
UpperCAmelCase = use_causal_conv
UpperCAmelCase = pad_mode
UpperCAmelCase = compress
UpperCAmelCase = num_lstm_layers
UpperCAmelCase = trim_right_ratio
UpperCAmelCase = codebook_size
UpperCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}" )
super().__init__(**lowercase )
@property
def A ( self : Dict ):
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 34
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
A =logging.getLogger()
def snake_case_ ():
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
UpperCAmelCase = parser.parse_args()
return args.f
def snake_case_ (_a : List[str] , _a : Union[str, Any]="eval" ):
UpperCAmelCase = os.path.join(_a , F"{split}_results.json" )
if os.path.exists(_a ):
with open(_a , '''r''' ) as f:
return json.load(_a )
raise ValueError(F"can't find {path}" )
A =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _a ( __a ):
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_glue.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
@slow
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_clm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 100 )
@slow
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_summarization_flax.main()
UpperCAmelCase = get_results(lowercase , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_ta_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = 7 if get_gpu_count() > 1 else 2
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_ner.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_qa.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 34
| 1
|
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def snake_case_ (_a : List[str]=None ):
if subparsers is not None:
UpperCAmelCase = subparsers.add_parser('''env''' )
else:
UpperCAmelCase = argparse.ArgumentParser('''Accelerate env command''' )
parser.add_argument(
'''--config_file''' , default=_a , help='''The config file to use for the default values in the launching script.''' )
if subparsers is not None:
parser.set_defaults(func=_a )
return parser
def snake_case_ (_a : List[Any] ):
UpperCAmelCase = torch.__version__
UpperCAmelCase = torch.cuda.is_available()
UpperCAmelCase = is_xpu_available()
UpperCAmelCase = is_npu_available()
UpperCAmelCase = '''Not found'''
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_a ):
UpperCAmelCase = load_config_from_file(args.config_file ).to_dict()
UpperCAmelCase = {
'''`Accelerate` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Numpy version''': np.__version__,
'''PyTorch version (GPU?)''': F"{pt_version} ({pt_cuda_available})",
'''PyTorch XPU available''': str(_a ),
'''PyTorch NPU available''': str(_a ),
'''System RAM''': F"{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB",
}
if pt_cuda_available:
UpperCAmelCase = torch.cuda.get_device_name()
print('''\nCopy-and-paste the text below in your GitHub issue\n''' )
print('''\n'''.join([F"- {prop}: {val}" for prop, val in info.items()] ) )
print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' )
UpperCAmelCase = (
'''\n'''.join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()] )
if isinstance(_a , _a )
else F"\t{accelerate_config}"
)
print(_a )
UpperCAmelCase = accelerate_config
return info
def snake_case_ ():
UpperCAmelCase = env_command_parser()
UpperCAmelCase = parser.parse_args()
env_command(_a )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 34
|
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[str] ):
'''simple docstring'''
for word in words:
self.insert(lowercase )
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
UpperCAmelCase = TrieNode()
UpperCAmelCase = curr.nodes[char]
UpperCAmelCase = True
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
return False
UpperCAmelCase = curr.nodes[char]
return curr.is_leaf
def A ( self : str , lowercase : str ):
'''simple docstring'''
def _delete(lowercase : TrieNode , lowercase : str , lowercase : int ) -> bool:
if index == len(lowercase ):
# If word does not exist
if not curr.is_leaf:
return False
UpperCAmelCase = False
return len(curr.nodes ) == 0
UpperCAmelCase = word[index]
UpperCAmelCase = curr.nodes.get(lowercase )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
UpperCAmelCase = _delete(lowercase , lowercase , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , lowercase , 0 )
def snake_case_ (_a : TrieNode , _a : str ):
if node.is_leaf:
print(_a , end=''' ''' )
for key, value in node.nodes.items():
print_words(_a , word + key )
def snake_case_ ():
UpperCAmelCase = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase = TrieNode()
root.insert_many(_a )
# print_words(root, "")
assert all(root.find(_a ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def snake_case_ (_a : str , _a : bool ):
print(str(_a ) , '''works!''' if passes else '''doesn\'t work :(''' )
def snake_case_ ():
assert test_trie()
def snake_case_ ():
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : list ):
def merge(_a : list , _a : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(_a ) <= 1:
return collection
UpperCAmelCase = len(_a ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A =input('Enter numbers separated by a comma:\n').strip()
A =[int(item) for item in user_input.split(',')]
print(*merge_sort(unsorted), sep=',')
| 34
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}
class _a ( __a ):
__a : Union[str, Any] = """encodec"""
def __init__( self : Tuple , lowercase : List[str]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowercase : Any=24_000 , lowercase : str=1 , lowercase : Optional[int]=False , lowercase : Optional[Any]=None , lowercase : str=None , lowercase : Tuple=128 , lowercase : Union[str, Any]=32 , lowercase : Union[str, Any]=1 , lowercase : Optional[Any]=[8, 5, 4, 2] , lowercase : Any="weight_norm" , lowercase : Tuple=7 , lowercase : int=7 , lowercase : Dict=3 , lowercase : List[Any]=2 , lowercase : str=True , lowercase : List[str]="reflect" , lowercase : List[Any]=2 , lowercase : Optional[Any]=2 , lowercase : int=1.0 , lowercase : Dict=1_024 , lowercase : str=None , lowercase : Union[str, Any]=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = target_bandwidths
UpperCAmelCase = sampling_rate
UpperCAmelCase = audio_channels
UpperCAmelCase = normalize
UpperCAmelCase = chunk_length_s
UpperCAmelCase = overlap
UpperCAmelCase = hidden_size
UpperCAmelCase = num_filters
UpperCAmelCase = num_residual_layers
UpperCAmelCase = upsampling_ratios
UpperCAmelCase = norm_type
UpperCAmelCase = kernel_size
UpperCAmelCase = last_kernel_size
UpperCAmelCase = residual_kernel_size
UpperCAmelCase = dilation_growth_rate
UpperCAmelCase = use_causal_conv
UpperCAmelCase = pad_mode
UpperCAmelCase = compress
UpperCAmelCase = num_lstm_layers
UpperCAmelCase = trim_right_ratio
UpperCAmelCase = codebook_size
UpperCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}" )
super().__init__(**lowercase )
@property
def A ( self : Dict ):
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 34
| 1
|
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 34
|
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A =logging.get_logger(__name__)
class _a ( __a ):
__a : str = ["""pixel_values"""]
def __init__( self : Optional[int] , lowercase : bool = True , lowercase : Optional[Dict[str, int]] = None , lowercase : PILImageResampling = PILImageResampling.BILINEAR , lowercase : bool = True , lowercase : Dict[str, int] = None , lowercase : bool = True , lowercase : Union[int, float] = 1 / 255 , lowercase : bool = True , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = size if size is not None else {'''shortest_edge''': 256}
UpperCAmelCase = get_size_dict(lowercase , default_to_square=lowercase )
UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = resample
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : PILImageResampling = PILImageResampling.BICUBIC , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Any , ):
'''simple docstring'''
UpperCAmelCase = 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()}" )
UpperCAmelCase = 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 A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : int , ):
'''simple docstring'''
UpperCAmelCase = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(lowercase , size=(size['''height'''], size['''width''']) , data_format=lowercase , **lowercase )
def A ( self : Tuple , lowercase : np.ndarray , lowercase : float , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : List[str] ):
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[int] , lowercase : np.ndarray , lowercase : Union[float, List[float]] , lowercase : Union[float, List[float]] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[int] , lowercase : ImageInput , lowercase : Optional[bool] = None , lowercase : Dict[str, int] = None , lowercase : PILImageResampling = None , lowercase : bool = None , lowercase : Dict[str, int] = None , lowercase : Optional[bool] = None , lowercase : Optional[float] = None , lowercase : Optional[bool] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[str, TensorType]] = None , lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **lowercase : Dict , ):
'''simple docstring'''
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(lowercase , default_to_square=lowercase )
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = 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.
UpperCAmelCase = [to_numpy_array(lowercase ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_center_crop:
UpperCAmelCase = [self.center_crop(image=lowercase , size=lowercase ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=lowercase , tensor_type=lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[Tuple] = None ):
'''simple docstring'''
UpperCAmelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowercase ) != len(lowercase ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(lowercase ):
UpperCAmelCase = target_sizes.numpy()
UpperCAmelCase = []
for idx in range(len(lowercase ) ):
UpperCAmelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=lowercase )
UpperCAmelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowercase )
else:
UpperCAmelCase = logits.argmax(dim=1 )
UpperCAmelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 34
| 1
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
A =logging.get_logger(__name__)
class _a ( __a ):
def __init__( self : Union[str, Any] , *lowercase : List[Any] , **lowercase : Union[str, Any] ):
'''simple docstring'''
warnings.warn(
'''The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use VideoMAEImageProcessor instead.''' , lowercase , )
super().__init__(*lowercase , **lowercase )
| 34
|
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A =logging.getLogger(__name__)
def snake_case_ (_a : Dict , _a : Union[str, Any] ):
return (preds == labels).mean()
@dataclass
class _a :
__a : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class _a :
__a : str = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
__a : str = field(metadata={"""help""": """Should contain the data files for the task."""} )
__a : int = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__a : bool = field(
default=__a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def snake_case_ ():
# 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 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , 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
set_seed(training_args.seed )
try:
UpperCAmelCase = processors[data_args.task_name]()
UpperCAmelCase = processor.get_labels()
UpperCAmelCase = len(_a )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_a , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoModelForMultipleChoice.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 , )
# Get datasets
UpperCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
UpperCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_a : EvalPrediction ) -> Dict:
UpperCAmelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(_a , p.label_ids )}
# Data collator
UpperCAmelCase = DataCollatorWithPadding(_a , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
UpperCAmelCase = Trainer(
model=_a , args=_a , train_dataset=_a , eval_dataset=_a , compute_metrics=_a , data_collator=_a , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCAmelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase = trainer.evaluate()
UpperCAmelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(_a , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , _a , _a )
writer.write('''%s = %s\n''' % (key, value) )
results.update(_a )
return results
def snake_case_ (_a : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A =get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
A =5_00_03
A =5_00_02
@require_sentencepiece
@require_tokenizers
class _a ( __a , unittest.TestCase ):
__a : Any = PLBartTokenizer
__a : Any = None
__a : Dict = False
def A ( self : List[str] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase = PLBartTokenizer(lowercase , language_codes='''base''' , keep_accents=lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = PLBartTokenizer(lowercase , language_codes='''base''' , keep_accents=lowercase )
UpperCAmelCase = 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]] , )
UpperCAmelCase = 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''',
'''.''',
] , )
UpperCAmelCase = 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]
] , )
UpperCAmelCase = 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>''',
'''.''',
] , )
UpperCAmelCase = tokenizer.vocab_size
UpperCAmelCase = [tokenizer.convert_ids_to_tokens(lowercase ) for x in range(end - 4 , lowercase )]
self.assertListEqual(lowercase , ['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>'''] )
UpperCAmelCase = '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
UpperCAmelCase = tokenizer(lowercase ).input_ids
self.assertEqual(
tokenizer.decode(lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase ) , lowercase , )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = PLBartTokenizer(lowercase , language_codes='''multi''' , keep_accents=lowercase )
UpperCAmelCase = 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]] , )
UpperCAmelCase = 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''',
'''.''',
] , )
UpperCAmelCase = 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]
] , )
UpperCAmelCase = 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>''',
'''.''',
] , )
UpperCAmelCase = tokenizer.vocab_size
UpperCAmelCase = [tokenizer.convert_ids_to_tokens(lowercase ) for x in range(end - 7 , lowercase )]
self.assertListEqual(
lowercase , ['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__'''] )
UpperCAmelCase = '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
UpperCAmelCase = tokenizer(lowercase ).input_ids
self.assertEqual(
tokenizer.decode(lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase ) , lowercase , )
@require_torch
@require_sentencepiece
@require_tokenizers
class _a ( unittest.TestCase ):
__a : Tuple = """uclanlp/plbart-python-en_XX"""
__a : int = [
"""def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""",
"""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""",
]
__a : List[str] = [
"""Returns the maximum value of a b c.""",
"""Sums the values of a b c.""",
]
__a : int = [
134,
5_452,
33_460,
33_441,
33_463,
33_465,
33_463,
33_449,
988,
20,
33_456,
19,
33_456,
771,
39,
4_258,
889,
3_318,
33_441,
33_463,
33_465,
33_463,
33_449,
2_471,
2,
PYTHON_CODE,
]
@classmethod
def A ( cls : str ):
'''simple docstring'''
UpperCAmelCase = PLBartTokenizer.from_pretrained(
cls.checkpoint_name , language_codes='''base''' , src_lang='''python''' , tgt_lang='''en_XX''' )
UpperCAmelCase = 1
return cls
def A ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__'''] , 50_001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__'''] , 50_002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__'''] , 50_003 )
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowercase )
def A ( self : Any ):
'''simple docstring'''
self.assertIn(lowercase , self.tokenizer.all_special_ids )
UpperCAmelCase = [EN_CODE, 9_037, 33_442, 57, 752, 153, 14, 56, 18, 9, 2]
UpperCAmelCase = self.tokenizer.decode(lowercase , skip_special_tokens=lowercase )
UpperCAmelCase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowercase )
self.assertEqual(lowercase , lowercase )
self.assertNotIn(self.tokenizer.eos_token , lowercase )
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = ['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
self.assertIsInstance(src_text[0] , lowercase )
UpperCAmelCase = 10
UpperCAmelCase = self.tokenizer(lowercase , max_length=lowercase , truncation=lowercase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , lowercase )
self.assertEqual(len(lowercase ) , lowercase )
def A ( self : Dict ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__'''] ) , [50_004, 50_001] )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowercase )
UpperCAmelCase = PLBartTokenizer.from_pretrained(lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowercase )
@require_torch
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowercase , return_tensors='''pt''' )
UpperCAmelCase = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist() , [2, PYTHON_CODE] )
self.assertEqual(batch.decoder_input_ids[1][0] , lowercase )
self.assertEqual(batch.decoder_input_ids[1][-1] , 2 )
self.assertEqual(batch.labels[1][-2:].tolist() , [2, EN_CODE] )
@require_torch
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowercase , truncation=lowercase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
UpperCAmelCase = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual((2, 26) , batch.input_ids.shape )
self.assertEqual((2, 26) , batch.attention_mask.shape )
UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowercase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, PYTHON_CODE] )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer(self.src_text , padding=lowercase , truncation=lowercase , max_length=3 , return_tensors='''pt''' )
UpperCAmelCase = self.tokenizer(
text_target=self.tgt_text , padding=lowercase , truncation=lowercase , max_length=10 , return_tensors='''pt''' )
UpperCAmelCase = targets['''input_ids''']
UpperCAmelCase = shift_tokens_right(lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''java''' )
self.assertEqual(
nested_simplify(lowercase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[150, 242, 2, 50_003]],
'''attention_mask''': [[1, 1, 1, 1]],
# java
'''forced_bos_token_id''': 50_001,
} , )
| 34
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImageProcessor"""
__a : List[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Dict , lowercase : Union[str, Any]=None , lowercase : Dict=None , **lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowercase , )
UpperCAmelCase = kwargs.pop('''feature_extractor''' )
UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowercase , lowercase )
UpperCAmelCase = self.image_processor
def __call__( self : Tuple , lowercase : Optional[Any]=None , lowercase : Union[str, Any]=None , lowercase : int=None , **lowercase : Dict ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCAmelCase = self.tokenizer(lowercase , return_tensors=lowercase , **lowercase )
if images is not None:
UpperCAmelCase = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def A ( self : int , *lowercase : Tuple , **lowercase : List[str] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A ( self : Optional[Any] , *lowercase : int , **lowercase : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer.model_input_names
UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A ( self : List[Any] ):
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowercase , )
return self.image_processor_class
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase = [[0 for i in range(_a )] for j in range(_a )]
# since string of zero length match pattern of zero length
UpperCAmelCase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _a ):
UpperCAmelCase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _a ):
UpperCAmelCase = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _a ):
for j in range(1 , _a ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase = dp[i - 1][j]
else:
UpperCAmelCase = 0
else:
UpperCAmelCase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A ='aab'
A ='c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"""{input_string} matches the given pattern {pattern}""")
else:
print(f"""{input_string} does not match with the given pattern {pattern}""")
| 34
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A =logging.get_logger(__name__)
A ={
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class _a ( __a ):
__a : List[Any] = """marian"""
__a : Union[str, Any] = ["""past_key_values"""]
__a : List[str] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[Any] , lowercase : Union[str, Any]=58_101 , lowercase : Tuple=None , lowercase : str=1_024 , lowercase : Optional[int]=12 , lowercase : Optional[int]=4_096 , lowercase : int=16 , lowercase : List[Any]=12 , lowercase : int=4_096 , lowercase : Optional[int]=16 , lowercase : int=0.0 , lowercase : Tuple=0.0 , lowercase : Tuple=True , lowercase : Union[str, Any]=True , lowercase : List[Any]="gelu" , lowercase : Tuple=1_024 , lowercase : str=0.1 , lowercase : str=0.0 , lowercase : Optional[int]=0.0 , lowercase : Dict=0.02 , lowercase : Union[str, Any]=58_100 , lowercase : List[str]=False , lowercase : str=58_100 , lowercase : Any=0 , lowercase : Optional[Any]=0 , lowercase : Tuple=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = vocab_size
UpperCAmelCase = decoder_vocab_size or vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = d_model
UpperCAmelCase = encoder_ffn_dim
UpperCAmelCase = encoder_layers
UpperCAmelCase = encoder_attention_heads
UpperCAmelCase = decoder_ffn_dim
UpperCAmelCase = decoder_layers
UpperCAmelCase = decoder_attention_heads
UpperCAmelCase = dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation_dropout
UpperCAmelCase = activation_function
UpperCAmelCase = init_std
UpperCAmelCase = encoder_layerdrop
UpperCAmelCase = decoder_layerdrop
UpperCAmelCase = use_cache
UpperCAmelCase = encoder_layers
UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , forced_eos_token_id=lowercase , **lowercase , )
class _a ( __a ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A ( self : int ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase = {0: '''batch'''}
UpperCAmelCase = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A ( self : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super().outputs
else:
UpperCAmelCase = super(lowercase , self ).outputs
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A ( self : Dict , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Generate decoder inputs
UpperCAmelCase = seq_length if not self.use_past else 1
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
UpperCAmelCase = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
UpperCAmelCase = dict(**lowercase , **lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
UpperCAmelCase = common_inputs['''decoder_input_ids'''].shape[1]
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = decoder_seq_length + 3
UpperCAmelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
UpperCAmelCase = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase , lowercase )] , dim=1 )
UpperCAmelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase = min(lowercase , lowercase )
UpperCAmelCase = max(lowercase , lowercase ) - min_num_layers
UpperCAmelCase = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
) )
# TODO: test this.
UpperCAmelCase = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase , lowercase ):
common_inputs["past_key_values"].append((torch.zeros(lowercase ), torch.zeros(lowercase )) )
return common_inputs
def A ( self : int , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase = seqlen + 2
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = common_inputs['''attention_mask'''].dtype
UpperCAmelCase = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase , lowercase , dtype=lowercase )] , dim=1 )
UpperCAmelCase = [
(torch.zeros(lowercase ), torch.zeros(lowercase )) for _ in range(lowercase )
]
return common_inputs
def A ( self : str , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase = tokenizer.num_special_tokens_to_add(lowercase )
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
UpperCAmelCase = dict(tokenizer(lowercase , return_tensors=lowercase ) )
return common_inputs
def A ( self : List[str] , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
else:
UpperCAmelCase = self._generate_dummy_inputs_for_causal_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
return common_inputs
def A ( self : List[Any] , lowercase : Any , lowercase : Tuple , lowercase : Any , lowercase : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super()._flatten_past_key_values_(lowercase , lowercase , lowercase , lowercase )
else:
UpperCAmelCase = super(lowercase , self )._flatten_past_key_values_(
lowercase , lowercase , lowercase , lowercase )
@property
def A ( self : Any ):
'''simple docstring'''
return 1E-4
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : str , _a : bool = False ):
if not isinstance(_a , _a ):
UpperCAmelCase = F"Expected string as input, found {type(_a )}"
raise ValueError(_a )
if not isinstance(_a , _a ):
UpperCAmelCase = F"Expected boolean as use_pascal parameter, found {type(_a )}"
raise ValueError(_a )
UpperCAmelCase = input_str.split('''_''' )
UpperCAmelCase = 0 if use_pascal else 1
UpperCAmelCase = words[start_index:]
UpperCAmelCase = [word[0].upper() + word[1:] for word in words_to_capitalize]
UpperCAmelCase = '''''' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 34
|
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
print(solution())
| 34
| 1
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _a ( __a ):
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase , '''embed_dim''' ) )
self.parent.assertTrue(hasattr(lowercase , '''num_heads''' ) )
class _a :
def __init__( self : int , lowercase : Tuple , lowercase : Optional[int]=13 , lowercase : Optional[Any]=64 , lowercase : int=3 , lowercase : Optional[int]=[16, 48, 96] , lowercase : int=[1, 3, 6] , lowercase : Union[str, Any]=[1, 2, 10] , lowercase : Optional[Any]=[7, 3, 3] , lowercase : List[Any]=[4, 2, 2] , lowercase : str=[2, 1, 1] , lowercase : Union[str, Any]=[2, 2, 2] , lowercase : str=[False, False, True] , lowercase : Tuple=[0.0, 0.0, 0.0] , lowercase : List[str]=0.02 , lowercase : int=1E-12 , lowercase : Optional[int]=True , lowercase : Dict=True , lowercase : Optional[Any]=2 , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = patch_sizes
UpperCAmelCase = patch_stride
UpperCAmelCase = patch_padding
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = num_labels
UpperCAmelCase = num_channels
UpperCAmelCase = embed_dim
UpperCAmelCase = num_heads
UpperCAmelCase = stride_kv
UpperCAmelCase = depth
UpperCAmelCase = cls_token
UpperCAmelCase = attention_drop_rate
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
# create a random int32 tensor of given shape
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def A ( self : List[Any] ):
'''simple docstring'''
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def A ( self : str , lowercase : Tuple , lowercase : Tuple , lowercase : int ):
'''simple docstring'''
UpperCAmelCase = TFCvtModel(config=lowercase )
UpperCAmelCase = model(lowercase , training=lowercase )
UpperCAmelCase = (self.image_size, self.image_size)
UpperCAmelCase , UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def A ( self : str , lowercase : int , lowercase : str , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = TFCvtForImageClassification(lowercase )
UpperCAmelCase = model(lowercase , labels=lowercase , training=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _a ( __a , __a , unittest.TestCase ):
__a : Any = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
__a : List[str] = (
{"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
if is_tf_available()
else {}
)
__a : Tuple = False
__a : Dict = False
__a : int = False
__a : Dict = False
__a : int = False
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = TFCvtModelTester(self )
UpperCAmelCase = TFCvtConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def A ( self : Optional[Any] ):
'''simple docstring'''
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason='''Cvt does not output attentions''' )
def A ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def A ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def A ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
def A ( self : Any ):
'''simple docstring'''
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def A ( self : Optional[Any] ):
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8''' )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = tf.keras.mixed_precision.Policy('''mixed_float16''' )
tf.keras.mixed_precision.set_global_policy(lowercase )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy('''float32''' )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase )
def A ( self : int ):
'''simple docstring'''
def check_hidden_states_output(lowercase : Tuple , lowercase : Tuple , lowercase : Union[str, Any] ):
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model(**self._prepare_for_class(lowercase , lowercase ) )
UpperCAmelCase = outputs.hidden_states
UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(lowercase ) , lowercase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = True
check_hidden_states_output(lowercase , lowercase , lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase = True
check_hidden_states_output(lowercase , lowercase , lowercase )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
@slow
def A ( self : List[str] ):
'''simple docstring'''
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = TFCvtModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def snake_case_ ():
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _a ( unittest.TestCase ):
@cached_property
def A ( self : Optional[Any] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCAmelCase = self.default_image_processor
UpperCAmelCase = prepare_img()
UpperCAmelCase = image_processor(images=lowercase , return_tensors='''tf''' )
# forward pass
UpperCAmelCase = model(**lowercase )
# verify the logits
UpperCAmelCase = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowercase )
UpperCAmelCase = tf.constant([0.9285, 0.9015, -0.3150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowercase , atol=1E-4 ) )
| 34
|
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
A =logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
A ={
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'summarization': AutoModelForSeqaSeqLM,
'translation': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
A ={
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
A =sorted(arg_to_scheduler.keys())
A ='{' + ', '.join(arg_to_scheduler_choices) + '}'
class _a ( pl.LightningModule ):
def __init__( self : List[str] , lowercase : argparse.Namespace , lowercase : List[Any]=None , lowercase : Dict="base" , lowercase : Optional[int]=None , lowercase : Dict=None , lowercase : Tuple=None , **lowercase : Optional[int] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowercase )
UpperCAmelCase = 0
UpperCAmelCase = Path(self.hparams.output_dir )
UpperCAmelCase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
UpperCAmelCase = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'''num_labels''': num_labels} if num_labels is not None else {}) , cache_dir=lowercase , **lowercase , )
else:
UpperCAmelCase = config
UpperCAmelCase = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , lowercase , lowercase ):
assert hasattr(self.config , lowercase ), f"model config doesn't have a `{p}` attribute"
setattr(self.config , lowercase , getattr(self.hparams , lowercase ) )
if tokenizer is None:
UpperCAmelCase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowercase , )
else:
UpperCAmelCase = tokenizer
UpperCAmelCase = MODEL_MODES[mode]
if model is None:
UpperCAmelCase = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=lowercase , )
else:
UpperCAmelCase = model
def A ( self : List[Any] , *lowercase : List[str] , **lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.model_type.from_pretrained(*lowercase , **lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.hparams.lr_scheduler]
UpperCAmelCase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
UpperCAmelCase = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''params''': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'''weight_decay''': self.hparams.weight_decay,
},
{
'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
if self.hparams.adafactor:
UpperCAmelCase = Adafactor(
lowercase , lr=self.hparams.learning_rate , scale_parameter=lowercase , relative_step=lowercase )
else:
UpperCAmelCase = AdamW(
lowercase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
UpperCAmelCase = optimizer
UpperCAmelCase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A ( self : List[Any] , lowercase : int , lowercase : List[str] ):
'''simple docstring'''
return self.validation_step(lowercase , lowercase )
def A ( self : List[Any] , lowercase : Tuple ):
'''simple docstring'''
return self.validation_end(lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
UpperCAmelCase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A ( self : List[str] , lowercase : Any ):
'''simple docstring'''
if stage == "test":
UpperCAmelCase = len(self.test_dataloader().dataset )
else:
UpperCAmelCase = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=lowercase )
UpperCAmelCase = len(self.train_dataloader().dataset )
def A ( self : List[str] , lowercase : str , lowercase : int , lowercase : bool = False ):
'''simple docstring'''
raise NotImplementedError('''You must implement this for your task''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
return self.train_loader
def A ( self : Optional[Any] ):
'''simple docstring'''
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
lowercase , list(filter(lowercase , self.hparams.model_name_or_path.split('''/''' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A ( self : List[str] , lowercase : Dict[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.output_dir.joinpath('''best_tfmr''' )
UpperCAmelCase = self.step_count
self.model.save_pretrained(lowercase )
self.tokenizer.save_pretrained(lowercase )
@staticmethod
def A ( lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
parser.add_argument(
'''--model_name_or_path''' , default=lowercase , type=lowercase , required=lowercase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=lowercase , help='''Pretrained config name or path if not the same as model_name''' )
parser.add_argument(
'''--tokenizer_name''' , default=lowercase , type=lowercase , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(lowercase ).parent / '''test_run''' / '''cache''' ) , type=lowercase , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=lowercase , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=lowercase , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=lowercase , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=lowercase , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5E-5 , type=lowercase , help='''The initial learning rate for Adam.''' )
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=lowercase , metavar=lowercase , type=lowercase , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=lowercase , help='''Weight decay if we apply some.''' )
parser.add_argument('''--adam_epsilon''' , default=1E-8 , type=lowercase , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--warmup_steps''' , default=0 , type=lowercase , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--num_workers''' , default=4 , type=lowercase , help='''kwarg passed to DataLoader''' )
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=lowercase )
parser.add_argument('''--train_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--eval_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--adafactor''' , action='''store_true''' )
class _a ( pl.Callback ):
def A ( self : Dict , lowercase : Optional[Any] , lowercase : List[Any] ):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Union[str, Any] , lowercase : Any ):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowercase )
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Optional[int] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = trainer.lr_schedulers[0]['''scheduler''']
UpperCAmelCase = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowercase )
def A ( self : Tuple , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Validation results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log results
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def A ( self : Dict , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Test results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log and save results to file
UpperCAmelCase = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''' )
with open(lowercase , '''w''' ) as writer:
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
writer.write('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def snake_case_ (_a : int , _a : Optional[Any] ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'''--output_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_a , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument(
'''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , )
parser.add_argument(
'''--fp16_opt_level''' , type=_a , default='''O2''' , help=(
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
'''See details at https://nvidia.github.io/apex/amp.html'''
) , )
parser.add_argument('''--n_tpu_cores''' , dest='''tpu_cores''' , type=_a )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_a , help='''Max gradient norm''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_predict''' , action='''store_true''' , help='''Whether to run predictions on the test set.''' )
parser.add_argument(
'''--gradient_accumulation_steps''' , dest='''accumulate_grad_batches''' , type=_a , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_a , default=4_2 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_a , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def snake_case_ (_a : BaseTransformer , _a : argparse.Namespace , _a : List[Any]=None , _a : Tuple=True , _a : int=[] , _a : Any=None , _a : int=None , **_a : Optional[Any] , ):
pl.seed_everything(args.seed )
# init model
UpperCAmelCase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_a )
# add custom checkpoints
if checkpoint_callback is None:
UpperCAmelCase = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='''checkpoint''' , monitor='''val_loss''' , mode='''min''' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_a )
if logging_callback is None:
UpperCAmelCase = LoggingCallback()
UpperCAmelCase = {}
if args.fpaa:
UpperCAmelCase = 1_6
if args.gpus > 1:
UpperCAmelCase = '''auto'''
UpperCAmelCase = '''ddp'''
UpperCAmelCase = args.accumulate_grad_batches
UpperCAmelCase = None
UpperCAmelCase = '''auto'''
UpperCAmelCase = pl.Trainer.from_argparse_args(
_a , weights_summary=_a , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_a , val_check_interval=1 , num_sanity_val_steps=2 , **_a , )
if args.do_train:
trainer.fit(_a )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 34
| 1
|
'''simple docstring'''
from jiwer import compute_measures
import datasets
A ='\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
A ='\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'
A ='\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> wer = datasets.load_metric("wer")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _a ( datasets.Metric ):
def A ( self : Any ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/jitsi/jiwer/'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
] , )
def A ( self : List[Any] , lowercase : List[str]=None , lowercase : List[str]=None , lowercase : Union[str, Any]=False ):
'''simple docstring'''
if concatenate_texts:
return compute_measures(lowercase , lowercase )["wer"]
else:
UpperCAmelCase = 0
UpperCAmelCase = 0
for prediction, reference in zip(lowercase , lowercase ):
UpperCAmelCase = compute_measures(lowercase , lowercase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 34
|
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : dict , _a : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
UpperCAmelCase = cst_fwd.get(_a , np.inf )
UpperCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
UpperCAmelCase = new_cost_f
UpperCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
UpperCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def snake_case_ (_a : str , _a : str , _a : dict , _a : dict ):
UpperCAmelCase = -1
UpperCAmelCase = set()
UpperCAmelCase = set()
UpperCAmelCase = {source: 0}
UpperCAmelCase = {destination: 0}
UpperCAmelCase = {source: None}
UpperCAmelCase = {destination: None}
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
UpperCAmelCase , UpperCAmelCase = queue_forward.get()
visited_forward.add(_a )
UpperCAmelCase , UpperCAmelCase = queue_backward.get()
visited_backward.add(_a )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
UpperCAmelCase = shortest_distance
return shortest_path_distance
A ={
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
A ={
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
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
A =get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class _a ( __a , unittest.TestCase ):
__a : int = XGLMTokenizer
__a : Any = XGLMTokenizerFast
__a : Any = True
__a : Tuple = True
def A ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase = XGLMTokenizer(lowercase , keep_accents=lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = '''<pad>'''
UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(len(lowercase ) , 1_008 )
def A ( self : str ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_008 )
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = XGLMTokenizer(lowercase , keep_accents=lowercase )
UpperCAmelCase = 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]] , )
UpperCAmelCase = 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''',
'''.''',
] , )
UpperCAmelCase = 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]
] , )
UpperCAmelCase = 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>''',
'''.''',
] , )
@cached_property
def A ( self : Any ):
'''simple docstring'''
return XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
def A ( self : str ):
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowercase , f.name )
UpperCAmelCase = XGLMTokenizer(f.name , keep_accents=lowercase )
UpperCAmelCase = pickle.dumps(lowercase )
pickle.loads(lowercase )
def A ( self : List[str] ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_rust_tokenizer()
UpperCAmelCase = '''I was born in 92000, and this is falsé.'''
UpperCAmelCase = tokenizer.tokenize(lowercase )
UpperCAmelCase = rust_tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
UpperCAmelCase = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
UpperCAmelCase = self.get_rust_tokenizer()
UpperCAmelCase = tokenizer.encode(lowercase )
UpperCAmelCase = rust_tokenizer.encode(lowercase )
self.assertListEqual(lowercase , lowercase )
@slow
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = '''Hello World!'''
UpperCAmelCase = [2, 31_227, 4_447, 35]
self.assertListEqual(lowercase , self.big_tokenizer.encode(lowercase ) )
@slow
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = (
'''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'''
)
# fmt: off
UpperCAmelCase = [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(lowercase , self.big_tokenizer.encode(lowercase ) )
@slow
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = {
'''input_ids''': [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
'''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name='''facebook/xglm-564M''' , padding=lowercase , )
| 34
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
def snake_case_ (_a : List[str] ):
UpperCAmelCase = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
UpperCAmelCase = 1_2_8
elif "12-12" in model_name:
UpperCAmelCase = 1_2
UpperCAmelCase = 1_2
elif "14-14" in model_name:
UpperCAmelCase = 1_4
UpperCAmelCase = 1_4
elif "16-16" in model_name:
UpperCAmelCase = 1_6
UpperCAmelCase = 1_6
else:
raise ValueError('''Model not supported''' )
UpperCAmelCase = '''huggingface/label-files'''
if "speech-commands" in model_name:
UpperCAmelCase = 3_5
UpperCAmelCase = '''speech-commands-v2-id2label.json'''
else:
UpperCAmelCase = 5_2_7
UpperCAmelCase = '''audioset-id2label.json'''
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ (_a : Tuple ):
if "module.v" in name:
UpperCAmelCase = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
UpperCAmelCase = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
UpperCAmelCase = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
UpperCAmelCase = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
UpperCAmelCase = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
UpperCAmelCase = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
UpperCAmelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
UpperCAmelCase = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
UpperCAmelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
UpperCAmelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
UpperCAmelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
UpperCAmelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
UpperCAmelCase = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
UpperCAmelCase = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
UpperCAmelCase = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def snake_case_ (_a : Dict , _a : List[Any] ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(_a )
if "qkv" in key:
UpperCAmelCase = key.split('''.''' )
UpperCAmelCase = int(key_split[3] )
UpperCAmelCase = config.hidden_size
if "weight" in key:
UpperCAmelCase = val[:dim, :]
UpperCAmelCase = val[dim : dim * 2, :]
UpperCAmelCase = val[-dim:, :]
else:
UpperCAmelCase = val[:dim]
UpperCAmelCase = val[dim : dim * 2]
UpperCAmelCase = val[-dim:]
else:
UpperCAmelCase = val
return orig_state_dict
def snake_case_ (_a : Tuple ):
UpperCAmelCase = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(_a , _a )
@torch.no_grad()
def snake_case_ (_a : int , _a : Union[str, Any] , _a : Dict=False ):
UpperCAmelCase = get_audio_spectrogram_transformer_config(_a )
UpperCAmelCase = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
UpperCAmelCase = model_name_to_url[model_name]
UpperCAmelCase = torch.hub.load_state_dict_from_url(_a , map_location='''cpu''' )
# remove some keys
remove_keys(_a )
# rename some keys
UpperCAmelCase = convert_state_dict(_a , _a )
# load 🤗 model
UpperCAmelCase = ASTForAudioClassification(_a )
model.eval()
model.load_state_dict(_a )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
UpperCAmelCase = -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
UpperCAmelCase = 4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
UpperCAmelCase = 1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
UpperCAmelCase = ASTFeatureExtractor(mean=_a , std=_a , max_length=_a )
if "speech-commands" in model_name:
UpperCAmelCase = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
UpperCAmelCase = dataset[0]['''audio''']['''array''']
else:
UpperCAmelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
UpperCAmelCase , UpperCAmelCase = torchaudio.load(_a )
UpperCAmelCase = waveform.squeeze().numpy()
UpperCAmelCase = feature_extractor(_a , sampling_rate=1_6_0_0_0 , return_tensors='''pt''' )
# forward pass
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
UpperCAmelCase = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
UpperCAmelCase = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
UpperCAmelCase = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
UpperCAmelCase = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
UpperCAmelCase = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
UpperCAmelCase = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
UpperCAmelCase = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
UpperCAmelCase = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , _a , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(F"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_a )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(F"MIT/{model_name}" )
feature_extractor.push_to_hub(F"MIT/{model_name}" )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub.'
)
A =parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _a ( __a , unittest.TestCase ):
__a : List[Any] = VideoToVideoSDPipeline
__a : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""} ) - {"""image""", """width""", """height"""}
__a : Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""} ) - {"""image"""}
__a : Union[str, Any] = PipelineTesterMixin.required_optional_params - {"""latents"""}
__a : str = False
# No `output_type`.
__a : Dict = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def A ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''') , up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''') , cross_attention_dim=32 , attention_head_dim=4 , )
UpperCAmelCase = 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 )
UpperCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act='''gelu''' , projection_dim=512 , )
UpperCAmelCase = CLIPTextModel(lowercase )
UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def A ( self : Optional[Any] , lowercase : Optional[Any] , lowercase : int=0 ):
'''simple docstring'''
UpperCAmelCase = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(lowercase ) ).to(lowercase )
if str(lowercase ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(lowercase )
else:
UpperCAmelCase = torch.Generator(device=lowercase ).manual_seed(lowercase )
UpperCAmelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''video''': video,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''pt''',
}
return inputs
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = VideoToVideoSDPipeline(**lowercase )
UpperCAmelCase = sd_pipe.to(lowercase )
sd_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = '''np'''
UpperCAmelCase = sd_pipe(**lowercase ).frames
UpperCAmelCase = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
UpperCAmelCase = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def A ( self : Optional[Any] ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase , expected_max_diff=5E-3 )
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def A ( self : Tuple ):
'''simple docstring'''
pass
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def A ( self : Any ):
'''simple docstring'''
pass
@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
def A ( self : Optional[int] ):
'''simple docstring'''
pass
def A ( self : List[Any] ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class _a ( unittest.TestCase ):
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = VideoToVideoSDPipeline.from_pretrained('''cerspense/zeroscope_v2_XL''' , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
UpperCAmelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase = torch.randn((1, 10, 3, 1_024, 576) , generator=lowercase )
UpperCAmelCase = video.to('''cuda''' )
UpperCAmelCase = '''Spiderman is surfing'''
UpperCAmelCase = pipe(lowercase , video=lowercase , generator=lowercase , num_inference_steps=3 , output_type='''pt''' ).frames
UpperCAmelCase = np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1E-2
| 34
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
UpperCAmelCase = m
else:
UpperCAmelCase = m # noqa: E741
return r
def snake_case_ (_a : list[int] ):
if len(_a ) == 0:
return 0
UpperCAmelCase = [0] * len(_a )
UpperCAmelCase = 1
UpperCAmelCase = v[0]
for i in range(1 , len(_a ) ):
if v[i] < tail[0]:
UpperCAmelCase = v[i]
elif v[i] > tail[length - 1]:
UpperCAmelCase = v[i]
length += 1
else:
UpperCAmelCase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : Any ):
UpperCAmelCase = []
UpperCAmelCase = set({'''(''', '''[''', '''{'''} )
UpperCAmelCase = set({''')''', ''']''', '''}'''} )
UpperCAmelCase = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
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 snake_case_ ():
UpperCAmelCase = input('''Enter sequence of brackets: ''' )
if is_balanced(_a ):
print(_a , '''is balanced''' )
else:
print(_a , '''is not balanced''' )
if __name__ == "__main__":
main()
| 34
|
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase = [[0 for i in range(_a )] for j in range(_a )]
# since string of zero length match pattern of zero length
UpperCAmelCase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _a ):
UpperCAmelCase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _a ):
UpperCAmelCase = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _a ):
for j in range(1 , _a ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase = dp[i - 1][j]
else:
UpperCAmelCase = 0
else:
UpperCAmelCase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A ='aab'
A ='c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"""{input_string} matches the given pattern {pattern}""")
else:
print(f"""{input_string} does not match with the given pattern {pattern}""")
| 34
| 1
|
'''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 ={
'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _a ( __a ):
__a : Dict = """levit"""
def __init__( self : List[Any] , lowercase : List[str]=224 , lowercase : List[Any]=3 , lowercase : Any=3 , lowercase : Union[str, Any]=2 , lowercase : int=1 , lowercase : List[str]=16 , lowercase : Tuple=[128, 256, 384] , lowercase : Optional[Any]=[4, 8, 12] , lowercase : str=[4, 4, 4] , lowercase : Optional[int]=[16, 16, 16] , lowercase : List[Any]=0 , lowercase : Optional[int]=[2, 2, 2] , lowercase : List[str]=[2, 2, 2] , lowercase : Optional[Any]=0.02 , **lowercase : Dict , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = image_size
UpperCAmelCase = num_channels
UpperCAmelCase = kernel_size
UpperCAmelCase = stride
UpperCAmelCase = padding
UpperCAmelCase = hidden_sizes
UpperCAmelCase = num_attention_heads
UpperCAmelCase = depths
UpperCAmelCase = key_dim
UpperCAmelCase = drop_path_rate
UpperCAmelCase = patch_size
UpperCAmelCase = attention_ratio
UpperCAmelCase = mlp_ratio
UpperCAmelCase = initializer_range
UpperCAmelCase = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _a ( __a ):
__a : Tuple = version.parse("""1.11""" )
@property
def A ( self : Tuple ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def A ( self : Dict ):
'''simple docstring'''
return 1E-4
| 34
|
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A ='pt'
elif is_tf_available():
A ='tf'
else:
A ='jax'
class _a ( __a , unittest.TestCase ):
__a : Optional[Any] = PerceiverTokenizer
__a : str = False
def A ( self : Union[str, Any] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A ( self : Optional[int] ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def A ( self : Union[str, Any] , **lowercase : int ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[str]=False , lowercase : Union[str, Any]=20 , lowercase : Union[str, Any]=5 ):
'''simple docstring'''
UpperCAmelCase = []
for i in range(len(lowercase ) ):
try:
UpperCAmelCase = tokenizer.decode([i] , clean_up_tokenization_spaces=lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
UpperCAmelCase = list(filter(lambda lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , lowercase ) )
UpperCAmelCase = list(filter(lambda lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=lowercase ) , lowercase ) )
if max_length is not None and len(lowercase ) > max_length:
UpperCAmelCase = toks[:max_length]
if min_length is not None and len(lowercase ) < min_length and len(lowercase ) > 0:
while len(lowercase ) < min_length:
UpperCAmelCase = toks + toks
# toks_str = [t[1] for t in toks]
UpperCAmelCase = [t[0] for t in toks]
# Ensure consistency
UpperCAmelCase = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
if " " not in output_txt and len(lowercase ) > 1:
UpperCAmelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=lowercase )
)
if with_prefix_space:
UpperCAmelCase = ''' ''' + output_txt
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
return output_txt, output_ids
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = '''Unicode €.'''
UpperCAmelCase = tokenizer(lowercase )
UpperCAmelCase = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]Unicode €.[SEP]''' )
UpperCAmelCase = tokenizer('''e è é ê ë''' )
UpperCAmelCase = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
UpperCAmelCase = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
self.assertIsInstance(lowercase , lowercase )
if FRAMEWORK != "jax":
UpperCAmelCase = list(batch.input_ids.numpy()[0] )
else:
UpperCAmelCase = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowercase , lowercase )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , lowercase )
self.assertIn('''attention_mask''' , lowercase )
self.assertNotIn('''decoder_input_ids''' , lowercase )
self.assertNotIn('''decoder_attention_mask''' , lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = [
'''Summary of the text.''',
'''Another summary.''',
]
UpperCAmelCase = tokenizer(
text_target=lowercase , max_length=32 , padding='''max_length''' , truncation=lowercase , return_tensors=lowercase )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
shutil.rmtree(lowercase )
UpperCAmelCase = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
UpperCAmelCase = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowercase )
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
UpperCAmelCase = [f"<extra_id_{i}>" for i in range(125 )]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['''an_additional_special_token'''] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
UpperCAmelCase = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=lowercase )]
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , additional_special_tokens=lowercase , )
self.assertIn('''a_new_additional_special_token''' , tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ) , )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ) , '''�''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
pass
def A ( self : Any ):
'''simple docstring'''
pass
def A ( self : Dict ):
'''simple docstring'''
pass
def A ( self : str ):
'''simple docstring'''
pass
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers(fast=lowercase , do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
UpperCAmelCase = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
UpperCAmelCase = tokenizer.convert_tokens_to_string(lowercase )
self.assertIsInstance(lowercase , lowercase )
| 34
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}
class _a ( __a ):
__a : int = """data2vec-text"""
def __init__( self : Tuple , lowercase : List[str]=30_522 , lowercase : Union[str, Any]=768 , lowercase : Dict=12 , lowercase : List[Any]=12 , lowercase : Union[str, Any]=3_072 , lowercase : Any="gelu" , lowercase : Optional[int]=0.1 , lowercase : str=0.1 , lowercase : List[str]=512 , lowercase : Optional[int]=2 , lowercase : int=0.02 , lowercase : int=1E-12 , lowercase : Union[str, Any]=1 , lowercase : List[str]=0 , lowercase : int=2 , lowercase : Tuple="absolute" , lowercase : Optional[int]=True , lowercase : Dict=None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = hidden_act
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = position_embedding_type
UpperCAmelCase = use_cache
UpperCAmelCase = classifier_dropout
class _a ( __a ):
@property
def A ( self : List[Any] ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 34
|
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return val
return default
def snake_case_ (_a : Dict , _a : Any=False ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return strtobool(_a ) == 1 # As its name indicates `strtobool` actually returns an int...
def snake_case_ (_a : str , _a : Optional[Any]="no" ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return value
| 34
| 1
|
'''simple docstring'''
import qiskit
def snake_case_ (_a : int , _a : int ):
UpperCAmelCase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
UpperCAmelCase = qiskit.QuantumCircuit(_a , _a )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
UpperCAmelCase = qiskit.execute(_a , _a , shots=1_0_0_0 )
# 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)}""")
| 34
|
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A =logging.getLogger(__name__)
A ='Hello world! cécé herlolip'
A =namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def snake_case_ (_a : List[Any] , _a : Any ):
UpperCAmelCase = BertAbsConfig(
temp_dir='''.''' , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder='''bert''' , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
UpperCAmelCase = torch.load(_a , lambda _a , _a : storage )
UpperCAmelCase = AbsSummarizer(_a , torch.device('''cpu''' ) , _a )
original.eval()
UpperCAmelCase = BertAbsSummarizer(_a , torch.device('''cpu''' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('''convert the model''' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('''Make sure that the models\' outputs are identical''' )
UpperCAmelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
# prepare the model inputs
UpperCAmelCase = tokenizer.encode('''This is sample éàalj\'-.''' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
UpperCAmelCase = tokenizer.encode('''This is sample 3 éàalj\'-.''' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
UpperCAmelCase = encoder_input_ids
UpperCAmelCase = decoder_input_ids
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
UpperCAmelCase = original(_a , _a , _a , _a , _a , _a , _a )[0]
UpperCAmelCase = original.generator(_a )
UpperCAmelCase = new_model(
_a , _a , _a , _a , _a )[0]
UpperCAmelCase = new_model.generator(_a )
UpperCAmelCase = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.allclose(_a , _a , atol=1E-3 )
if are_identical:
logging.info('''all weights are equal up to 1e-3''' )
else:
raise ValueError('''the weights are different. The new model is likely different from the original one.''' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('''saving the model\'s state dictionary''' )
torch.save(
new_model.state_dict() , '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''' )
if __name__ == "__main__":
A =argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
A =parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 34
| 1
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
UpperCAmelCase = m
else:
UpperCAmelCase = m # noqa: E741
return r
def snake_case_ (_a : list[int] ):
if len(_a ) == 0:
return 0
UpperCAmelCase = [0] * len(_a )
UpperCAmelCase = 1
UpperCAmelCase = v[0]
for i in range(1 , len(_a ) ):
if v[i] < tail[0]:
UpperCAmelCase = v[i]
elif v[i] > tail[length - 1]:
UpperCAmelCase = v[i]
length += 1
else:
UpperCAmelCase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : List[Any] , **lowercase : List[str] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : int ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : int , *lowercase : Tuple , **lowercase : Dict ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : List[str] , *lowercase : Optional[int] , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Union[str, Any] , **lowercase : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[int] , *lowercase : Union[str, Any] , **lowercase : Any ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Any , *lowercase : Dict , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : Any = ["""flax""", """transformers"""]
def __init__( self : Any , *lowercase : Optional[Any] , **lowercase : Optional[int] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Optional[Any] , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : str , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : int ):
if not isinstance(_a , _a ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''' )
UpperCAmelCase = 0
UpperCAmelCase = str(_a )
while len(_a ) != 1:
UpperCAmelCase = [int(_a ) for i in num_string]
UpperCAmelCase = 1
for i in range(0 , len(_a ) ):
total *= numbers[i]
UpperCAmelCase = str(_a )
steps += 1
return steps
def snake_case_ (_a : int ):
if not isinstance(_a , _a ):
raise ValueError('''additive_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''' )
UpperCAmelCase = 0
UpperCAmelCase = str(_a )
while len(_a ) != 1:
UpperCAmelCase = [int(_a ) for i in num_string]
UpperCAmelCase = 0
for i in range(0 , len(_a ) ):
total += numbers[i]
UpperCAmelCase = str(_a )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
|
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
A ={
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
A ={
'b0': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 2_24,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 2_40,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 14_08,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 2_60,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 15_36,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 3_00,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 17_92,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 3_80,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 20_48,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 4_56,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 23_04,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 5_28,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 25_60,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 6_00,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def snake_case_ (_a : List[str] ):
UpperCAmelCase = EfficientNetConfig()
UpperCAmelCase = CONFIG_MAP[model_name]['''hidden_dim''']
UpperCAmelCase = CONFIG_MAP[model_name]['''width_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''depth_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dropout_rate''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dw_padding''']
UpperCAmelCase = '''huggingface/label-files'''
UpperCAmelCase = '''imagenet-1k-id2label.json'''
UpperCAmelCase = 1_0_0_0
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ():
UpperCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase = Image.open(requests.get(_a , stream=_a ).raw )
return im
def snake_case_ (_a : str ):
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163] , do_center_crop=_a , )
return preprocessor
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
UpperCAmelCase = sorted(set(_a ) )
UpperCAmelCase = len(_a )
UpperCAmelCase = {b: str(_a ) for b, i in zip(_a , range(_a ) )}
UpperCAmelCase = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
UpperCAmelCase = block_name_mapping[b]
rename_keys.append((F"block{b}_expand_conv/kernel:0", F"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((F"block{b}_expand_bn/gamma:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((F"block{b}_expand_bn/beta:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(F"block{b}_expand_bn/moving_mean:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(F"block{b}_expand_bn/moving_variance:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(F"block{b}_dwconv/depthwise_kernel:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((F"block{b}_bn/gamma:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((F"block{b}_bn/beta:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(F"block{b}_bn/moving_mean:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(F"block{b}_bn/moving_variance:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((F"block{b}_se_reduce/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((F"block{b}_se_reduce/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((F"block{b}_se_expand/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((F"block{b}_se_expand/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(F"block{b}_project_conv/kernel:0", F"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((F"block{b}_project_bn/gamma:0", F"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((F"block{b}_project_bn/beta:0", F"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(F"block{b}_project_bn/moving_mean:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(F"block{b}_project_bn/moving_variance:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
UpperCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCAmelCase = '''efficientnet.''' + item[1]
UpperCAmelCase = '''classifier.weight'''
UpperCAmelCase = '''classifier.bias'''
return key_mapping
def snake_case_ (_a : Dict , _a : List[str] , _a : Dict ):
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
UpperCAmelCase = torch.from_numpy(np.transpose(_a ) )
else:
UpperCAmelCase = torch.from_numpy(_a )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_a )
@torch.no_grad()
def snake_case_ (_a : Optional[Any] , _a : List[str] , _a : Optional[int] , _a : Dict ):
UpperCAmelCase = model_classes[model_name](
include_top=_a , weights='''imagenet''' , input_tensor=_a , input_shape=_a , pooling=_a , classes=1_0_0_0 , classifier_activation='''softmax''' , )
UpperCAmelCase = original_model.trainable_variables
UpperCAmelCase = original_model.non_trainable_variables
UpperCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCAmelCase = param.numpy()
UpperCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
UpperCAmelCase = get_efficientnet_config(_a )
UpperCAmelCase = EfficientNetForImageClassification(_a ).eval()
UpperCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
UpperCAmelCase = rename_keys(_a )
replace_params(_a , _a , _a )
# Initialize preprocessor and preprocess input image
UpperCAmelCase = convert_image_processor(_a )
UpperCAmelCase = preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCAmelCase = hf_model(**_a )
UpperCAmelCase = outputs.logits.detach().numpy()
# Original model inference
UpperCAmelCase = False
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
UpperCAmelCase = image.img_to_array(_a )
UpperCAmelCase = np.expand_dims(_a , axis=0 )
UpperCAmelCase = original_model.predict(_a )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_a , _a , atol=1E-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(_a ):
os.mkdir(_a )
# Save converted model and image processor
hf_model.save_pretrained(_a )
preprocessor.save_pretrained(_a )
if push_to_hub:
# Push model and image processor to hub
print(F"Pushing converted {model_name} to the hub..." )
UpperCAmelCase = F"efficientnet-{model_name}"
preprocessor.push_to_hub(_a )
hf_model.push_to_hub(_a )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
A =parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _a ( unittest.TestCase ):
def A ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase = '''xvjiarui/stable-diffusion-2-inpainting'''
UpperCAmelCase , UpperCAmelCase = FlaxStableDiffusionInpaintPipeline.from_pretrained(lowercase , safety_checker=lowercase )
UpperCAmelCase = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase = jax.random.PRNGKey(0 )
UpperCAmelCase = 50
UpperCAmelCase = jax.device_count()
UpperCAmelCase = num_samples * [prompt]
UpperCAmelCase = num_samples * [init_image]
UpperCAmelCase = num_samples * [mask_image]
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = pipeline.prepare_inputs(lowercase , lowercase , lowercase )
# shard inputs and rng
UpperCAmelCase = replicate(lowercase )
UpperCAmelCase = jax.random.split(lowercase , jax.device_count() )
UpperCAmelCase = shard(lowercase )
UpperCAmelCase = shard(lowercase )
UpperCAmelCase = shard(lowercase )
UpperCAmelCase = pipeline(
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , jit=lowercase )
UpperCAmelCase = output.images.reshape(lowercase , 512 , 512 , 3 )
UpperCAmelCase = images[0, 253:256, 253:256, -1]
UpperCAmelCase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCAmelCase = jnp.array(
[0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 34
|
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
A =soup.find('meta', {'property': 'og:image'})['content']
A =requests.get(image_url).content
A =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(f"""Done. Image saved to disk as {file_name}.""")
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : int ):
stooge(_a , 0 , len(_a ) - 1 )
return arr
def snake_case_ (_a : Tuple , _a : Optional[Any] , _a : List[str] ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
UpperCAmelCase , UpperCAmelCase = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
UpperCAmelCase = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(_a , _a , (h - t) )
# Recursively sort last 2/3 elements
stooge(_a , i + t , (_a) )
# Recursively sort first 2/3 elements
stooge(_a , _a , (h - t) )
if __name__ == "__main__":
A =input('Enter numbers separated by a comma:\n').strip()
A =[int(item) for item in user_input.split(',')]
print(stooge_sort(unsorted))
| 34
|
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _a ( __a ):
__a : str = ["""vqvae"""]
def __init__( self : str , lowercase : AutoencoderKL , lowercase : UNetaDConditionModel , lowercase : Mel , lowercase : Union[DDIMScheduler, DDPMScheduler] , ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowercase , scheduler=lowercase , mel=lowercase , vqvae=lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
return 50 if isinstance(self.scheduler , lowercase ) else 1_000
@torch.no_grad()
def __call__( self : Optional[Any] , lowercase : int = 1 , lowercase : str = None , lowercase : np.ndarray = None , lowercase : int = 0 , lowercase : int = 0 , lowercase : int = None , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : float = 0 , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : torch.Tensor = None , lowercase : torch.Tensor = None , lowercase : Tuple=True , ):
'''simple docstring'''
UpperCAmelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowercase , device=self.device , )
UpperCAmelCase = noise
UpperCAmelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowercase , lowercase )
UpperCAmelCase = self.mel.audio_slice_to_image(lowercase )
UpperCAmelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase = (input_image / 255) * 2 - 1
UpperCAmelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase = self.vqvae.encode(torch.unsqueeze(lowercase , 0 ) ).latent_dist.sample(
generator=lowercase )[0]
UpperCAmelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase = int(mask_start_secs * pixels_per_second )
UpperCAmelCase = int(mask_end_secs * pixels_per_second )
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowercase ):
UpperCAmelCase = self.unet(lowercase , lowercase , lowercase )['''sample''']
else:
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
if isinstance(self.scheduler , lowercase ):
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , eta=lowercase , generator=lowercase , )['''prev_sample''']
else:
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , generator=lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
UpperCAmelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase = self.vqvae.decode(lowercase )['''sample''']
UpperCAmelCase = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase = (images * 255).round().astype('''uint8''' )
UpperCAmelCase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
UpperCAmelCase = [self.mel.image_to_audio(lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowercase ) )
@torch.no_grad()
def A ( self : Dict , lowercase : List[Image.Image] , lowercase : int = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler , lowercase )
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase = (sample / 255) * 2 - 1
UpperCAmelCase = torch.Tensor(lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase = self.scheduler.alphas_cumprod[t]
UpperCAmelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase = 1 - alpha_prod_t
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def A ( lowercase : torch.Tensor , lowercase : torch.Tensor , lowercase : float ):
'''simple docstring'''
UpperCAmelCase = acos(torch.dot(torch.flatten(lowercase ) , torch.flatten(lowercase ) ) / torch.norm(lowercase ) / torch.norm(lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(lowercase ) + sin(alpha * theta ) * xa / sin(lowercase )
| 34
| 1
|
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
A =logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
A ={
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'summarization': AutoModelForSeqaSeqLM,
'translation': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
A ={
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
A =sorted(arg_to_scheduler.keys())
A ='{' + ', '.join(arg_to_scheduler_choices) + '}'
class _a ( pl.LightningModule ):
def __init__( self : List[str] , lowercase : argparse.Namespace , lowercase : List[Any]=None , lowercase : Dict="base" , lowercase : Optional[int]=None , lowercase : Dict=None , lowercase : Tuple=None , **lowercase : Optional[int] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowercase )
UpperCAmelCase = 0
UpperCAmelCase = Path(self.hparams.output_dir )
UpperCAmelCase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
UpperCAmelCase = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'''num_labels''': num_labels} if num_labels is not None else {}) , cache_dir=lowercase , **lowercase , )
else:
UpperCAmelCase = config
UpperCAmelCase = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , lowercase , lowercase ):
assert hasattr(self.config , lowercase ), f"model config doesn't have a `{p}` attribute"
setattr(self.config , lowercase , getattr(self.hparams , lowercase ) )
if tokenizer is None:
UpperCAmelCase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowercase , )
else:
UpperCAmelCase = tokenizer
UpperCAmelCase = MODEL_MODES[mode]
if model is None:
UpperCAmelCase = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=lowercase , )
else:
UpperCAmelCase = model
def A ( self : List[Any] , *lowercase : List[str] , **lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.model_type.from_pretrained(*lowercase , **lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.hparams.lr_scheduler]
UpperCAmelCase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
UpperCAmelCase = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''params''': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'''weight_decay''': self.hparams.weight_decay,
},
{
'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
if self.hparams.adafactor:
UpperCAmelCase = Adafactor(
lowercase , lr=self.hparams.learning_rate , scale_parameter=lowercase , relative_step=lowercase )
else:
UpperCAmelCase = AdamW(
lowercase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
UpperCAmelCase = optimizer
UpperCAmelCase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A ( self : List[Any] , lowercase : int , lowercase : List[str] ):
'''simple docstring'''
return self.validation_step(lowercase , lowercase )
def A ( self : List[Any] , lowercase : Tuple ):
'''simple docstring'''
return self.validation_end(lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
UpperCAmelCase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A ( self : List[str] , lowercase : Any ):
'''simple docstring'''
if stage == "test":
UpperCAmelCase = len(self.test_dataloader().dataset )
else:
UpperCAmelCase = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=lowercase )
UpperCAmelCase = len(self.train_dataloader().dataset )
def A ( self : List[str] , lowercase : str , lowercase : int , lowercase : bool = False ):
'''simple docstring'''
raise NotImplementedError('''You must implement this for your task''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
return self.train_loader
def A ( self : Optional[Any] ):
'''simple docstring'''
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
lowercase , list(filter(lowercase , self.hparams.model_name_or_path.split('''/''' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A ( self : List[str] , lowercase : Dict[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.output_dir.joinpath('''best_tfmr''' )
UpperCAmelCase = self.step_count
self.model.save_pretrained(lowercase )
self.tokenizer.save_pretrained(lowercase )
@staticmethod
def A ( lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
parser.add_argument(
'''--model_name_or_path''' , default=lowercase , type=lowercase , required=lowercase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=lowercase , help='''Pretrained config name or path if not the same as model_name''' )
parser.add_argument(
'''--tokenizer_name''' , default=lowercase , type=lowercase , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(lowercase ).parent / '''test_run''' / '''cache''' ) , type=lowercase , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=lowercase , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=lowercase , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=lowercase , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=lowercase , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5E-5 , type=lowercase , help='''The initial learning rate for Adam.''' )
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=lowercase , metavar=lowercase , type=lowercase , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=lowercase , help='''Weight decay if we apply some.''' )
parser.add_argument('''--adam_epsilon''' , default=1E-8 , type=lowercase , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--warmup_steps''' , default=0 , type=lowercase , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--num_workers''' , default=4 , type=lowercase , help='''kwarg passed to DataLoader''' )
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=lowercase )
parser.add_argument('''--train_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--eval_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--adafactor''' , action='''store_true''' )
class _a ( pl.Callback ):
def A ( self : Dict , lowercase : Optional[Any] , lowercase : List[Any] ):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Union[str, Any] , lowercase : Any ):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowercase )
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Optional[int] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = trainer.lr_schedulers[0]['''scheduler''']
UpperCAmelCase = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowercase )
def A ( self : Tuple , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Validation results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log results
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def A ( self : Dict , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Test results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log and save results to file
UpperCAmelCase = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''' )
with open(lowercase , '''w''' ) as writer:
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
writer.write('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def snake_case_ (_a : int , _a : Optional[Any] ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'''--output_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_a , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument(
'''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , )
parser.add_argument(
'''--fp16_opt_level''' , type=_a , default='''O2''' , help=(
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
'''See details at https://nvidia.github.io/apex/amp.html'''
) , )
parser.add_argument('''--n_tpu_cores''' , dest='''tpu_cores''' , type=_a )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_a , help='''Max gradient norm''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_predict''' , action='''store_true''' , help='''Whether to run predictions on the test set.''' )
parser.add_argument(
'''--gradient_accumulation_steps''' , dest='''accumulate_grad_batches''' , type=_a , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_a , default=4_2 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_a , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def snake_case_ (_a : BaseTransformer , _a : argparse.Namespace , _a : List[Any]=None , _a : Tuple=True , _a : int=[] , _a : Any=None , _a : int=None , **_a : Optional[Any] , ):
pl.seed_everything(args.seed )
# init model
UpperCAmelCase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_a )
# add custom checkpoints
if checkpoint_callback is None:
UpperCAmelCase = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='''checkpoint''' , monitor='''val_loss''' , mode='''min''' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_a )
if logging_callback is None:
UpperCAmelCase = LoggingCallback()
UpperCAmelCase = {}
if args.fpaa:
UpperCAmelCase = 1_6
if args.gpus > 1:
UpperCAmelCase = '''auto'''
UpperCAmelCase = '''ddp'''
UpperCAmelCase = args.accumulate_grad_batches
UpperCAmelCase = None
UpperCAmelCase = '''auto'''
UpperCAmelCase = pl.Trainer.from_argparse_args(
_a , weights_summary=_a , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_a , val_check_interval=1 , num_sanity_val_steps=2 , **_a , )
if args.do_train:
trainer.fit(_a )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 34
|
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
A =logging.get_logger(__name__)
A =TypeVar('DatasetType', Dataset, IterableDataset)
def snake_case_ (_a : List[DatasetType] , _a : Optional[List[float]] = None , _a : Optional[int] = None , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
else:
return _interleave_iterable_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
def snake_case_ (_a : List[DatasetType] , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : int = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_a , info=_a , split=_a , axis=_a )
else:
return _concatenate_iterable_datasets(_a , info=_a , split=_a , axis=_a )
| 34
| 1
|
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
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
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
A =get_tests_dir('fixtures')
A =get_tests_dir('fixtures/dummy_feature_extractor_config.json')
A =get_tests_dir('fixtures/dummy-config.json')
class _a ( unittest.TestCase ):
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = 0
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h''' )
self.assertIsInstance(lowercase , lowercase )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A ( self : Any ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowercase ).to_dict()
config_dict.pop('''feature_extractor_type''' )
UpperCAmelCase = WavaVecaFeatureExtractor(**lowercase )
# save in new folder
model_config.save_pretrained(lowercase )
config.save_pretrained(lowercase )
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowercase )
# make sure private variable is not incorrectly saved
UpperCAmelCase = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(lowercase , lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A ( self : str ):
'''simple docstring'''
with self.assertRaisesRegex(
lowercase , '''bert-base is not a local folder and is not a valid model identifier''' ):
UpperCAmelCase = AutoFeatureExtractor.from_pretrained('''bert-base''' )
def A ( self : Optional[Any] ):
'''simple docstring'''
with self.assertRaisesRegex(
lowercase , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowercase , revision='''aaaaaa''' )
def A ( self : Tuple ):
'''simple docstring'''
with self.assertRaisesRegex(
lowercase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
UpperCAmelCase = AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model''' )
def A ( self : List[str] ):
'''simple docstring'''
with self.assertRaises(lowercase ):
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowercase ):
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' , trust_remote_code=lowercase )
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' , trust_remote_code=lowercase )
self.assertEqual(feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowercase )
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowercase , trust_remote_code=lowercase )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
def A ( self : List[Any] ):
'''simple docstring'''
try:
AutoConfig.register('''custom''' , lowercase )
AutoFeatureExtractor.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
AutoFeatureExtractor.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
UpperCAmelCase = CustomFeatureExtractor.from_pretrained(lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowercase )
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def A ( self : str ):
'''simple docstring'''
class _a ( __a ):
__a : Optional[Any] = True
try:
AutoConfig.register('''custom''' , lowercase )
AutoFeatureExtractor.register(lowercase , lowercase )
# If remote code is not set, the default is to use local
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' )
self.assertEqual(feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' , trust_remote_code=lowercase )
self.assertEqual(feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' , trust_remote_code=lowercase )
self.assertEqual(feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
self.assertTrue(not hasattr(lowercase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 34
|
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def snake_case_ (_a : Tuple ):
return {key.lstrip('''-''' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def snake_case_ ():
UpperCAmelCase = ArgumentParser(
'''HuggingFace Datasets CLI tool''' , usage='''datasets-cli <command> [<args>]''' , allow_abbrev=_a )
UpperCAmelCase = parser.add_subparsers(help='''datasets-cli command helpers''' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(_a )
EnvironmentCommand.register_subcommand(_a )
TestCommand.register_subcommand(_a )
RunBeamCommand.register_subcommand(_a )
DummyDataCommand.register_subcommand(_a )
# Parse args
UpperCAmelCase , UpperCAmelCase = parser.parse_known_args()
if not hasattr(_a , '''func''' ):
parser.print_help()
exit(1 )
UpperCAmelCase = parse_unknown_args(_a )
# Run
UpperCAmelCase = args.func(_a , **_a )
service.run()
if __name__ == "__main__":
main()
| 34
| 1
|
'''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_retribert import RetriBertTokenizer
A =logging.get_logger(__name__)
A ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A ={
'vocab_file': {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
),
},
}
A ={
'yjernite/retribert-base-uncased': 5_12,
}
A ={
'yjernite/retribert-base-uncased': {'do_lower_case': True},
}
class _a ( __a ):
__a : List[str] = VOCAB_FILES_NAMES
__a : List[str] = PRETRAINED_VOCAB_FILES_MAP
__a : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a : Dict = PRETRAINED_INIT_CONFIGURATION
__a : int = RetriBertTokenizer
__a : Optional[Any] = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , lowercase : int=None , lowercase : Dict=None , lowercase : Dict=True , lowercase : Optional[Any]="[UNK]" , lowercase : Any="[SEP]" , lowercase : List[str]="[PAD]" , lowercase : Dict="[CLS]" , lowercase : List[Any]="[MASK]" , lowercase : Optional[int]=True , lowercase : Dict=None , **lowercase : Optional[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 = 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 = getattr(lowercase , normalizer_state.pop('''type''' ) )
UpperCAmelCase = do_lower_case
UpperCAmelCase = strip_accents
UpperCAmelCase = tokenize_chinese_chars
UpperCAmelCase = normalizer_class(**lowercase )
UpperCAmelCase = do_lower_case
def A ( self : str , lowercase : Optional[int] , lowercase : int=None ):
'''simple docstring'''
UpperCAmelCase = [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 A ( self : List[str] , lowercase : List[int] , lowercase : Optional[List[int]] = None ):
'''simple docstring'''
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 ) * [0] + len(token_ids_a + sep ) * [1]
def A ( self : int , lowercase : str , lowercase : Optional[str] = None ):
'''simple docstring'''
UpperCAmelCase = self._tokenizer.model.save(lowercase , name=lowercase )
return tuple(lowercase )
| 34
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
A =logging.getLogger()
def snake_case_ ():
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
UpperCAmelCase = parser.parse_args()
return args.f
def snake_case_ (_a : List[str] , _a : Union[str, Any]="eval" ):
UpperCAmelCase = os.path.join(_a , F"{split}_results.json" )
if os.path.exists(_a ):
with open(_a , '''r''' ) as f:
return json.load(_a )
raise ValueError(F"can't find {path}" )
A =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _a ( __a ):
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_glue.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
@slow
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_clm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 100 )
@slow
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_summarization_flax.main()
UpperCAmelCase = get_results(lowercase , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_ta_mlm_flax.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.42 )
@slow
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = 7 if get_gpu_count() > 1 else 2
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_flax_ner.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.get_auto_remove_tmp_dir()
UpperCAmelCase = f"\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n ".split()
with patch.object(lowercase , '''argv''' , lowercase ):
run_qa.main()
UpperCAmelCase = get_results(lowercase )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 34
| 1
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
A ='\nHuman: <<task>>\n\nAssistant: '
A ='huggingface-tools/default-prompts'
A ={'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}
def snake_case_ (_a : Dict , _a : Any , _a : str="run" ):
if prompt_or_repo_id is None:
UpperCAmelCase = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('''\\s''' , _a ) is not None:
return prompt_or_repo_id
UpperCAmelCase = cached_file(
_a , PROMPT_FILES[mode] , repo_type='''dataset''' , user_agent={'''agent''': agent_name} )
with open(_a , '''r''' , encoding='''utf-8''' ) as f:
return f.read()
| 34
|
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[str] ):
'''simple docstring'''
for word in words:
self.insert(lowercase )
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
UpperCAmelCase = TrieNode()
UpperCAmelCase = curr.nodes[char]
UpperCAmelCase = True
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self
for char in word:
if char not in curr.nodes:
return False
UpperCAmelCase = curr.nodes[char]
return curr.is_leaf
def A ( self : str , lowercase : str ):
'''simple docstring'''
def _delete(lowercase : TrieNode , lowercase : str , lowercase : int ) -> bool:
if index == len(lowercase ):
# If word does not exist
if not curr.is_leaf:
return False
UpperCAmelCase = False
return len(curr.nodes ) == 0
UpperCAmelCase = word[index]
UpperCAmelCase = curr.nodes.get(lowercase )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
UpperCAmelCase = _delete(lowercase , lowercase , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , lowercase , 0 )
def snake_case_ (_a : TrieNode , _a : str ):
if node.is_leaf:
print(_a , end=''' ''' )
for key, value in node.nodes.items():
print_words(_a , word + key )
def snake_case_ ():
UpperCAmelCase = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase = TrieNode()
root.insert_many(_a )
# print_words(root, "")
assert all(root.find(_a ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def snake_case_ (_a : str , _a : bool ):
print(str(_a ) , '''works!''' if passes else '''doesn\'t work :(''' )
def snake_case_ ():
assert test_trie()
def snake_case_ ():
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class _a :
def __init__( self : List[str] , lowercase : Optional[int] , lowercase : List[str]=None , lowercase : Tuple=None , lowercase : int=None , lowercase : List[str]="resnet50" , lowercase : Optional[Any]=3 , lowercase : str=32 , lowercase : List[Any]=3 , lowercase : int=True , lowercase : Any=True , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = out_indices if out_indices is not None else [4]
UpperCAmelCase = stage_names
UpperCAmelCase = out_features
UpperCAmelCase = backbone
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = num_channels
UpperCAmelCase = use_pretrained_backbone
UpperCAmelCase = is_training
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = self.get_config()
return config, pixel_values
def A ( self : Optional[int] ):
'''simple docstring'''
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def A ( self : Tuple , lowercase : List[Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = TimmBackbone(config=lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
UpperCAmelCase = model(lowercase )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class _a ( __a , __a , __a , unittest.TestCase ):
__a : Optional[int] = (TimmBackbone,) if is_torch_available() else ()
__a : Dict = {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
__a : Optional[Any] = False
__a : List[str] = False
__a : int = False
__a : Optional[Any] = False
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = TimmBackboneModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase )
def A ( self : str ):
'''simple docstring'''
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = '''resnet18'''
UpperCAmelCase = '''microsoft/resnet-18'''
UpperCAmelCase = AutoBackbone.from_pretrained(lowercase , use_timm_backbone=lowercase )
UpperCAmelCase = AutoBackbone.from_pretrained(lowercase )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
UpperCAmelCase = AutoBackbone.from_pretrained(lowercase , use_timm_backbone=lowercase , out_indices=[1, 2, 3] )
UpperCAmelCase = AutoBackbone.from_pretrained(lowercase , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking''' )
def A ( self : int ):
'''simple docstring'''
pass
@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''' )
def A ( self : int ):
'''simple docstring'''
pass
@unittest.skip('''TimmBackbone initialization is managed on the timm side''' )
def A ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''' )
def A ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint''' )
def A ( self : int ):
'''simple docstring'''
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def A ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('''model weights aren\'t tied in TimmBackbone.''' )
def A ( self : Tuple ):
'''simple docstring'''
pass
@unittest.skip('''model weights aren\'t tied in TimmBackbone.''' )
def A ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def A ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def A ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.''' )
def A ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip('''TimmBackbone doesn\'t support output_attentions.''' )
def A ( self : Tuple ):
'''simple docstring'''
pass
@unittest.skip('''Safetensors is not supported by timm.''' )
def A ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def A ( self : int ):
'''simple docstring'''
pass
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = True
UpperCAmelCase = self.has_attentions
# no need to test all models as different heads yield the same functionality
UpperCAmelCase = self.all_model_classes[0]
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
UpperCAmelCase = self._prepare_for_class(lowercase , lowercase )
UpperCAmelCase = model(**lowercase )
UpperCAmelCase = outputs[0][-1]
# Encoder-/Decoder-only models
UpperCAmelCase = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
UpperCAmelCase = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=lowercase )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(**lowercase )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
UpperCAmelCase = copy.deepcopy(lowercase )
UpperCAmelCase = None
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(**lowercase )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
UpperCAmelCase = copy.deepcopy(lowercase )
UpperCAmelCase = False
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(**lowercase )
| 34
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}
class _a ( __a ):
__a : Union[str, Any] = """encodec"""
def __init__( self : Tuple , lowercase : List[str]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowercase : Any=24_000 , lowercase : str=1 , lowercase : Optional[int]=False , lowercase : Optional[Any]=None , lowercase : str=None , lowercase : Tuple=128 , lowercase : Union[str, Any]=32 , lowercase : Union[str, Any]=1 , lowercase : Optional[Any]=[8, 5, 4, 2] , lowercase : Any="weight_norm" , lowercase : Tuple=7 , lowercase : int=7 , lowercase : Dict=3 , lowercase : List[Any]=2 , lowercase : str=True , lowercase : List[str]="reflect" , lowercase : List[Any]=2 , lowercase : Optional[Any]=2 , lowercase : int=1.0 , lowercase : Dict=1_024 , lowercase : str=None , lowercase : Union[str, Any]=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = target_bandwidths
UpperCAmelCase = sampling_rate
UpperCAmelCase = audio_channels
UpperCAmelCase = normalize
UpperCAmelCase = chunk_length_s
UpperCAmelCase = overlap
UpperCAmelCase = hidden_size
UpperCAmelCase = num_filters
UpperCAmelCase = num_residual_layers
UpperCAmelCase = upsampling_ratios
UpperCAmelCase = norm_type
UpperCAmelCase = kernel_size
UpperCAmelCase = last_kernel_size
UpperCAmelCase = residual_kernel_size
UpperCAmelCase = dilation_growth_rate
UpperCAmelCase = use_causal_conv
UpperCAmelCase = pad_mode
UpperCAmelCase = compress
UpperCAmelCase = num_lstm_layers
UpperCAmelCase = trim_right_ratio
UpperCAmelCase = codebook_size
UpperCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}" )
super().__init__(**lowercase )
@property
def A ( self : Dict ):
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 34
| 1
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _a ( __a , unittest.TestCase ):
__a : int = UnCLIPImageVariationPipeline
__a : Any = IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
__a : Any = IMAGE_VARIATION_BATCH_PARAMS
__a : Dict = [
"""generator""",
"""return_dict""",
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
__a : int = False
@property
def A ( self : Tuple ):
'''simple docstring'''
return 32
@property
def A ( self : Dict ):
'''simple docstring'''
return 32
@property
def A ( self : Optional[int] ):
'''simple docstring'''
return self.time_input_dim
@property
def A ( self : Optional[Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def A ( self : int ):
'''simple docstring'''
return 100
@property
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def A ( self : str ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModelWithProjection(lowercase )
@property
def A ( self : str ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(lowercase )
@property
def A ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = {
'''clip_embeddings_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''cross_attention_dim''': self.cross_attention_dim,
}
UpperCAmelCase = UnCLIPTextProjModel(**lowercase )
return model
@property
def A ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = {
'''sample_size''': 32,
# RGB in channels
'''in_channels''': 3,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 6,
'''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,
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': '''identity''',
}
UpperCAmelCase = UNetaDConditionModel(**lowercase )
return model
@property
def A ( self : str ):
'''simple docstring'''
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def A ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(1 )
UpperCAmelCase = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.dummy_decoder
UpperCAmelCase = self.dummy_text_proj
UpperCAmelCase = self.dummy_text_encoder
UpperCAmelCase = self.dummy_tokenizer
UpperCAmelCase = self.dummy_super_res_first
UpperCAmelCase = self.dummy_super_res_last
UpperCAmelCase = UnCLIPScheduler(
variance_type='''learned_range''' , prediction_type='''epsilon''' , num_train_timesteps=1_000 , )
UpperCAmelCase = UnCLIPScheduler(
variance_type='''fixed_small_log''' , prediction_type='''epsilon''' , num_train_timesteps=1_000 , )
UpperCAmelCase = CLIPImageProcessor(crop_size=32 , size=32 )
UpperCAmelCase = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def A ( self : Union[str, Any] , lowercase : str , lowercase : Any=0 , lowercase : Optional[int]=True ):
'''simple docstring'''
UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase ) ).to(lowercase )
if str(lowercase ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(lowercase )
else:
UpperCAmelCase = torch.Generator(device=lowercase ).manual_seed(lowercase )
if pil_image:
UpperCAmelCase = input_image * 0.5 + 0.5
UpperCAmelCase = input_image.clamp(0 , 1 )
UpperCAmelCase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCAmelCase = DiffusionPipeline.numpy_to_pil(lowercase )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = '''cpu'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowercase )
UpperCAmelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase , pil_image=lowercase )
UpperCAmelCase = pipe(**lowercase )
UpperCAmelCase = output.images
UpperCAmelCase = self.get_dummy_inputs(lowercase , pil_image=lowercase )
UpperCAmelCase = pipe(
**lowercase , return_dict=lowercase , )[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = '''cpu'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowercase )
UpperCAmelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase , pil_image=lowercase )
UpperCAmelCase = pipe(**lowercase )
UpperCAmelCase = output.images
UpperCAmelCase = self.get_dummy_inputs(lowercase , pil_image=lowercase )
UpperCAmelCase = pipe(
**lowercase , return_dict=lowercase , )[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = '''cpu'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowercase )
UpperCAmelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase , pil_image=lowercase )
UpperCAmelCase = [
pipeline_inputs['''image'''],
pipeline_inputs['''image'''],
]
UpperCAmelCase = pipe(**lowercase )
UpperCAmelCase = output.images
UpperCAmelCase = self.get_dummy_inputs(lowercase , pil_image=lowercase )
UpperCAmelCase = [
tuple_pipeline_inputs['''image'''],
tuple_pipeline_inputs['''image'''],
]
UpperCAmelCase = pipe(
**lowercase , return_dict=lowercase , )[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
UpperCAmelCase = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = torch.device('''cpu''' )
class _a :
__a : Optional[Any] = 1
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowercase )
UpperCAmelCase = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = torch.Generator(device=lowercase ).manual_seed(0 )
UpperCAmelCase = pipe.decoder.dtype
UpperCAmelCase = 1
UpperCAmelCase = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
UpperCAmelCase = pipe.prepare_latents(
lowercase , dtype=lowercase , device=lowercase , generator=lowercase , latents=lowercase , scheduler=DummyScheduler() )
UpperCAmelCase = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
UpperCAmelCase = pipe.prepare_latents(
lowercase , dtype=lowercase , device=lowercase , generator=lowercase , latents=lowercase , scheduler=DummyScheduler() )
UpperCAmelCase = self.get_dummy_inputs(lowercase , pil_image=lowercase )
UpperCAmelCase = pipe(
**lowercase , decoder_latents=lowercase , super_res_latents=lowercase ).images
UpperCAmelCase = self.get_dummy_inputs(lowercase , pil_image=lowercase )
# Don't pass image, instead pass embedding
UpperCAmelCase = pipeline_inputs.pop('''image''' )
UpperCAmelCase = pipe.image_encoder(lowercase ).image_embeds
UpperCAmelCase = pipe(
**lowercase , decoder_latents=lowercase , super_res_latents=lowercase , image_embeddings=lowercase , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = torch_device == '''cpu'''
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
UpperCAmelCase = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=lowercase , expected_max_diff=lowercase )
@skip_mps
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = torch_device == '''cpu'''
UpperCAmelCase = True
UpperCAmelCase = [
'''decoder_num_inference_steps''',
'''super_res_num_inference_steps''',
]
self._test_inference_batch_single_identical(
test_max_difference=lowercase , relax_max_difference=lowercase , additional_params_copy_to_batched_inputs=lowercase , )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = [
'''decoder_num_inference_steps''',
'''super_res_num_inference_steps''',
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
UpperCAmelCase = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=lowercase , additional_params_copy_to_batched_inputs=lowercase , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=lowercase )
@skip_mps
def A ( self : Any ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def A ( self : List[str] ):
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def A ( self : Tuple ):
'''simple docstring'''
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
def A ( self : List[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png''' )
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/unclip/karlo_v1_alpha_cat_variation_fp16.npy''' )
UpperCAmelCase = UnCLIPImageVariationPipeline.from_pretrained(
'''kakaobrain/karlo-v1-alpha-image-variations''' , torch_dtype=torch.floataa )
UpperCAmelCase = pipeline.to(lowercase )
pipeline.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase = pipeline(
lowercase , generator=lowercase , output_type='''np''' , )
UpperCAmelCase = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(lowercase , lowercase , 15 )
| 34
|
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A =logging.get_logger(__name__)
class _a ( __a ):
__a : str = ["""pixel_values"""]
def __init__( self : Optional[int] , lowercase : bool = True , lowercase : Optional[Dict[str, int]] = None , lowercase : PILImageResampling = PILImageResampling.BILINEAR , lowercase : bool = True , lowercase : Dict[str, int] = None , lowercase : bool = True , lowercase : Union[int, float] = 1 / 255 , lowercase : bool = True , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
super().__init__(**lowercase )
UpperCAmelCase = size if size is not None else {'''shortest_edge''': 256}
UpperCAmelCase = get_size_dict(lowercase , default_to_square=lowercase )
UpperCAmelCase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = resample
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : PILImageResampling = PILImageResampling.BICUBIC , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Any , ):
'''simple docstring'''
UpperCAmelCase = 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()}" )
UpperCAmelCase = 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 A ( self : Union[str, Any] , lowercase : np.ndarray , lowercase : Dict[str, int] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : int , ):
'''simple docstring'''
UpperCAmelCase = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(lowercase , size=(size['''height'''], size['''width''']) , data_format=lowercase , **lowercase )
def A ( self : Tuple , lowercase : np.ndarray , lowercase : float , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : List[str] ):
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[int] , lowercase : np.ndarray , lowercase : Union[float, List[float]] , lowercase : Union[float, List[float]] , lowercase : Optional[Union[str, ChannelDimension]] = None , **lowercase : Union[str, Any] , ):
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def A ( self : Optional[int] , lowercase : ImageInput , lowercase : Optional[bool] = None , lowercase : Dict[str, int] = None , lowercase : PILImageResampling = None , lowercase : bool = None , lowercase : Dict[str, int] = None , lowercase : Optional[bool] = None , lowercase : Optional[float] = None , lowercase : Optional[bool] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[float, List[float]]] = None , lowercase : Optional[Union[str, TensorType]] = None , lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **lowercase : Dict , ):
'''simple docstring'''
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(lowercase , default_to_square=lowercase )
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase = get_size_dict(lowercase , param_name='''crop_size''' )
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = 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.
UpperCAmelCase = [to_numpy_array(lowercase ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_center_crop:
UpperCAmelCase = [self.center_crop(image=lowercase , size=lowercase ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=lowercase , tensor_type=lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[Tuple] = None ):
'''simple docstring'''
UpperCAmelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowercase ) != len(lowercase ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(lowercase ):
UpperCAmelCase = target_sizes.numpy()
UpperCAmelCase = []
for idx in range(len(lowercase ) ):
UpperCAmelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=lowercase )
UpperCAmelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowercase )
else:
UpperCAmelCase = logits.argmax(dim=1 )
UpperCAmelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 34
| 1
|
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
A ='\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
A ='\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
A ='\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _a ( datasets.Metric ):
def A ( self : Optional[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , )
def A ( self : List[Any] , lowercase : List[List[List[str]]] , lowercase : List[List[str]] , lowercase : int = 1 , lowercase : int = 4 , ):
'''simple docstring'''
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=lowercase , hypotheses=lowercase , min_len=lowercase , max_len=lowercase )
}
| 34
|
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A =logging.getLogger(__name__)
def snake_case_ (_a : Dict , _a : Union[str, Any] ):
return (preds == labels).mean()
@dataclass
class _a :
__a : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class _a :
__a : str = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
__a : str = field(metadata={"""help""": """Should contain the data files for the task."""} )
__a : int = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__a : bool = field(
default=__a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def snake_case_ ():
# 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 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , 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
set_seed(training_args.seed )
try:
UpperCAmelCase = processors[data_args.task_name]()
UpperCAmelCase = processor.get_labels()
UpperCAmelCase = len(_a )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_a , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoModelForMultipleChoice.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 , )
# Get datasets
UpperCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
UpperCAmelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_a : EvalPrediction ) -> Dict:
UpperCAmelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(_a , p.label_ids )}
# Data collator
UpperCAmelCase = DataCollatorWithPadding(_a , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
UpperCAmelCase = Trainer(
model=_a , args=_a , train_dataset=_a , eval_dataset=_a , compute_metrics=_a , data_collator=_a , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCAmelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase = trainer.evaluate()
UpperCAmelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(_a , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , _a , _a )
writer.write('''%s = %s\n''' % (key, value) )
results.update(_a )
return results
def snake_case_ (_a : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 34
| 1
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _a ( unittest.TestCase ):
@property
def A ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.dummy_uncond_unet
UpperCAmelCase = KarrasVeScheduler()
UpperCAmelCase = KarrasVePipeline(unet=lowercase , scheduler=lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(num_inference_steps=2 , generator=lowercase , output_type='''numpy''' ).images
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(num_inference_steps=2 , generator=lowercase , output_type='''numpy''' , return_dict=lowercase )[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class _a ( unittest.TestCase ):
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = '''google/ncsnpp-celebahq-256'''
UpperCAmelCase = UNetaDModel.from_pretrained(lowercase )
UpperCAmelCase = KarrasVeScheduler()
UpperCAmelCase = KarrasVePipeline(unet=lowercase , scheduler=lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(num_inference_steps=20 , generator=lowercase , output_type='''numpy''' ).images
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 34
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImageProcessor"""
__a : List[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Dict , lowercase : Union[str, Any]=None , lowercase : Dict=None , **lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowercase , )
UpperCAmelCase = kwargs.pop('''feature_extractor''' )
UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowercase , lowercase )
UpperCAmelCase = self.image_processor
def __call__( self : Tuple , lowercase : Optional[Any]=None , lowercase : Union[str, Any]=None , lowercase : int=None , **lowercase : Dict ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCAmelCase = self.tokenizer(lowercase , return_tensors=lowercase , **lowercase )
if images is not None:
UpperCAmelCase = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def A ( self : int , *lowercase : Tuple , **lowercase : List[str] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A ( self : Optional[Any] , *lowercase : int , **lowercase : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer.model_input_names
UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A ( self : List[Any] ):
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowercase , )
return self.image_processor_class
| 34
| 1
|
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
A =soup.find('meta', {'property': 'og:image'})['content']
A =requests.get(image_url).content
A =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(f"""Done. Image saved to disk as {file_name}.""")
| 34
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A =logging.get_logger(__name__)
A ={
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class _a ( __a ):
__a : List[Any] = """marian"""
__a : Union[str, Any] = ["""past_key_values"""]
__a : List[str] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[Any] , lowercase : Union[str, Any]=58_101 , lowercase : Tuple=None , lowercase : str=1_024 , lowercase : Optional[int]=12 , lowercase : Optional[int]=4_096 , lowercase : int=16 , lowercase : List[Any]=12 , lowercase : int=4_096 , lowercase : Optional[int]=16 , lowercase : int=0.0 , lowercase : Tuple=0.0 , lowercase : Tuple=True , lowercase : Union[str, Any]=True , lowercase : List[Any]="gelu" , lowercase : Tuple=1_024 , lowercase : str=0.1 , lowercase : str=0.0 , lowercase : Optional[int]=0.0 , lowercase : Dict=0.02 , lowercase : Union[str, Any]=58_100 , lowercase : List[str]=False , lowercase : str=58_100 , lowercase : Any=0 , lowercase : Optional[Any]=0 , lowercase : Tuple=True , **lowercase : Optional[int] , ):
'''simple docstring'''
UpperCAmelCase = vocab_size
UpperCAmelCase = decoder_vocab_size or vocab_size
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = d_model
UpperCAmelCase = encoder_ffn_dim
UpperCAmelCase = encoder_layers
UpperCAmelCase = encoder_attention_heads
UpperCAmelCase = decoder_ffn_dim
UpperCAmelCase = decoder_layers
UpperCAmelCase = decoder_attention_heads
UpperCAmelCase = dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation_dropout
UpperCAmelCase = activation_function
UpperCAmelCase = init_std
UpperCAmelCase = encoder_layerdrop
UpperCAmelCase = decoder_layerdrop
UpperCAmelCase = use_cache
UpperCAmelCase = encoder_layers
UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , forced_eos_token_id=lowercase , **lowercase , )
class _a ( __a ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A ( self : int ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase = {0: '''batch'''}
UpperCAmelCase = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
UpperCAmelCase = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
UpperCAmelCase = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A ( self : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super().outputs
else:
UpperCAmelCase = super(lowercase , self ).outputs
if self.use_past:
UpperCAmelCase , UpperCAmelCase = self.num_layers
for i in range(lowercase ):
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
UpperCAmelCase = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A ( self : Dict , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Generate decoder inputs
UpperCAmelCase = seq_length if not self.use_past else 1
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
UpperCAmelCase = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
UpperCAmelCase = dict(**lowercase , **lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
UpperCAmelCase = common_inputs['''decoder_input_ids'''].shape[1]
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = decoder_seq_length + 3
UpperCAmelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
UpperCAmelCase = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase , lowercase )] , dim=1 )
UpperCAmelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase = min(lowercase , lowercase )
UpperCAmelCase = max(lowercase , lowercase ) - min_num_layers
UpperCAmelCase = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
torch.zeros(lowercase ),
) )
# TODO: test this.
UpperCAmelCase = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase , lowercase ):
common_inputs["past_key_values"].append((torch.zeros(lowercase ), torch.zeros(lowercase )) )
return common_inputs
def A ( self : int , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
UpperCAmelCase , UpperCAmelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase = seqlen + 2
UpperCAmelCase , UpperCAmelCase = self.num_layers
UpperCAmelCase , UpperCAmelCase = self.num_attention_heads
UpperCAmelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCAmelCase = common_inputs['''attention_mask'''].dtype
UpperCAmelCase = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase , lowercase , dtype=lowercase )] , dim=1 )
UpperCAmelCase = [
(torch.zeros(lowercase ), torch.zeros(lowercase )) for _ in range(lowercase )
]
return common_inputs
def A ( self : str , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase = tokenizer.num_special_tokens_to_add(lowercase )
UpperCAmelCase = compute_effective_axis_dimension(
lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
UpperCAmelCase = dict(tokenizer(lowercase , return_tensors=lowercase ) )
return common_inputs
def A ( self : List[str] , lowercase : PreTrainedTokenizer , lowercase : int = -1 , lowercase : int = -1 , lowercase : bool = False , lowercase : Optional[TensorType] = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
else:
UpperCAmelCase = self._generate_dummy_inputs_for_causal_lm(
lowercase , batch_size=lowercase , seq_length=lowercase , is_pair=lowercase , framework=lowercase )
return common_inputs
def A ( self : List[Any] , lowercase : Any , lowercase : Tuple , lowercase : Any , lowercase : Any ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCAmelCase = super()._flatten_past_key_values_(lowercase , lowercase , lowercase , lowercase )
else:
UpperCAmelCase = super(lowercase , self )._flatten_past_key_values_(
lowercase , lowercase , lowercase , lowercase )
@property
def A ( self : Any ):
'''simple docstring'''
return 1E-4
| 34
| 1
|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _a ( unittest.TestCase ):
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = [[1, 2, 4], [1, 2, 3, 4]]
UpperCAmelCase = DisjunctiveConstraint(lowercase )
self.assertTrue(isinstance(dc.token_ids , lowercase ) )
with self.assertRaises(lowercase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(lowercase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(lowercase ):
DisjunctiveConstraint(lowercase ) # fails here
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = [[1, 2, 3], [1, 2, 4]]
UpperCAmelCase = DisjunctiveConstraint(lowercase )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(1 )
UpperCAmelCase = stepped is True and completed is False and reset is False
self.assertTrue(lowercase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(2 )
UpperCAmelCase = stepped is True and completed is False and reset is False
self.assertTrue(lowercase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(3 )
UpperCAmelCase = stepped is True and completed is True and reset is False
self.assertTrue(lowercase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
UpperCAmelCase = DisjunctiveConstraint(lowercase )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 34
|
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
print(solution())
| 34
| 1
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class _a :
__a : Optional[Any] = MBartConfig
__a : List[str] = {}
__a : str = """gelu"""
def __init__( self : List[str] , lowercase : Optional[Any] , lowercase : Any=13 , lowercase : Any=7 , lowercase : str=True , lowercase : Union[str, Any]=False , lowercase : str=99 , lowercase : Union[str, Any]=32 , lowercase : int=2 , lowercase : List[str]=4 , lowercase : List[str]=37 , lowercase : Dict=0.1 , lowercase : int=0.1 , lowercase : Dict=20 , lowercase : List[str]=2 , lowercase : Optional[Any]=1 , lowercase : str=0 , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = eos_token_id
UpperCAmelCase = pad_token_id
UpperCAmelCase = bos_token_id
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase = prepare_mbart_inputs_dict(lowercase , lowercase , lowercase )
return config, inputs_dict
def A ( self : Any , lowercase : List[str] , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = TFMBartModel(config=lowercase ).get_decoder()
UpperCAmelCase = inputs_dict['''input_ids''']
UpperCAmelCase = input_ids[:1, :]
UpperCAmelCase = inputs_dict['''attention_mask'''][:1, :]
UpperCAmelCase = inputs_dict['''head_mask''']
UpperCAmelCase = 1
# first forward pass
UpperCAmelCase = model(lowercase , attention_mask=lowercase , head_mask=lowercase , use_cache=lowercase )
UpperCAmelCase , UpperCAmelCase = outputs.to_tuple()
UpperCAmelCase = past_key_values[1]
def snake_case_ (_a : Tuple , _a : Union[str, Any] , _a : Optional[Any] , _a : List[str]=None , _a : int=None , _a : Any=None , _a : List[str]=None , _a : Union[str, Any]=None , ):
if attention_mask is None:
UpperCAmelCase = tf.cast(tf.math.not_equal(_a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _a ( __a , __a , unittest.TestCase ):
__a : str = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
__a : List[str] = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
__a : Optional[int] = (
{
"""conversational""": TFMBartForConditionalGeneration,
"""feature-extraction""": TFMBartModel,
"""summarization""": TFMBartForConditionalGeneration,
"""text2text-generation""": TFMBartForConditionalGeneration,
"""translation""": TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
__a : Tuple = True
__a : List[Any] = False
__a : Tuple = False
def A ( self : Tuple , lowercase : List[Any] , lowercase : List[str] , lowercase : Tuple , lowercase : List[Any] , lowercase : List[Any] ):
'''simple docstring'''
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = TFMBartModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase )
@require_sentencepiece
@require_tokenizers
@require_tf
class _a ( unittest.TestCase ):
__a : int = [
""" UN Chief Says There Is No Military Solution in Syria""",
]
__a : Optional[int] = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
]
__a : str = """facebook/mbart-large-en-ro"""
@cached_property
def A ( self : Optional[Any] ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def A ( self : str , **lowercase : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.translate_src_text(**lowercase )
self.assertListEqual(self.expected_text , lowercase )
def A ( self : List[Any] , **lowercase : int ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer(self.src_text , **lowercase , return_tensors='''tf''' )
UpperCAmelCase = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase = self.tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
return generated_words
@slow
def A ( self : Optional[Any] ):
'''simple docstring'''
self._assert_generated_batch_equal_expected()
| 34
|
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
A =logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
A ={
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'summarization': AutoModelForSeqaSeqLM,
'translation': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
A ={
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
A =sorted(arg_to_scheduler.keys())
A ='{' + ', '.join(arg_to_scheduler_choices) + '}'
class _a ( pl.LightningModule ):
def __init__( self : List[str] , lowercase : argparse.Namespace , lowercase : List[Any]=None , lowercase : Dict="base" , lowercase : Optional[int]=None , lowercase : Dict=None , lowercase : Tuple=None , **lowercase : Optional[int] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowercase )
UpperCAmelCase = 0
UpperCAmelCase = Path(self.hparams.output_dir )
UpperCAmelCase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
UpperCAmelCase = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'''num_labels''': num_labels} if num_labels is not None else {}) , cache_dir=lowercase , **lowercase , )
else:
UpperCAmelCase = config
UpperCAmelCase = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , lowercase , lowercase ):
assert hasattr(self.config , lowercase ), f"model config doesn't have a `{p}` attribute"
setattr(self.config , lowercase , getattr(self.hparams , lowercase ) )
if tokenizer is None:
UpperCAmelCase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowercase , )
else:
UpperCAmelCase = tokenizer
UpperCAmelCase = MODEL_MODES[mode]
if model is None:
UpperCAmelCase = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=lowercase , )
else:
UpperCAmelCase = model
def A ( self : List[Any] , *lowercase : List[str] , **lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.model_type.from_pretrained(*lowercase , **lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.hparams.lr_scheduler]
UpperCAmelCase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
UpperCAmelCase = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''params''': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'''weight_decay''': self.hparams.weight_decay,
},
{
'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
if self.hparams.adafactor:
UpperCAmelCase = Adafactor(
lowercase , lr=self.hparams.learning_rate , scale_parameter=lowercase , relative_step=lowercase )
else:
UpperCAmelCase = AdamW(
lowercase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
UpperCAmelCase = optimizer
UpperCAmelCase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A ( self : List[Any] , lowercase : int , lowercase : List[str] ):
'''simple docstring'''
return self.validation_step(lowercase , lowercase )
def A ( self : List[Any] , lowercase : Tuple ):
'''simple docstring'''
return self.validation_end(lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
UpperCAmelCase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A ( self : List[str] , lowercase : Any ):
'''simple docstring'''
if stage == "test":
UpperCAmelCase = len(self.test_dataloader().dataset )
else:
UpperCAmelCase = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=lowercase )
UpperCAmelCase = len(self.train_dataloader().dataset )
def A ( self : List[str] , lowercase : str , lowercase : int , lowercase : bool = False ):
'''simple docstring'''
raise NotImplementedError('''You must implement this for your task''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
return self.train_loader
def A ( self : Optional[Any] ):
'''simple docstring'''
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
lowercase , list(filter(lowercase , self.hparams.model_name_or_path.split('''/''' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A ( self : List[str] , lowercase : Dict[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.output_dir.joinpath('''best_tfmr''' )
UpperCAmelCase = self.step_count
self.model.save_pretrained(lowercase )
self.tokenizer.save_pretrained(lowercase )
@staticmethod
def A ( lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
parser.add_argument(
'''--model_name_or_path''' , default=lowercase , type=lowercase , required=lowercase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=lowercase , help='''Pretrained config name or path if not the same as model_name''' )
parser.add_argument(
'''--tokenizer_name''' , default=lowercase , type=lowercase , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(lowercase ).parent / '''test_run''' / '''cache''' ) , type=lowercase , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=lowercase , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=lowercase , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=lowercase , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=lowercase , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5E-5 , type=lowercase , help='''The initial learning rate for Adam.''' )
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=lowercase , metavar=lowercase , type=lowercase , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=lowercase , help='''Weight decay if we apply some.''' )
parser.add_argument('''--adam_epsilon''' , default=1E-8 , type=lowercase , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--warmup_steps''' , default=0 , type=lowercase , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--num_workers''' , default=4 , type=lowercase , help='''kwarg passed to DataLoader''' )
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=lowercase )
parser.add_argument('''--train_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--eval_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--adafactor''' , action='''store_true''' )
class _a ( pl.Callback ):
def A ( self : Dict , lowercase : Optional[Any] , lowercase : List[Any] ):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Union[str, Any] , lowercase : Any ):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowercase )
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Optional[int] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = trainer.lr_schedulers[0]['''scheduler''']
UpperCAmelCase = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowercase )
def A ( self : Tuple , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Validation results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log results
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def A ( self : Dict , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Test results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log and save results to file
UpperCAmelCase = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''' )
with open(lowercase , '''w''' ) as writer:
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
writer.write('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def snake_case_ (_a : int , _a : Optional[Any] ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'''--output_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_a , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument(
'''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , )
parser.add_argument(
'''--fp16_opt_level''' , type=_a , default='''O2''' , help=(
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
'''See details at https://nvidia.github.io/apex/amp.html'''
) , )
parser.add_argument('''--n_tpu_cores''' , dest='''tpu_cores''' , type=_a )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_a , help='''Max gradient norm''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_predict''' , action='''store_true''' , help='''Whether to run predictions on the test set.''' )
parser.add_argument(
'''--gradient_accumulation_steps''' , dest='''accumulate_grad_batches''' , type=_a , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_a , default=4_2 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_a , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def snake_case_ (_a : BaseTransformer , _a : argparse.Namespace , _a : List[Any]=None , _a : Tuple=True , _a : int=[] , _a : Any=None , _a : int=None , **_a : Optional[Any] , ):
pl.seed_everything(args.seed )
# init model
UpperCAmelCase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_a )
# add custom checkpoints
if checkpoint_callback is None:
UpperCAmelCase = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='''checkpoint''' , monitor='''val_loss''' , mode='''min''' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_a )
if logging_callback is None:
UpperCAmelCase = LoggingCallback()
UpperCAmelCase = {}
if args.fpaa:
UpperCAmelCase = 1_6
if args.gpus > 1:
UpperCAmelCase = '''auto'''
UpperCAmelCase = '''ddp'''
UpperCAmelCase = args.accumulate_grad_batches
UpperCAmelCase = None
UpperCAmelCase = '''auto'''
UpperCAmelCase = pl.Trainer.from_argparse_args(
_a , weights_summary=_a , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_a , val_check_interval=1 , num_sanity_val_steps=2 , **_a , )
if args.do_train:
trainer.fit(_a )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 34
| 1
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : float , _a : float , _a : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative in a semiconductor''' )
elif hole_conc < 0:
raise ValueError('''Hole concentration cannot be negative in a semiconductor''' )
elif intrinsic_conc < 0:
raise ValueError(
'''Intrinsic concentration cannot be negative in a semiconductor''' )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
|
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : dict , _a : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
UpperCAmelCase = cst_fwd.get(_a , np.inf )
UpperCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
UpperCAmelCase = new_cost_f
UpperCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
UpperCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def snake_case_ (_a : str , _a : str , _a : dict , _a : dict ):
UpperCAmelCase = -1
UpperCAmelCase = set()
UpperCAmelCase = set()
UpperCAmelCase = {source: 0}
UpperCAmelCase = {destination: 0}
UpperCAmelCase = {source: None}
UpperCAmelCase = {destination: None}
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
UpperCAmelCase , UpperCAmelCase = queue_forward.get()
visited_forward.add(_a )
UpperCAmelCase , UpperCAmelCase = queue_backward.get()
visited_backward.add(_a )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
UpperCAmelCase = shortest_distance
return shortest_path_distance
A ={
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
A ={
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : str ):
UpperCAmelCase = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case_ (_a : str ):
UpperCAmelCase = [chr(i + 6_5 ) for i in range(2_6 )]
# Remove duplicate characters from key
UpperCAmelCase = remove_duplicates(key.upper() )
UpperCAmelCase = len(_a )
# First fill cipher with key characters
UpperCAmelCase = {alphabet[i]: char for i, char in enumerate(_a )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_a ) , 2_6 ):
UpperCAmelCase = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
UpperCAmelCase = alphabet[i - offset]
UpperCAmelCase = char
return cipher_alphabet
def snake_case_ (_a : str , _a : dict[str, str] ):
return "".join(cipher_map.get(_a , _a ) for ch in message.upper() )
def snake_case_ (_a : str , _a : dict[str, str] ):
UpperCAmelCase = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_a , _a ) for ch in message.upper() )
def snake_case_ ():
UpperCAmelCase = input('''Enter message to encode or decode: ''' ).strip()
UpperCAmelCase = input('''Enter keyword: ''' ).strip()
UpperCAmelCase = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
UpperCAmelCase = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
UpperCAmelCase = create_cipher_map(_a )
print(func(_a , _a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 34
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
def snake_case_ (_a : List[str] ):
UpperCAmelCase = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
UpperCAmelCase = 1_2_8
elif "12-12" in model_name:
UpperCAmelCase = 1_2
UpperCAmelCase = 1_2
elif "14-14" in model_name:
UpperCAmelCase = 1_4
UpperCAmelCase = 1_4
elif "16-16" in model_name:
UpperCAmelCase = 1_6
UpperCAmelCase = 1_6
else:
raise ValueError('''Model not supported''' )
UpperCAmelCase = '''huggingface/label-files'''
if "speech-commands" in model_name:
UpperCAmelCase = 3_5
UpperCAmelCase = '''speech-commands-v2-id2label.json'''
else:
UpperCAmelCase = 5_2_7
UpperCAmelCase = '''audioset-id2label.json'''
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ (_a : Tuple ):
if "module.v" in name:
UpperCAmelCase = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
UpperCAmelCase = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
UpperCAmelCase = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
UpperCAmelCase = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
UpperCAmelCase = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
UpperCAmelCase = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
UpperCAmelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
UpperCAmelCase = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
UpperCAmelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
UpperCAmelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
UpperCAmelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
UpperCAmelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
UpperCAmelCase = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
UpperCAmelCase = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
UpperCAmelCase = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def snake_case_ (_a : Dict , _a : List[Any] ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(_a )
if "qkv" in key:
UpperCAmelCase = key.split('''.''' )
UpperCAmelCase = int(key_split[3] )
UpperCAmelCase = config.hidden_size
if "weight" in key:
UpperCAmelCase = val[:dim, :]
UpperCAmelCase = val[dim : dim * 2, :]
UpperCAmelCase = val[-dim:, :]
else:
UpperCAmelCase = val[:dim]
UpperCAmelCase = val[dim : dim * 2]
UpperCAmelCase = val[-dim:]
else:
UpperCAmelCase = val
return orig_state_dict
def snake_case_ (_a : Tuple ):
UpperCAmelCase = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(_a , _a )
@torch.no_grad()
def snake_case_ (_a : int , _a : Union[str, Any] , _a : Dict=False ):
UpperCAmelCase = get_audio_spectrogram_transformer_config(_a )
UpperCAmelCase = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
UpperCAmelCase = model_name_to_url[model_name]
UpperCAmelCase = torch.hub.load_state_dict_from_url(_a , map_location='''cpu''' )
# remove some keys
remove_keys(_a )
# rename some keys
UpperCAmelCase = convert_state_dict(_a , _a )
# load 🤗 model
UpperCAmelCase = ASTForAudioClassification(_a )
model.eval()
model.load_state_dict(_a )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
UpperCAmelCase = -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
UpperCAmelCase = 4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
UpperCAmelCase = 1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
UpperCAmelCase = ASTFeatureExtractor(mean=_a , std=_a , max_length=_a )
if "speech-commands" in model_name:
UpperCAmelCase = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
UpperCAmelCase = dataset[0]['''audio''']['''array''']
else:
UpperCAmelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
UpperCAmelCase , UpperCAmelCase = torchaudio.load(_a )
UpperCAmelCase = waveform.squeeze().numpy()
UpperCAmelCase = feature_extractor(_a , sampling_rate=1_6_0_0_0 , return_tensors='''pt''' )
# forward pass
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
UpperCAmelCase = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
UpperCAmelCase = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
UpperCAmelCase = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
UpperCAmelCase = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
UpperCAmelCase = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
UpperCAmelCase = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
UpperCAmelCase = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
UpperCAmelCase = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , _a , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(F"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_a )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(F"MIT/{model_name}" )
feature_extractor.push_to_hub(F"MIT/{model_name}" )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub.'
)
A =parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A ={
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConfig',
'Pix2StructTextConfig',
'Pix2StructVisionConfig',
],
'processing_pix2struct': ['Pix2StructProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['Pix2StructImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Pix2StructPreTrainedModel',
'Pix2StructForConditionalGeneration',
'Pix2StructVisionModel',
'Pix2StructTextModel',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
UpperCAmelCase = m
else:
UpperCAmelCase = m # noqa: E741
return r
def snake_case_ (_a : list[int] ):
if len(_a ) == 0:
return 0
UpperCAmelCase = [0] * len(_a )
UpperCAmelCase = 1
UpperCAmelCase = v[0]
for i in range(1 , len(_a ) ):
if v[i] < tail[0]:
UpperCAmelCase = v[i]
elif v[i] > tail[length - 1]:
UpperCAmelCase = v[i]
length += 1
else:
UpperCAmelCase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
| 1
|
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class _a :
def __init__( self : str , lowercase : List[Any] , lowercase : Dict=2 , lowercase : str=32 , lowercase : Optional[Any]=16 , lowercase : Optional[Any]=3 , lowercase : Union[str, Any]=True , lowercase : List[Any]=True , lowercase : Optional[int]=32 , lowercase : Any=4 , lowercase : str=[0, 1, 2, 3] , lowercase : List[Any]=4 , lowercase : str=37 , lowercase : Optional[Any]="gelu" , lowercase : Tuple=0.1 , lowercase : Tuple=0.1 , lowercase : Union[str, Any]=0.02 , lowercase : int=3 , lowercase : int=[1, 384, 24, 24] , lowercase : str=True , lowercase : List[Any]=None , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = patch_size
UpperCAmelCase = num_channels
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = backbone_out_indices
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = backbone_featmap_shape
UpperCAmelCase = scope
UpperCAmelCase = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase = (image_size // patch_size) ** 2
UpperCAmelCase = num_patches + 1
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [96, 192, 384, 768],
'''num_groups''': 2,
}
return DPTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=lowercase , backbone_featmap_shape=self.backbone_featmap_shape , )
def A ( self : Optional[int] , lowercase : str , lowercase : Optional[Any] , lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = DPTModel(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : List[str] , lowercase : Tuple , lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = DPTForDepthEstimation(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def A ( self : int , lowercase : Dict , lowercase : Union[str, Any] , lowercase : int ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = DPTForSemanticSegmentation(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , labels=lowercase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _a ( __a , __a , unittest.TestCase ):
__a : Dict = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
__a : Optional[int] = (
{
"""depth-estimation""": DPTForDepthEstimation,
"""feature-extraction""": DPTModel,
"""image-segmentation""": DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__a : Any = False
__a : List[Any] = False
__a : Dict = False
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = DPTModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def A ( self : int ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''DPT does not use inputs_embeds''' )
def A ( self : List[str] ):
'''simple docstring'''
pass
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase , nn.Linear ) )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*lowercase )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowercase )
def A ( self : List[str] ):
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = True
if model_class in get_values(lowercase ):
continue
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
model.train()
UpperCAmelCase = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase )
UpperCAmelCase = model(**lowercase ).loss
loss.backward()
def A ( self : str ):
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = False
UpperCAmelCase = True
if model_class in get_values(lowercase ) or not model_class.supports_gradient_checkpointing:
continue
UpperCAmelCase = model_class(lowercase )
model.to(lowercase )
model.gradient_checkpointing_enable()
model.train()
UpperCAmelCase = self._prepare_for_class(lowercase , lowercase , return_labels=lowercase )
UpperCAmelCase = model(**lowercase ).loss
loss.backward()
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = _config_zero_init(lowercase )
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(config=lowercase )
# Skip the check for the backbone
UpperCAmelCase = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
UpperCAmelCase = [f"{name}.{key}" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"Parameter {name} of model {model_class} seems not properly initialized" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def A ( self : int ):
'''simple docstring'''
pass
@slow
def A ( self : Any ):
'''simple docstring'''
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
UpperCAmelCase = DPTModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = '''add'''
with self.assertRaises(lowercase ):
UpperCAmelCase = DPTForDepthEstimation(lowercase )
def snake_case_ ():
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
@slow
class _a ( unittest.TestCase ):
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''' )
UpperCAmelCase = DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''' ).to(lowercase )
UpperCAmelCase = prepare_img()
UpperCAmelCase = image_processor(images=lowercase , return_tensors='''pt''' ).to(lowercase )
# forward pass
with torch.no_grad():
UpperCAmelCase = model(**lowercase )
UpperCAmelCase = outputs.predicted_depth
# verify the predicted depth
UpperCAmelCase = torch.Size((1, 384, 384) )
self.assertEqual(predicted_depth.shape , lowercase )
UpperCAmelCase = torch.tensor(
[[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]] ).to(lowercase )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 , lowercase , atol=1E-4 ) )
| 34
|
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase = [[0 for i in range(_a )] for j in range(_a )]
# since string of zero length match pattern of zero length
UpperCAmelCase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _a ):
UpperCAmelCase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _a ):
UpperCAmelCase = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _a ):
for j in range(1 , _a ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase = dp[i - 1][j]
else:
UpperCAmelCase = 0
else:
UpperCAmelCase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A ='aab'
A ='c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"""{input_string} matches the given pattern {pattern}""")
else:
print(f"""{input_string} does not match with the given pattern {pattern}""")
| 34
| 1
|
'''simple docstring'''
A ={
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o': 'ABBAB',
'p': 'ABBBA',
'q': 'ABBBB',
'r': 'BAAAA',
's': 'BAAAB',
't': 'BAABA',
'u': 'BAABB',
'v': 'BBBAB',
'w': 'BABAA',
'x': 'BABAB',
'y': 'BABBA',
'z': 'BABBB',
' ': ' ',
}
A ={value: key for key, value in encode_dict.items()}
def snake_case_ (_a : str ):
UpperCAmelCase = ''''''
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception('''encode() accepts only letters of the alphabet and spaces''' )
return encoded
def snake_case_ (_a : str ):
if set(_a ) - {"A", "B", " "} != set():
raise Exception('''decode() accepts only \'A\', \'B\' and spaces''' )
UpperCAmelCase = ''''''
for word in coded.split():
while len(_a ) != 0:
decoded += decode_dict[word[:5]]
UpperCAmelCase = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 34
|
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A ='pt'
elif is_tf_available():
A ='tf'
else:
A ='jax'
class _a ( __a , unittest.TestCase ):
__a : Optional[Any] = PerceiverTokenizer
__a : str = False
def A ( self : Union[str, Any] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A ( self : Optional[int] ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def A ( self : Union[str, Any] , **lowercase : int ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase )
def A ( self : Tuple , lowercase : str , lowercase : List[str]=False , lowercase : Union[str, Any]=20 , lowercase : Union[str, Any]=5 ):
'''simple docstring'''
UpperCAmelCase = []
for i in range(len(lowercase ) ):
try:
UpperCAmelCase = tokenizer.decode([i] , clean_up_tokenization_spaces=lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
UpperCAmelCase = list(filter(lambda lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , lowercase ) )
UpperCAmelCase = list(filter(lambda lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=lowercase ) , lowercase ) )
if max_length is not None and len(lowercase ) > max_length:
UpperCAmelCase = toks[:max_length]
if min_length is not None and len(lowercase ) < min_length and len(lowercase ) > 0:
while len(lowercase ) < min_length:
UpperCAmelCase = toks + toks
# toks_str = [t[1] for t in toks]
UpperCAmelCase = [t[0] for t in toks]
# Ensure consistency
UpperCAmelCase = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
if " " not in output_txt and len(lowercase ) > 1:
UpperCAmelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=lowercase )
)
if with_prefix_space:
UpperCAmelCase = ''' ''' + output_txt
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
return output_txt, output_ids
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = '''Unicode €.'''
UpperCAmelCase = tokenizer(lowercase )
UpperCAmelCase = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]Unicode €.[SEP]''' )
UpperCAmelCase = tokenizer('''e è é ê ë''' )
UpperCAmelCase = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded['''input_ids'''] , lowercase )
# decoding
UpperCAmelCase = tokenizer.decode(lowercase )
self.assertEqual(lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
UpperCAmelCase = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
self.assertIsInstance(lowercase , lowercase )
if FRAMEWORK != "jax":
UpperCAmelCase = list(batch.input_ids.numpy()[0] )
else:
UpperCAmelCase = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowercase , lowercase )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
UpperCAmelCase = tokenizer(lowercase , padding=lowercase , return_tensors=lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , lowercase )
self.assertIn('''attention_mask''' , lowercase )
self.assertNotIn('''decoder_input_ids''' , lowercase )
self.assertNotIn('''decoder_attention_mask''' , lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
UpperCAmelCase = [
'''Summary of the text.''',
'''Another summary.''',
]
UpperCAmelCase = tokenizer(
text_target=lowercase , max_length=32 , padding='''max_length''' , truncation=lowercase , return_tensors=lowercase )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
shutil.rmtree(lowercase )
UpperCAmelCase = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
UpperCAmelCase = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
UpperCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
tokenizer.save_pretrained(lowercase )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase )
UpperCAmelCase = after_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
UpperCAmelCase = tokenizer.__class__.from_pretrained(lowercase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(lowercase )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowercase )
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
UpperCAmelCase = json.load(lowercase )
UpperCAmelCase = [f"<extra_id_{i}>" for i in range(125 )]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
UpperCAmelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
with open(os.path.join(lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(lowercase , lowercase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['''an_additional_special_token'''] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
UpperCAmelCase = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=lowercase )]
UpperCAmelCase = tokenizer_class.from_pretrained(
lowercase , additional_special_tokens=lowercase , )
self.assertIn('''a_new_additional_special_token''' , tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ) , )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ) , '''�''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
pass
def A ( self : Any ):
'''simple docstring'''
pass
def A ( self : Dict ):
'''simple docstring'''
pass
def A ( self : str ):
'''simple docstring'''
pass
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.get_tokenizers(fast=lowercase , do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
UpperCAmelCase = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
UpperCAmelCase = tokenizer.convert_tokens_to_string(lowercase )
self.assertIsInstance(lowercase , lowercase )
| 34
| 1
|
'''simple docstring'''
import math
def snake_case_ (_a : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_a ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case_ (_a : float = 0.1 ):
UpperCAmelCase = 3
UpperCAmelCase = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_a )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34
|
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return val
return default
def snake_case_ (_a : Dict , _a : Any=False ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return strtobool(_a ) == 1 # As its name indicates `strtobool` actually returns an int...
def snake_case_ (_a : str , _a : Optional[Any]="no" ):
UpperCAmelCase = os.environ.get(_a , str(_a ) )
return value
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A ={
'configuration_xlm_roberta': [
'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaConfig',
'XLMRobertaOnnxConfig',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['XLMRobertaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['XLMRobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaForCausalLM',
'XLMRobertaForMaskedLM',
'XLMRobertaForMultipleChoice',
'XLMRobertaForQuestionAnswering',
'XLMRobertaForSequenceClassification',
'XLMRobertaForTokenClassification',
'XLMRobertaModel',
'XLMRobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMRobertaForCausalLM',
'TFXLMRobertaForMaskedLM',
'TFXLMRobertaForMultipleChoice',
'TFXLMRobertaForQuestionAnswering',
'TFXLMRobertaForSequenceClassification',
'TFXLMRobertaForTokenClassification',
'TFXLMRobertaModel',
'TFXLMRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'FlaxXLMRobertaForMaskedLM',
'FlaxXLMRobertaForCausalLM',
'FlaxXLMRobertaForMultipleChoice',
'FlaxXLMRobertaForQuestionAnswering',
'FlaxXLMRobertaForSequenceClassification',
'FlaxXLMRobertaForTokenClassification',
'FlaxXLMRobertaModel',
'FlaxXLMRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A =logging.getLogger(__name__)
A ='Hello world! cécé herlolip'
A =namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def snake_case_ (_a : List[Any] , _a : Any ):
UpperCAmelCase = BertAbsConfig(
temp_dir='''.''' , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder='''bert''' , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
UpperCAmelCase = torch.load(_a , lambda _a , _a : storage )
UpperCAmelCase = AbsSummarizer(_a , torch.device('''cpu''' ) , _a )
original.eval()
UpperCAmelCase = BertAbsSummarizer(_a , torch.device('''cpu''' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('''convert the model''' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('''Make sure that the models\' outputs are identical''' )
UpperCAmelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
# prepare the model inputs
UpperCAmelCase = tokenizer.encode('''This is sample éàalj\'-.''' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
UpperCAmelCase = tokenizer.encode('''This is sample 3 éàalj\'-.''' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
UpperCAmelCase = encoder_input_ids
UpperCAmelCase = decoder_input_ids
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
UpperCAmelCase = original(_a , _a , _a , _a , _a , _a , _a )[0]
UpperCAmelCase = original.generator(_a )
UpperCAmelCase = new_model(
_a , _a , _a , _a , _a )[0]
UpperCAmelCase = new_model.generator(_a )
UpperCAmelCase = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.allclose(_a , _a , atol=1E-3 )
if are_identical:
logging.info('''all weights are equal up to 1e-3''' )
else:
raise ValueError('''the weights are different. The new model is likely different from the original one.''' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('''saving the model\'s state dictionary''' )
torch.save(
new_model.state_dict() , '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''' )
if __name__ == "__main__":
A =argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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.',
)
A =parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 34
| 1
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
A =logging.get_logger(__name__)
A ={
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _a ( __a ):
def __init__( self : List[Any] , lowercase : int=None , lowercase : Any=None , *lowercase : int , **lowercase : str ):
'''simple docstring'''
super().__init__(*lowercase , **lowercase )
if config is None:
assert isinstance(self.model , lowercase ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f" {self.model.__class__}"
)
UpperCAmelCase = self.model.config
else:
UpperCAmelCase = config
UpperCAmelCase = data_args
UpperCAmelCase = self.config.tgt_vocab_size if isinstance(self.config , lowercase ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
''' padding..''' )
if self.args.label_smoothing == 0:
UpperCAmelCase = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
UpperCAmelCase = label_smoothed_nll_loss
def A ( self : Optional[Any] , lowercase : int ):
'''simple docstring'''
if self.optimizer is None:
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
UpperCAmelCase = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
UpperCAmelCase = Adafactor
UpperCAmelCase = {'''scale_parameter''': False, '''relative_step''': False}
else:
UpperCAmelCase = AdamW
UpperCAmelCase = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
UpperCAmelCase = self.args.learning_rate
if self.sharded_ddp:
UpperCAmelCase = OSS(
params=lowercase , optim=lowercase , **lowercase , )
else:
UpperCAmelCase = optimizer_cls(lowercase , **lowercase )
if self.lr_scheduler is None:
UpperCAmelCase = self._get_lr_scheduler(lowercase )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def A ( self : Optional[Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
UpperCAmelCase = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
UpperCAmelCase = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
UpperCAmelCase = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=lowercase )
return scheduler
def A ( self : Tuple ):
'''simple docstring'''
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def A ( self : int , lowercase : str , lowercase : Any , lowercase : Any ):
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
UpperCAmelCase = model(**lowercase , use_cache=lowercase )[0]
UpperCAmelCase = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
UpperCAmelCase , UpperCAmelCase = model(**lowercase , labels=lowercase , use_cache=lowercase )[:2]
else:
# compute label smoothed loss
UpperCAmelCase = model(**lowercase , use_cache=lowercase )[0]
UpperCAmelCase = torch.nn.functional.log_softmax(lowercase , dim=-1 )
UpperCAmelCase , UpperCAmelCase = self.loss_fn(lowercase , lowercase , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def A ( self : Tuple , lowercase : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = inputs.pop('''labels''' )
UpperCAmelCase , UpperCAmelCase = self._compute_loss(lowercase , lowercase , lowercase )
return loss
def A ( self : str , lowercase : nn.Module , lowercase : Dict[str, Union[torch.Tensor, Any]] , lowercase : bool , lowercase : Optional[List[str]] = None , ):
'''simple docstring'''
UpperCAmelCase = self._prepare_inputs(lowercase )
UpperCAmelCase = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
UpperCAmelCase = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **lowercase , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
UpperCAmelCase = self._pad_tensors_to_max_len(lowercase , gen_kwargs['''max_length'''] )
UpperCAmelCase = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
UpperCAmelCase , UpperCAmelCase = self._compute_loss(lowercase , lowercase , lowercase )
UpperCAmelCase = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
UpperCAmelCase = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
UpperCAmelCase = self._pad_tensors_to_max_len(lowercase , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def A ( self : List[str] , lowercase : List[Any] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
f" padded to `max_length`={max_length}" )
UpperCAmelCase = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
UpperCAmelCase = tensor
return padded_tensor
| 34
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : List[Any] , **lowercase : List[str] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : int ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : int , *lowercase : Tuple , **lowercase : Dict ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : List[str] , *lowercase : Optional[int] , **lowercase : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Union[str, Any] , **lowercase : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[int] , *lowercase : Union[str, Any] , **lowercase : Any ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : Tuple , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Any , *lowercase : Dict , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
class _a ( metaclass=__a ):
__a : Any = ["""flax""", """transformers"""]
def __init__( self : Any , *lowercase : Optional[Any] , **lowercase : Optional[int] ):
'''simple docstring'''
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Dict , *lowercase : Optional[Any] , **lowercase : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def A ( cls : Union[str, Any] , *lowercase : str , **lowercase : Any ):
'''simple docstring'''
requires_backends(cls , ['''flax''', '''transformers'''] )
| 34
| 1
|
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : int | float | str , _a : int | float | str ):
if nth_term == "":
return [""]
UpperCAmelCase = int(_a )
UpperCAmelCase = int(_a )
UpperCAmelCase = []
for temp in range(int(_a ) ):
series.append(F"1 / {pow(temp + 1 , int(_a ) )}" if series else '''1''' )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
A =int(input('Enter the last number (nth term) of the P-Series'))
A =int(input('Enter the power for P-Series'))
print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
print(p_series(nth_term, power))
| 34
|
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
A ={
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
A ={
'b0': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 2_24,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 12_80,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 2_40,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 14_08,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 2_60,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 15_36,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 3_00,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 17_92,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 3_80,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 20_48,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 4_56,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 23_04,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 5_28,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 25_60,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 6_00,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def snake_case_ (_a : List[str] ):
UpperCAmelCase = EfficientNetConfig()
UpperCAmelCase = CONFIG_MAP[model_name]['''hidden_dim''']
UpperCAmelCase = CONFIG_MAP[model_name]['''width_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''depth_coef''']
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dropout_rate''']
UpperCAmelCase = CONFIG_MAP[model_name]['''dw_padding''']
UpperCAmelCase = '''huggingface/label-files'''
UpperCAmelCase = '''imagenet-1k-id2label.json'''
UpperCAmelCase = 1_0_0_0
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ():
UpperCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase = Image.open(requests.get(_a , stream=_a ).raw )
return im
def snake_case_ (_a : str ):
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163] , do_center_crop=_a , )
return preprocessor
def snake_case_ (_a : Optional[Any] ):
UpperCAmelCase = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
UpperCAmelCase = sorted(set(_a ) )
UpperCAmelCase = len(_a )
UpperCAmelCase = {b: str(_a ) for b, i in zip(_a , range(_a ) )}
UpperCAmelCase = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
UpperCAmelCase = block_name_mapping[b]
rename_keys.append((F"block{b}_expand_conv/kernel:0", F"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((F"block{b}_expand_bn/gamma:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((F"block{b}_expand_bn/beta:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(F"block{b}_expand_bn/moving_mean:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(F"block{b}_expand_bn/moving_variance:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(F"block{b}_dwconv/depthwise_kernel:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((F"block{b}_bn/gamma:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((F"block{b}_bn/beta:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(F"block{b}_bn/moving_mean:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(F"block{b}_bn/moving_variance:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((F"block{b}_se_reduce/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((F"block{b}_se_reduce/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((F"block{b}_se_expand/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((F"block{b}_se_expand/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(F"block{b}_project_conv/kernel:0", F"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((F"block{b}_project_bn/gamma:0", F"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((F"block{b}_project_bn/beta:0", F"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(F"block{b}_project_bn/moving_mean:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(F"block{b}_project_bn/moving_variance:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
UpperCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCAmelCase = '''efficientnet.''' + item[1]
UpperCAmelCase = '''classifier.weight'''
UpperCAmelCase = '''classifier.bias'''
return key_mapping
def snake_case_ (_a : Dict , _a : List[str] , _a : Dict ):
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
UpperCAmelCase = torch.from_numpy(_a ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
UpperCAmelCase = torch.from_numpy(np.transpose(_a ) )
else:
UpperCAmelCase = torch.from_numpy(_a )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_a )
@torch.no_grad()
def snake_case_ (_a : Optional[Any] , _a : List[str] , _a : Optional[int] , _a : Dict ):
UpperCAmelCase = model_classes[model_name](
include_top=_a , weights='''imagenet''' , input_tensor=_a , input_shape=_a , pooling=_a , classes=1_0_0_0 , classifier_activation='''softmax''' , )
UpperCAmelCase = original_model.trainable_variables
UpperCAmelCase = original_model.non_trainable_variables
UpperCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCAmelCase = param.numpy()
UpperCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
UpperCAmelCase = get_efficientnet_config(_a )
UpperCAmelCase = EfficientNetForImageClassification(_a ).eval()
UpperCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
UpperCAmelCase = rename_keys(_a )
replace_params(_a , _a , _a )
# Initialize preprocessor and preprocess input image
UpperCAmelCase = convert_image_processor(_a )
UpperCAmelCase = preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCAmelCase = hf_model(**_a )
UpperCAmelCase = outputs.logits.detach().numpy()
# Original model inference
UpperCAmelCase = False
UpperCAmelCase = CONFIG_MAP[model_name]['''image_size''']
UpperCAmelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
UpperCAmelCase = image.img_to_array(_a )
UpperCAmelCase = np.expand_dims(_a , axis=0 )
UpperCAmelCase = original_model.predict(_a )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_a , _a , atol=1E-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(_a ):
os.mkdir(_a )
# Save converted model and image processor
hf_model.save_pretrained(_a )
preprocessor.save_pretrained(_a )
if push_to_hub:
# Push model and image processor to hub
print(F"Pushing converted {model_name} to the hub..." )
UpperCAmelCase = F"efficientnet-{model_name}"
preprocessor.push_to_hub(_a )
hf_model.push_to_hub(_a )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
A =parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 34
| 1
|
'''simple docstring'''
def snake_case_ (_a : int , _a : int ):
return "\n".join(
F"{number} * {i} = {number * i}" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 34
|
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
A =soup.find('meta', {'property': 'og:image'})['content']
A =requests.get(image_url).content
A =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(f"""Done. Image saved to disk as {file_name}.""")
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A ={
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
'feature_extraction_whisper': ['WhisperFeatureExtractor'],
'processing_whisper': ['WhisperProcessor'],
'tokenization_whisper': ['WhisperTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['WhisperTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'WhisperForConditionalGeneration',
'WhisperModel',
'WhisperPreTrainedModel',
'WhisperForAudioClassification',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFWhisperForConditionalGeneration',
'TFWhisperModel',
'TFWhisperPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'FlaxWhisperForConditionalGeneration',
'FlaxWhisperModel',
'FlaxWhisperPreTrainedModel',
'FlaxWhisperForAudioClassification',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _a ( __a ):
__a : str = ["""vqvae"""]
def __init__( self : str , lowercase : AutoencoderKL , lowercase : UNetaDConditionModel , lowercase : Mel , lowercase : Union[DDIMScheduler, DDPMScheduler] , ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowercase , scheduler=lowercase , mel=lowercase , vqvae=lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
return 50 if isinstance(self.scheduler , lowercase ) else 1_000
@torch.no_grad()
def __call__( self : Optional[Any] , lowercase : int = 1 , lowercase : str = None , lowercase : np.ndarray = None , lowercase : int = 0 , lowercase : int = 0 , lowercase : int = None , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : float = 0 , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : torch.Tensor = None , lowercase : torch.Tensor = None , lowercase : Tuple=True , ):
'''simple docstring'''
UpperCAmelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowercase , device=self.device , )
UpperCAmelCase = noise
UpperCAmelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowercase , lowercase )
UpperCAmelCase = self.mel.audio_slice_to_image(lowercase )
UpperCAmelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase = (input_image / 255) * 2 - 1
UpperCAmelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase = self.vqvae.encode(torch.unsqueeze(lowercase , 0 ) ).latent_dist.sample(
generator=lowercase )[0]
UpperCAmelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase = int(mask_start_secs * pixels_per_second )
UpperCAmelCase = int(mask_end_secs * pixels_per_second )
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowercase ):
UpperCAmelCase = self.unet(lowercase , lowercase , lowercase )['''sample''']
else:
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
if isinstance(self.scheduler , lowercase ):
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , eta=lowercase , generator=lowercase , )['''prev_sample''']
else:
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , generator=lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
UpperCAmelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase = self.vqvae.decode(lowercase )['''sample''']
UpperCAmelCase = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase = (images * 255).round().astype('''uint8''' )
UpperCAmelCase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
UpperCAmelCase = [self.mel.image_to_audio(lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowercase ) )
@torch.no_grad()
def A ( self : Dict , lowercase : List[Image.Image] , lowercase : int = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler , lowercase )
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase = (sample / 255) * 2 - 1
UpperCAmelCase = torch.Tensor(lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase = self.scheduler.alphas_cumprod[t]
UpperCAmelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase = 1 - alpha_prod_t
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def A ( lowercase : torch.Tensor , lowercase : torch.Tensor , lowercase : float ):
'''simple docstring'''
UpperCAmelCase = acos(torch.dot(torch.flatten(lowercase ) , torch.flatten(lowercase ) ) / torch.norm(lowercase ) / torch.norm(lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(lowercase ) + sin(alpha * theta ) * xa / sin(lowercase )
| 34
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A ={'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =['BeitFeatureExtractor']
A =['BeitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BeitForImageClassification',
'BeitForMaskedImageModeling',
'BeitForSemanticSegmentation',
'BeitModel',
'BeitPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A =[
'FlaxBeitForImageClassification',
'FlaxBeitForMaskedImageModeling',
'FlaxBeitModel',
'FlaxBeitPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
A =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
A =logging.get_logger(__name__)
A =TypeVar('DatasetType', Dataset, IterableDataset)
def snake_case_ (_a : List[DatasetType] , _a : Optional[List[float]] = None , _a : Optional[int] = None , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
else:
return _interleave_iterable_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
def snake_case_ (_a : List[DatasetType] , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : int = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_a , info=_a , split=_a , axis=_a )
else:
return _concatenate_iterable_datasets(_a , info=_a , split=_a , axis=_a )
| 34
| 1
|
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