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'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a ( _UpperCAmelCase ) -> List[Any]:
"""simple docstring"""
return 1 / (1 + np.exp(-z ))
def a ( _UpperCAmelCase , _UpperCAmelCase ) -> Any:
"""simple docstring"""
return (-y * np.log(UpperCamelCase__ ) - (1 - y) * np.log(1 - h )).mean()
def a ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> str:
"""simple docstring"""
a_ = np.dot(UpperCamelCase__ , UpperCamelCase__ )
return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase__ ) ) )
def a ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=7_0_0_0_0 ) -> Any:
"""simple docstring"""
a_ = np.zeros(x.shape[1] )
for iterations in range(UpperCamelCase__ ):
a_ = np.dot(UpperCamelCase__ , UpperCamelCase__ )
a_ = sigmoid_function(UpperCamelCase__ )
a_ = np.dot(x.T , h - y ) / y.size
a_ = theta - alpha * gradient # updating the weights
a_ = np.dot(UpperCamelCase__ , UpperCamelCase__ )
a_ = sigmoid_function(UpperCamelCase__ )
a_ = cost_function(UpperCamelCase__ , UpperCamelCase__ )
if iterations % 1_0_0 == 0:
print(F'''loss: {j} \t''' ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
__lowerCAmelCase =datasets.load_iris()
__lowerCAmelCase =iris.data[:, :2]
__lowerCAmelCase =(iris.target != 0) * 1
__lowerCAmelCase =0.1
__lowerCAmelCase =logistic_reg(alpha, x, y, max_iterations=7_0000)
print("theta: ", theta) # printing the theta i.e our weights vector
def a ( _UpperCAmelCase ) -> Any:
"""simple docstring"""
return sigmoid_function(
np.dot(UpperCamelCase__ , UpperCamelCase__ ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
((__lowerCAmelCase) , (__lowerCAmelCase)) =(x[:, 0].min(), x[:, 0].max())
((__lowerCAmelCase) , (__lowerCAmelCase)) =(x[:, 1].min(), x[:, 1].max())
((__lowerCAmelCase) , (__lowerCAmelCase)) =np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
__lowerCAmelCase =np.c_[xxa.ravel(), xxa.ravel()]
__lowerCAmelCase =predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")
plt.legend()
plt.show()
| 697
|
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: str ):
SCREAMING_SNAKE_CASE__ = model.config
SCREAMING_SNAKE_CASE__ = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
SCREAMING_SNAKE_CASE__ = MBartConfig(
is_decoder=UpperCamelCase__ , is_encoder_decoder=UpperCamelCase__ , add_cross_attention=UpperCamelCase__ , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=UpperCamelCase__ , add_final_layer_norm=UpperCamelCase__ , )
return encoder_config, decoder_config
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: List[str] ):
if "encoder.model" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""encoder.model""" , """encoder""" )
if "decoder.model" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""decoder.model""" , """decoder""" )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if name.startswith("""encoder""" ):
if "layers" in name:
SCREAMING_SNAKE_CASE__ = """encoder.""" + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "mask" not in name:
SCREAMING_SNAKE_CASE__ = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE__ = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "encoder.norm.weight":
SCREAMING_SNAKE_CASE__ = """encoder.layernorm.weight"""
if name == "encoder.norm.bias":
SCREAMING_SNAKE_CASE__ = """encoder.layernorm.bias"""
return name
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: str , UpperCamelCase__: Optional[int] ):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE__ = orig_state_dict.pop(UpperCamelCase__ )
if "qkv" in key:
SCREAMING_SNAKE_CASE__ = key.split(""".""" )
SCREAMING_SNAKE_CASE__ = int(key_split[3] )
SCREAMING_SNAKE_CASE__ = int(key_split[5] )
SCREAMING_SNAKE_CASE__ = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE__ = val[:dim, :]
SCREAMING_SNAKE_CASE__ = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE__ = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE__ = val[:dim]
SCREAMING_SNAKE_CASE__ = val[dim : dim * 2]
SCREAMING_SNAKE_CASE__ = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
SCREAMING_SNAKE_CASE__ = val
return orig_state_dict
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Union[str, Any] , UpperCamelCase__: int=None , UpperCamelCase__: str=False ):
# load original model
SCREAMING_SNAKE_CASE__ = DonutModel.from_pretrained(UpperCamelCase__ ).eval()
# load HuggingFace model
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_configs(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = DonutSwinModel(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = MBartForCausalLM(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = VisionEncoderDecoderModel(encoder=UpperCamelCase__ , decoder=UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE__ = original_model.state_dict()
SCREAMING_SNAKE_CASE__ = convert_state_dict(UpperCamelCase__ , UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
# verify results on scanned document
SCREAMING_SNAKE_CASE__ = load_dataset("""hf-internal-testing/example-documents""" )
SCREAMING_SNAKE_CASE__ = dataset["""test"""][0]["""image"""].convert("""RGB""" )
SCREAMING_SNAKE_CASE__ = XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase__ , from_slow=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
SCREAMING_SNAKE_CASE__ = DonutProcessor(UpperCamelCase__ , UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = processor(UpperCamelCase__ , return_tensors="""pt""" ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
SCREAMING_SNAKE_CASE__ = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
SCREAMING_SNAKE_CASE__ = """When is the coffee break?"""
SCREAMING_SNAKE_CASE__ = task_prompt.replace("""{user_input}""" , UpperCamelCase__ )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
SCREAMING_SNAKE_CASE__ = """<s_rvlcdip>"""
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
SCREAMING_SNAKE_CASE__ = """<s_cord>"""
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
SCREAMING_SNAKE_CASE__ = """s_cord-v2>"""
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
SCREAMING_SNAKE_CASE__ = """<s_zhtrainticket>"""
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
SCREAMING_SNAKE_CASE__ = """hello world"""
else:
raise ValueError("""Model name not supported""" )
SCREAMING_SNAKE_CASE__ = original_model.decoder.tokenizer(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , return_tensors="""pt""" )[
"""input_ids"""
]
SCREAMING_SNAKE_CASE__ = original_model.encoder.model.patch_embed(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = model.encoder.embeddings(UpperCamelCase__ )
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 )
# verify encoder hidden states
SCREAMING_SNAKE_CASE__ = original_model.encoder(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ = model.encoder(UpperCamelCase__ ).last_hidden_state
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-2 )
# verify decoder hidden states
SCREAMING_SNAKE_CASE__ = original_model(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).logits
SCREAMING_SNAKE_CASE__ = model(UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ ).logits
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
model.push_to_hub("""nielsr/""" + model_name.split("""/""" )[-1] , commit_message="""Update model""" )
processor.push_to_hub("""nielsr/""" + model_name.split("""/""" )[-1] , commit_message="""Update model""" )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='naver-clova-ix/donut-base-finetuned-docvqa',
required=False,
type=str,
help='Name of the original model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
required=False,
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 and processor to the 🤗 hub.',
)
_lowerCamelCase = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 6
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ : Union[str, Any] = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : str = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[int] = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
a_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 263
|
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
a_ : Optional[int] = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 1_28,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.0_1),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class __lowercase( unittest.TestCase ):
'''simple docstring'''
@classmethod
def snake_case_ ( cls ):
__lowerCamelCase : Tuple = TOKEN
HfFolder.save_token(__a )
@classmethod
def snake_case_ ( cls ):
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def snake_case_ ( self ):
__lowerCamelCase : List[str] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('test-config' , use_auth_token=self._token )
__lowerCamelCase : Any = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__a , repo_id='test-config' , push_to_hub=__a , use_auth_token=self._token )
__lowerCamelCase : Any = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
def snake_case_ ( self ):
__lowerCamelCase : int = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
__lowerCamelCase : Tuple = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__a , repo_id='valid_org/test-config-org' , push_to_hub=__a , use_auth_token=self._token )
__lowerCamelCase : Tuple = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
def snake_case_ ( self ):
CustomConfig.register_for_auto_class()
__lowerCamelCase : Optional[Any] = CustomConfig(attribute=42 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
__lowerCamelCase : Tuple = AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config''' , trust_remote_code=__a )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 42 )
class __lowercase( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
__lowerCamelCase : Tuple = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__lowerCamelCase : List[str] = c.n_embd + 1 # int
__lowerCamelCase : Dict = c.resid_pdrop + 1.0 # float
__lowerCamelCase : int = not c.scale_attn_weights # bool
__lowerCamelCase : Optional[int] = c.summary_type + 'foo' # str
c.update_from_string(
f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(__a , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(__a , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(__a , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(__a , c.summary_type , 'mismatch for key: summary_type' )
def snake_case_ ( self ):
__lowerCamelCase : Tuple = PretrainedConfig()
__lowerCamelCase : Dict = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
__a , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
__lowerCamelCase : int = [key for key, value in config_common_kwargs.items() if value == getattr(__a , __a )]
if len(__a ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
f''' {", ".join(__a )}.''' )
def snake_case_ ( self ):
with self.assertRaises(__a ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowerCamelCase : int = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
__lowerCamelCase : Optional[int] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(__a )
def snake_case_ ( self ):
# A mock response for an HTTP head request to emulate server down
__lowerCamelCase : List[str] = mock.Mock()
__lowerCamelCase : Tuple = 500
__lowerCamelCase : Tuple = {}
__lowerCamelCase : Optional[Any] = HTTPError
__lowerCamelCase : str = {}
# Download this model to make sure it's in the cache.
__lowerCamelCase : Optional[int] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__a ) as mock_head:
__lowerCamelCase : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def snake_case_ ( self ):
# This test is for deprecated behavior and can be removed in v5
__lowerCamelCase : Optional[Any] = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def snake_case_ ( self ):
__lowerCamelCase : List[Any] = AutoConfig.from_pretrained('bert-base-cased' )
__lowerCamelCase : str = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(__a )
__lowerCamelCase : Optional[int] = 2
json.dump(configuration.to_dict() , open(os.path.join(__a , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__lowerCamelCase : Any = AutoConfig.from_pretrained(__a )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__lowerCamelCase : Any = ['config.42.0.0.json']
__lowerCamelCase : Tuple = 768
configuration.save_pretrained(__a )
shutil.move(os.path.join(__a , 'config.4.0.0.json' ) , os.path.join(__a , 'config.42.0.0.json' ) )
__lowerCamelCase : Dict = AutoConfig.from_pretrained(__a )
self.assertEqual(new_configuration.hidden_size , 768 )
def snake_case_ ( self ):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__lowerCamelCase : List[str] = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
__lowerCamelCase : Tuple = 'v4.0.0'
__lowerCamelCase , __lowerCamelCase : Optional[Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
__a , return_unused_kwargs=__a )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(__a , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__lowerCamelCase : Union[str, Any] = 'v3.0.0'
__lowerCamelCase : Optional[int] = old_transformers.models.auto.AutoConfig.from_pretrained(__a )
self.assertEqual(old_configuration.hidden_size , 768 )
| 263
| 1
|
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
__a :Tuple = logging.get_logger(__name__)
__a :Tuple = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
__a :Any = {
'vocab_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
),
},
'merges_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
),
},
'tokenizer_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
'roberta-base-openai-detector': (
'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
),
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
),
},
}
__a :Union[str, Any] = {
'roberta-base': 512,
'roberta-large': 512,
'roberta-large-mnli': 512,
'distilroberta-base': 512,
'roberta-base-openai-detector': 512,
'roberta-large-openai-detector': 512,
}
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = VOCAB_FILES_NAMES
_lowerCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : Optional[Any] = ['input_ids', 'attention_mask']
_lowerCamelCase : Tuple = RobertaTokenizer
def __init__( self : int , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Any=None , UpperCAmelCase : Optional[Any]="replace" , UpperCAmelCase : Optional[int]="<s>" , UpperCAmelCase : Tuple="</s>" , UpperCAmelCase : Tuple="</s>" , UpperCAmelCase : Optional[Any]="<s>" , UpperCAmelCase : Optional[int]="<unk>" , UpperCAmelCase : Any="<pad>" , UpperCAmelCase : Union[str, Any]="<mask>" , UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : Optional[int]=True , **UpperCAmelCase : Tuple , ):
super().__init__(
UpperCAmelCase , UpperCAmelCase , tokenizer_file=UpperCAmelCase , errors=UpperCAmelCase , bos_token=UpperCAmelCase , eos_token=UpperCAmelCase , sep_token=UpperCAmelCase , cls_token=UpperCAmelCase , unk_token=UpperCAmelCase , pad_token=UpperCAmelCase , mask_token=UpperCAmelCase , add_prefix_space=UpperCAmelCase , trim_offsets=UpperCAmelCase , **UpperCAmelCase , )
A_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , UpperCAmelCase ) != add_prefix_space:
A_ = getattr(UpperCAmelCase , pre_tok_state.pop("type" ) )
A_ = add_prefix_space
A_ = pre_tok_class(**UpperCAmelCase )
A_ = add_prefix_space
A_ = "post_processor"
A_ = getattr(self.backend_tokenizer , UpperCAmelCase , UpperCAmelCase )
if tokenizer_component_instance:
A_ = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
A_ = tuple(state["sep"] )
if "cls" in state:
A_ = tuple(state["cls"] )
A_ = False
if state.get("add_prefix_space" , UpperCAmelCase ) != add_prefix_space:
A_ = add_prefix_space
A_ = True
if state.get("trim_offsets" , UpperCAmelCase ) != trim_offsets:
A_ = trim_offsets
A_ = True
if changes_to_apply:
A_ = getattr(UpperCAmelCase , state.pop("type" ) )
A_ = component_class(**UpperCAmelCase )
setattr(self.backend_tokenizer , UpperCAmelCase , UpperCAmelCase )
@property
def __A ( self : Tuple ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def __A ( self : List[Any] , UpperCAmelCase : str ):
A_ = AddedToken(UpperCAmelCase , lstrip=UpperCAmelCase , rstrip=UpperCAmelCase ) if isinstance(UpperCAmelCase , UpperCAmelCase ) else value
A_ = value
def __A ( self : Tuple , *UpperCAmelCase : Tuple , **UpperCAmelCase : List[str] ):
A_ = kwargs.get("is_split_into_words" , UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*UpperCAmelCase , **UpperCAmelCase )
def __A ( self : Tuple , *UpperCAmelCase : Dict , **UpperCAmelCase : str ):
A_ = kwargs.get("is_split_into_words" , UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*UpperCAmelCase , **UpperCAmelCase )
def __A ( self : Any , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ):
A_ = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
def __A ( self : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any]=None ):
A_ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __A ( self : Optional[int] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ):
A_ = [self.sep_token_id]
A_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 86
|
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
lowerCamelCase__ : Any = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __magic_name__ (datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:int ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def SCREAMING_SNAKE_CASE__ ( self:List[Any] , _a:int , _a:List[Any] , _a:int = 16 , _a:bool = True , _a:Any=None ):
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
snake_case__ = '''cuda'''
else:
snake_case__ = '''cuda''' if torch.cuda.is_available() else '''cpu'''
snake_case__ = AutoModelForCausalLM.from_pretrained(_a )
snake_case__ = model.to(_a )
snake_case__ = AutoTokenizer.from_pretrained(_a )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
snake_case__ = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(_a ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
snake_case__ = model.config.max_length - 1
else:
snake_case__ = model.config.max_length
snake_case__ = tokenizer(
_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , return_tensors='''pt''' , return_attention_mask=_a , ).to(_a )
snake_case__ = encodings['''input_ids''']
snake_case__ = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
snake_case__ = []
snake_case__ = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(_a ) , _a ) ):
snake_case__ = min(start_index + batch_size , len(_a ) )
snake_case__ = encoded_texts[start_index:end_index]
snake_case__ = attn_masks[start_index:end_index]
if add_start_token:
snake_case__ = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_a )
snake_case__ = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
snake_case__ = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(_a ), attn_mask] , dim=1 )
snake_case__ = encoded_batch
with torch.no_grad():
snake_case__ = model(_a , attention_mask=_a ).logits
snake_case__ = out_logits[..., :-1, :].contiguous()
snake_case__ = labels[..., 1:].contiguous()
snake_case__ = attn_mask[..., 1:].contiguous()
snake_case__ = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , _a ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(_a )}
| 33
| 0
|
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
UpperCamelCase = 0
UpperCamelCase = str(_SCREAMING_SNAKE_CASE )
while len(_SCREAMING_SNAKE_CASE ) != 1:
UpperCamelCase = [int(_SCREAMING_SNAKE_CASE ) for i in num_string]
UpperCamelCase = 1
for i in range(0 , len(_SCREAMING_SNAKE_CASE ) ):
total *= numbers[i]
UpperCamelCase = str(_SCREAMING_SNAKE_CASE )
steps += 1
return steps
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
UpperCamelCase = 0
UpperCamelCase = str(_SCREAMING_SNAKE_CASE )
while len(_SCREAMING_SNAKE_CASE ) != 1:
UpperCamelCase = [int(_SCREAMING_SNAKE_CASE ) for i in num_string]
UpperCamelCase = 0
for i in range(0 , len(_SCREAMING_SNAKE_CASE ) ):
total += numbers[i]
UpperCamelCase = str(_SCREAMING_SNAKE_CASE )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 544
|
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _lowerCamelCase :
pass
| 544
| 1
|
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCAmelCase_ ( __lowerCamelCase : Optional[Any] ):
return np.dot(__lowerCamelCase ,__lowerCamelCase )
class a_ :
def __init__( self : int , *,
lowercase : float = np.inf , lowercase : str = "linear" , lowercase : float = 0.0 , ):
"""simple docstring"""
lowercase_ :Optional[int] = regularization
lowercase_ :int = gamma
if kernel == "linear":
lowercase_ :Optional[int] = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError("rbf kernel requires gamma" )
if not isinstance(self.gamma , (float, int) ):
raise ValueError("gamma must be float or int" )
if not self.gamma > 0:
raise ValueError("gamma must be > 0" )
lowercase_ :Union[str, Any] = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
lowercase_ :Optional[Any] = F'Unknown kernel: {kernel}'
raise ValueError(snake_case__ )
def lowercase__ ( self : Tuple , lowercase : ndarray , lowercase : ndarray ):
"""simple docstring"""
return np.dot(snake_case__ , snake_case__ )
def lowercase__ ( self : int , lowercase : ndarray , lowercase : ndarray ):
"""simple docstring"""
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def lowercase__ ( self : int , lowercase : list[ndarray] , lowercase : ndarray ):
"""simple docstring"""
lowercase_ :Tuple = observations
lowercase_ :Optional[Any] = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((lowercase_ ) , ) :Tuple = np.shape(snake_case__ )
def to_minimize(lowercase : ndarray ) -> float:
lowercase_ :List[str] = 0
((lowercase_ ) , ) :Optional[int] = np.shape(snake_case__ )
for i in range(snake_case__ ):
for j in range(snake_case__ ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(snake_case__ )
lowercase_ :List[Any] = LinearConstraint(snake_case__ , 0 , 0 )
lowercase_ :List[str] = Bounds(0 , self.regularization )
lowercase_ :Union[str, Any] = minimize(
snake_case__ , np.ones(snake_case__ ) , bounds=snake_case__ , constraints=[ly_contraint] ).x
lowercase_ :Optional[Any] = l_star
# calculating mean offset of separation plane to points
lowercase_ :List[Any] = 0
for i in range(snake_case__ ):
for j in range(snake_case__ ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
lowercase_ :List[str] = s / n
def lowercase__ ( self : Any , lowercase : ndarray ):
"""simple docstring"""
lowercase_ :Optional[int] = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , snake_case__ )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 172
|
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
SCREAMING_SNAKE_CASE: Optional[int] = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['''memory_attention''', '''encoder_attn'''],
['''attention''', '''attn'''],
['''/''', '''.'''],
['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
['''.LayerNorm.beta''', '''_layer_norm.bias'''],
['''r.layer_''', '''r.layers.'''],
['''output_proj''', '''out_proj'''],
['''ffn.dense_1.''', '''fc2.'''],
['''ffn.dense.''', '''fc1.'''],
['''ffn_layer_norm''', '''final_layer_norm'''],
['''kernel''', '''weight'''],
['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
['''embeddings.weights''', '''shared.weight'''],
]
def _a ( lowerCAmelCase )-> str:
for pegasus_name, hf_name in PATTERNS:
SCREAMING_SNAKE_CASE_ = k.replace(lowerCAmelCase , lowerCAmelCase )
return k
def _a ( lowerCAmelCase , lowerCAmelCase )-> PegasusForConditionalGeneration:
SCREAMING_SNAKE_CASE_ = DEFAULTS.copy()
cfg_kwargs.update(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = PegasusConfig(**lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = PegasusForConditionalGeneration(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = torch_model.model.state_dict()
SCREAMING_SNAKE_CASE_ = {}
for k, v in tf_weights.items():
SCREAMING_SNAKE_CASE_ = rename_state_dict_key(lowerCAmelCase )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
SCREAMING_SNAKE_CASE_ = v.T
SCREAMING_SNAKE_CASE_ = torch.tensor(lowerCAmelCase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
SCREAMING_SNAKE_CASE_ = torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
SCREAMING_SNAKE_CASE_ = mapping['shared.weight']
SCREAMING_SNAKE_CASE_ = mapping['shared.weight']
SCREAMING_SNAKE_CASE_ = {k: torch.zeros_like(lowerCAmelCase ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
mapping.update(**lowerCAmelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = torch_model.model.load_state_dict(lowerCAmelCase , strict=lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = [
k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def _a ( lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" )-> Dict:
SCREAMING_SNAKE_CASE_ = tf.train.list_variables(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = ['Adafactor', 'global_step']
for name, shape in tqdm(lowerCAmelCase , desc='converting tf checkpoint to dict' ):
SCREAMING_SNAKE_CASE_ = any(pat in name for pat in ignore_name )
if skip_key:
continue
SCREAMING_SNAKE_CASE_ = tf.train.load_variable(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = array
return tf_weights
def _a ( lowerCAmelCase , lowerCAmelCase )-> Optional[Any]:
# save tokenizer first
SCREAMING_SNAKE_CASE_ = Path(lowerCAmelCase ).parent.name
SCREAMING_SNAKE_CASE_ = task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
SCREAMING_SNAKE_CASE_ = PegasusTokenizer.from_pretrained('sshleifer/pegasus' , model_max_length=lowerCAmelCase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(lowerCAmelCase )
# convert model
SCREAMING_SNAKE_CASE_ = get_tf_weights_as_numpy(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
SCREAMING_SNAKE_CASE_ = task_specific_params
SCREAMING_SNAKE_CASE_ = convert_pegasus(lowerCAmelCase , lowerCAmelCase )
torch_model.save_pretrained(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = torch_model.state_dict()
sd.pop('model.decoder.embed_positions.weight' )
sd.pop('model.encoder.embed_positions.weight' )
torch.save(lowerCAmelCase , Path(lowerCAmelCase ) / 'pytorch_model.bin' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE: Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
SCREAMING_SNAKE_CASE: int = parser.parse_args()
if args.save_dir is None:
SCREAMING_SNAKE_CASE: List[Any] = Path(args.tf_ckpt_path).parent.name
SCREAMING_SNAKE_CASE: Tuple = os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 360
| 0
|
'''simple docstring'''
from __future__ import annotations
snake_case_ : Optional[int] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
snake_case_ : Tuple = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __snake_case ( _UpperCAmelCase : list[float]):
UpperCamelCase = []
UpperCamelCase = len(_UpperCAmelCase)
for i in range(_UpperCAmelCase):
UpperCamelCase = -1
for j in range(i + 1, _UpperCAmelCase):
if arr[i] < arr[j]:
UpperCamelCase = arr[j]
break
result.append(_UpperCAmelCase)
return result
def __snake_case ( _UpperCAmelCase : list[float]):
UpperCamelCase = []
for i, outer in enumerate(_UpperCAmelCase):
UpperCamelCase = -1
for inner in arr[i + 1 :]:
if outer < inner:
UpperCamelCase = inner
break
result.append(_UpperCAmelCase)
return result
def __snake_case ( _UpperCAmelCase : list[float]):
UpperCamelCase = len(_UpperCAmelCase)
UpperCamelCase = []
UpperCamelCase = [-1] * arr_size
for index in reversed(range(_UpperCAmelCase)):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
UpperCamelCase = stack[-1]
stack.append(arr[index])
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
snake_case_ : Dict = (
'from __main__ import arr, next_greatest_element_slow, '
'next_greatest_element_fast, next_greatest_element'
)
print(
'next_greatest_element_slow():',
timeit('next_greatest_element_slow(arr)', setup=setup),
)
print(
'next_greatest_element_fast():',
timeit('next_greatest_element_fast(arr)', setup=setup),
)
print(
' next_greatest_element():',
timeit('next_greatest_element(arr)', setup=setup),
)
| 350
|
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
snake_case_ : Tuple = logging.get_logger(__name__)
def __snake_case ( _UpperCAmelCase : str, _UpperCAmelCase : int, _UpperCAmelCase : Optional[int]):
return [
int(1000 * (box[0] / width)),
int(1000 * (box[1] / height)),
int(1000 * (box[2] / width)),
int(1000 * (box[3] / height)),
]
def __snake_case ( _UpperCAmelCase : np.ndarray, _UpperCAmelCase : Optional[str], _UpperCAmelCase : Optional[str]):
UpperCamelCase = to_pil_image(_UpperCAmelCase)
UpperCamelCase , UpperCamelCase = pil_image.size
UpperCamelCase = pytesseract.image_to_data(_UpperCAmelCase, lang=_UpperCAmelCase, output_type='''dict''', config=_UpperCAmelCase)
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
UpperCamelCase = [idx for idx, word in enumerate(_UpperCAmelCase) if not word.strip()]
UpperCamelCase = [word for idx, word in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
UpperCamelCase = [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
UpperCamelCase = [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
UpperCamelCase = [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
UpperCamelCase = [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
UpperCamelCase = []
for x, y, w, h in zip(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase):
UpperCamelCase = [x, y, x + w, y + h]
actual_boxes.append(_UpperCAmelCase)
# finally, normalize the bounding boxes
UpperCamelCase = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase))
assert len(_UpperCAmelCase) == len(_UpperCAmelCase), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class lowercase__ ( snake_case_ ):
'''simple docstring'''
_snake_case = ['''pixel_values''']
def __init__( self , lowerCamelCase__ = True , lowerCamelCase__ = None , lowerCamelCase__ = PILImageResampling.BILINEAR , lowerCamelCase__ = True , lowerCamelCase__ = 1 / 2_5_5 , lowerCamelCase__ = True , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = True , lowerCamelCase__ = None , lowerCamelCase__ = "" , **lowerCamelCase__ , ):
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
UpperCamelCase = size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
UpperCamelCase = get_size_dict(lowerCamelCase__ )
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = resample
UpperCamelCase = do_rescale
UpperCamelCase = rescale_value
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
UpperCamelCase = apply_ocr
UpperCamelCase = ocr_lang
UpperCamelCase = tesseract_config
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = PILImageResampling.BILINEAR , lowerCamelCase__ = None , **lowerCamelCase__ , ):
'''simple docstring'''
UpperCamelCase = get_size_dict(lowerCamelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase = (size['''height'''], size['''width'''])
return resize(lowerCamelCase__ , size=lowerCamelCase__ , resample=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , **lowerCamelCase__ , ):
'''simple docstring'''
return rescale(lowerCamelCase__ , scale=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , **lowerCamelCase__ , ):
'''simple docstring'''
return normalize(lowerCamelCase__ , mean=lowerCamelCase__ , std=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__=None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = ChannelDimension.FIRST , **lowerCamelCase__ , ):
'''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(lowerCamelCase__ )
UpperCamelCase = resample if resample is not None else self.resample
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 = apply_ocr if apply_ocr is not None else self.apply_ocr
UpperCamelCase = ocr_lang if ocr_lang is not None else self.ocr_lang
UpperCamelCase = tesseract_config if tesseract_config is not None else self.tesseract_config
UpperCamelCase = make_list_of_images(lowerCamelCase__ )
if not valid_images(lowerCamelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize 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('''If do_normalize is True, image_mean and image_std must be specified.''' )
# All transformations expect numpy arrays.
UpperCamelCase = [to_numpy_array(lowerCamelCase__ ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , '''pytesseract''' )
UpperCamelCase = []
UpperCamelCase = []
for image in images:
UpperCamelCase , UpperCamelCase = apply_tesseract(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
words_batch.append(lowerCamelCase__ )
boxes_batch.append(lowerCamelCase__ )
if do_resize:
UpperCamelCase = [self.resize(image=lowerCamelCase__ , size=lowerCamelCase__ , resample=lowerCamelCase__ ) for image in images]
if do_rescale:
UpperCamelCase = [self.rescale(image=lowerCamelCase__ , scale=lowerCamelCase__ ) for image in images]
if do_normalize:
UpperCamelCase = [self.normalize(image=lowerCamelCase__ , mean=lowerCamelCase__ , std=lowerCamelCase__ ) for image in images]
UpperCamelCase = [to_channel_dimension_format(lowerCamelCase__ , lowerCamelCase__ ) for image in images]
UpperCamelCase = BatchFeature(data={'''pixel_values''': images} , tensor_type=lowerCamelCase__ )
if apply_ocr:
UpperCamelCase = words_batch
UpperCamelCase = boxes_batch
return data
| 350
| 1
|
import re
from filelock import FileLock
try:
import nltk
UpperCamelCase = True
except (ImportError, ModuleNotFoundError):
UpperCamelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
re.sub("<n>" , "" , lowerCAmelCase_ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(lowerCAmelCase_ ) )
| 61
|
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = 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=lowerCAmelCase_ , default=1 , help="Number of TPU cores to use (1 or 8)." )
# positional
parser.add_argument(
"training_script" , type=lowerCAmelCase_ , 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=lowerCAmelCase_ )
return parser.parse_args()
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = parse_args()
# Import training_script as a module.
lowerCAmelCase__ = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowerCAmelCase__ = script_fpath.stem
lowerCAmelCase__ = importlib.import_module(lowerCAmelCase_ )
# Patch sys.argv
lowerCAmelCase__ = [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()
| 61
| 1
|
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class lowerCamelCase_ :
def __init__( self : Dict , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Union[str, Any]=13 , lowerCAmelCase__ : Any=10 , lowerCAmelCase__ : Tuple=3 , lowerCAmelCase__ : str=2 , lowerCAmelCase__ : Any=2 , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : List[str]=True , lowerCAmelCase__ : Any=32 , lowerCAmelCase__ : Dict=5 , lowerCAmelCase__ : Any=4 , lowerCAmelCase__ : Any=37 , lowerCAmelCase__ : Optional[Any]="gelu" , lowerCAmelCase__ : Optional[int]=0.1 , lowerCAmelCase__ : Tuple=0.1 , lowerCAmelCase__ : Union[str, Any]=10 , lowerCAmelCase__ : Union[str, Any]=0.02 , lowerCAmelCase__ : Any="divided_space_time" , lowerCAmelCase__ : Union[str, Any]=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = parent
SCREAMING_SNAKE_CASE : int = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = image_size
SCREAMING_SNAKE_CASE : int = num_channels
SCREAMING_SNAKE_CASE : List[Any] = patch_size
SCREAMING_SNAKE_CASE : Dict = num_frames
SCREAMING_SNAKE_CASE : Tuple = is_training
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : str = hidden_size
SCREAMING_SNAKE_CASE : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : int = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = attention_type
SCREAMING_SNAKE_CASE : Any = initializer_range
SCREAMING_SNAKE_CASE : str = scope
SCREAMING_SNAKE_CASE : Optional[Any] = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
SCREAMING_SNAKE_CASE : Optional[Any] = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE : List[Any] = (num_frames) * self.num_patches_per_frame + 1
def __lowercase ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : str = None
if self.use_labels:
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def __lowercase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.num_labels
return config
def __lowercase ( self : List[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = TimesformerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase ( self : str , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = TimesformerForVideoClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
SCREAMING_SNAKE_CASE : Tuple = model(__lowerCAmelCase )
# verify the logits shape
SCREAMING_SNAKE_CASE : Optional[int] = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __lowerCAmelCase )
def __lowercase ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = config_and_inputs
SCREAMING_SNAKE_CASE : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
_lowerCAmelCase : Optional[int] = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
_lowerCAmelCase : Optional[int] = (
{'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase : str = False
_lowerCAmelCase : Any = False
_lowerCAmelCase : Union[str, Any] = False
_lowerCAmelCase : List[Any] = False
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = TimesformerModelTester(self )
SCREAMING_SNAKE_CASE : List[Any] = ConfigTester(
self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def __lowercase ( self : Tuple , lowerCAmelCase__ : Any , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any]=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = copy.deepcopy(__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def __lowercase ( self : List[Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def __lowercase ( self : int ):
"""simple docstring"""
pass
def __lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Any = model_class(__lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowerCAmelCase , nn.Linear ) )
def __lowercase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Optional[int] = model_class(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : str = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : Optional[int] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def __lowercase ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def __lowercase ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__lowerCAmelCase )
@slow
def __lowercase ( self : Dict ):
"""simple docstring"""
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : List[str] = TimesformerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
if not self.has_attentions:
pass
else:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE : List[Any] = True
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.seq_length
SCREAMING_SNAKE_CASE : Dict = self.model_tester.num_frames
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : Union[str, Any] = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : int = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.attentions
self.assertEqual(len(__lowerCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : int = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : Any = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.attentions
self.assertEqual(len(__lowerCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
SCREAMING_SNAKE_CASE : Union[str, Any] = len(__lowerCAmelCase )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : Dict = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[int] = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
self.assertEqual(out_len + 1 , len(__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.attentions
self.assertEqual(len(__lowerCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : str ):
SCREAMING_SNAKE_CASE : Tuple = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : Any = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
SCREAMING_SNAKE_CASE : Any = outputs.hidden_states
SCREAMING_SNAKE_CASE : int = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__lowerCAmelCase ) , __lowerCAmelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : List[Any] = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE : Dict = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def UpperCAmelCase ( ):
SCREAMING_SNAKE_CASE : Optional[int] = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
SCREAMING_SNAKE_CASE : Any = np.load(UpperCAmelCase__ )
return list(UpperCAmelCase__ )
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def __lowercase ( self : List[str] ):
"""simple docstring"""
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__lowerCAmelCase )
SCREAMING_SNAKE_CASE : Dict = self.default_image_processor
SCREAMING_SNAKE_CASE : Tuple = prepare_video()
SCREAMING_SNAKE_CASE : Tuple = image_processor(video[:8] , return_tensors='''pt''' ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(**__lowerCAmelCase )
# verify the logits
SCREAMING_SNAKE_CASE : List[Any] = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
SCREAMING_SNAKE_CASE : int = torch.tensor([-0.3016, -0.7713, -0.4205] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1e-4 ) )
| 710
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : int = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''的''',
'''价''',
'''格''',
'''是''',
'''15''',
'''便''',
'''alex''',
'''##andra''',
'''，''',
'''。''',
'''-''',
'''t''',
'''shirt''',
]
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''do_resize''': True,
'''size''': {'''height''': 2_24, '''width''': 2_24},
'''do_center_crop''': True,
'''crop_size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
'''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
'''do_convert_rgb''': True,
}
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(self.tmpdirname , lowerCAmelCase__ )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(lowerCAmelCase__ , lowerCAmelCase__ )
def __lowercase ( self : str , **lowerCAmelCase__ : int ):
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def __lowercase ( self : Any , **lowerCAmelCase__ : Dict ):
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def __lowercase ( self : List[Any] , **lowerCAmelCase__ : Dict ):
"""simple docstring"""
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def __lowercase ( self : List[Any] ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE : Optional[Any] = [Image.fromarray(np.moveaxis(lowerCAmelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE : Dict = ChineseCLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : List[str] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = ChineseCLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : Union[str, Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowerCAmelCase__ )
self.assertIsInstance(processor_fast.tokenizer , lowerCAmelCase__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowerCAmelCase__ )
self.assertIsInstance(processor_fast.image_processor , lowerCAmelCase__ )
def __lowercase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_tokenizer(cls_token='''(CLS)''' , sep_token='''(SEP)''' )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor(do_normalize=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='''(CLS)''' , sep_token='''(SEP)''' , do_normalize=lowerCAmelCase__ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCAmelCase__ )
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Tuple = ChineseCLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : List[Any] = image_processor(lowerCAmelCase__ , return_tensors='''np''' )
SCREAMING_SNAKE_CASE : List[Any] = processor(images=lowerCAmelCase__ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Tuple = ChineseCLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = '''Alexandra，T-shirt的价格是15便士。'''
SCREAMING_SNAKE_CASE : Union[str, Any] = processor(text=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer(lowerCAmelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Tuple = ChineseCLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = '''Alexandra，T-shirt的价格是15便士。'''
SCREAMING_SNAKE_CASE : Tuple = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : List[str] = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__ ):
processor()
def __lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self.get_image_processor()
SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Tuple = ChineseCLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE : Any = processor.batch_decode(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.batch_decode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def __lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.get_image_processor()
SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Dict = ChineseCLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = '''Alexandra，T-shirt的价格是15便士。'''
SCREAMING_SNAKE_CASE : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : Any = processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 464
| 0
|
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __lowercase ( ) -> Iterator[int]:
"""simple docstring"""
__a = 2
while True:
if is_prime(__SCREAMING_SNAKE_CASE ):
yield num
num += 1
def __lowercase ( __SCREAMING_SNAKE_CASE = 200_0000 ) -> int:
"""simple docstring"""
return sum(takewhile(lambda __SCREAMING_SNAKE_CASE : x < n , prime_generator() ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 582
|
'''simple docstring'''
from __future__ import annotations
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE__ : int = 0 ):
'''simple docstring'''
__a = key
def __a ( self : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__a = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_5_5
return [chr(ord(SCREAMING_SNAKE_CASE__ ) ^ key ) for ch in content]
def __a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__a = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_5_5
return [chr(ord(SCREAMING_SNAKE_CASE__ ) ^ key ) for ch in content]
def __a ( self : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int = 0 ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__a = key or self.__key or 1
# make sure key can be any size
while key > 2_5_5:
key -= 2_5_5
# This will be returned
__a = """"""
for ch in content:
ans += chr(ord(SCREAMING_SNAKE_CASE__ ) ^ key )
return ans
def __a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int = 0 ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__a = key or self.__key or 1
# make sure key can be any size
while key > 2_5_5:
key -= 2_5_5
# This will be returned
__a = """"""
for ch in content:
ans += chr(ord(SCREAMING_SNAKE_CASE__ ) ^ key )
return ans
def __a ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int = 0 ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
try:
with open(SCREAMING_SNAKE_CASE__ ) as fin, open("""encrypt.out""" , """w+""" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
except OSError:
return False
return True
def __a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
try:
with open(SCREAMING_SNAKE_CASE__ ) as fin, open("""decrypt.out""" , """w+""" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 582
| 1
|
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
a_ = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False , ):
output_path.parent.mkdir(parents=__UpperCamelCase , exist_ok=__UpperCamelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
__UpperCamelCase , __UpperCamelCase , f=output_path.as_posix() , input_names=__UpperCamelCase , output_names=__UpperCamelCase , dynamic_axes=__UpperCamelCase , do_constant_folding=__UpperCamelCase , use_external_data_format=__UpperCamelCase , enable_onnx_checker=__UpperCamelCase , opset_version=__UpperCamelCase , )
else:
export(
__UpperCamelCase , __UpperCamelCase , f=output_path.as_posix() , input_names=__UpperCamelCase , output_names=__UpperCamelCase , dynamic_axes=__UpperCamelCase , do_constant_folding=__UpperCamelCase , opset_version=__UpperCamelCase , )
@torch.no_grad()
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False ):
__lowercase : str = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__lowercase : Optional[Any] = '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
else:
__lowercase : str = '''cpu'''
__lowercase : int = StableDiffusionPipeline.from_pretrained(__UpperCamelCase , torch_dtype=__UpperCamelCase ).to(__UpperCamelCase )
__lowercase : Dict = Path(__UpperCamelCase )
# TEXT ENCODER
__lowercase : Optional[Any] = pipeline.text_encoder.config.max_position_embeddings
__lowercase : Optional[int] = pipeline.text_encoder.config.hidden_size
__lowercase : List[str] = pipeline.tokenizer(
'''A sample prompt''' , padding='''max_length''' , max_length=pipeline.tokenizer.model_max_length , truncation=__UpperCamelCase , return_tensors='''pt''' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=__UpperCamelCase , dtype=torch.intaa )) , output_path=output_path / '''text_encoder''' / '''model.onnx''' , ordered_input_names=['''input_ids'''] , output_names=['''last_hidden_state''', '''pooler_output'''] , dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''sequence'''},
} , opset=__UpperCamelCase , )
del pipeline.text_encoder
# UNET
__lowercase : Optional[Any] = pipeline.unet.config.in_channels
__lowercase : Optional[Any] = pipeline.unet.config.sample_size
__lowercase : Tuple = output_path / '''unet''' / '''model.onnx'''
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
torch.randn(2 ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
torch.randn(2 , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
False,
) , output_path=__UpperCamelCase , ordered_input_names=['''sample''', '''timestep''', '''encoder_hidden_states''', '''return_dict'''] , output_names=['''out_sample'''] , dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''timestep''': {0: '''batch'''},
'''encoder_hidden_states''': {0: '''batch''', 1: '''sequence'''},
} , opset=__UpperCamelCase , use_external_data_format=__UpperCamelCase , )
__lowercase : int = str(unet_path.absolute().as_posix() )
__lowercase : Any = os.path.dirname(__UpperCamelCase )
__lowercase : str = onnx.load(__UpperCamelCase )
# clean up existing tensor files
shutil.rmtree(__UpperCamelCase )
os.mkdir(__UpperCamelCase )
# collate external tensor files into one
onnx.save_model(
__UpperCamelCase , __UpperCamelCase , save_as_external_data=__UpperCamelCase , all_tensors_to_one_file=__UpperCamelCase , location='''weights.pb''' , convert_attribute=__UpperCamelCase , )
del pipeline.unet
# VAE ENCODER
__lowercase : Tuple = pipeline.vae
__lowercase : Any = vae_encoder.config.in_channels
__lowercase : str = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
__lowercase : Any = lambda __UpperCamelCase , __UpperCamelCase : vae_encoder.encode(__UpperCamelCase , __UpperCamelCase )[0].sample()
onnx_export(
__UpperCamelCase , model_args=(
torch.randn(1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
False,
) , output_path=output_path / '''vae_encoder''' / '''model.onnx''' , ordered_input_names=['''sample''', '''return_dict'''] , output_names=['''latent_sample'''] , dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=__UpperCamelCase , )
# VAE DECODER
__lowercase : Union[str, Any] = pipeline.vae
__lowercase : List[str] = vae_decoder.config.latent_channels
__lowercase : int = vae_decoder.config.out_channels
# forward only through the decoder part
__lowercase : str = vae_encoder.decode
onnx_export(
__UpperCamelCase , model_args=(
torch.randn(1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
False,
) , output_path=output_path / '''vae_decoder''' / '''model.onnx''' , ordered_input_names=['''latent_sample''', '''return_dict'''] , output_names=['''sample'''] , dynamic_axes={
'''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=__UpperCamelCase , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
__lowercase : Tuple = pipeline.safety_checker
__lowercase : List[str] = safety_checker.config.vision_config.num_channels
__lowercase : List[Any] = safety_checker.config.vision_config.image_size
__lowercase : List[str] = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
torch.randn(1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
) , output_path=output_path / '''safety_checker''' / '''model.onnx''' , ordered_input_names=['''clip_input''', '''images'''] , output_names=['''out_images''', '''has_nsfw_concepts'''] , dynamic_axes={
'''clip_input''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''images''': {0: '''batch''', 1: '''height''', 2: '''width''', 3: '''channels'''},
} , opset=__UpperCamelCase , )
del pipeline.safety_checker
__lowercase : Optional[Any] = OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker''' )
__lowercase : List[str] = pipeline.feature_extractor
else:
__lowercase : Dict = None
__lowercase : Tuple = None
__lowercase : Union[str, Any] = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_encoder''' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_decoder''' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''text_encoder''' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / '''unet''' ) , scheduler=pipeline.scheduler , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(__UpperCamelCase )
print('''ONNX pipeline saved to''' , __UpperCamelCase )
del pipeline
del onnx_pipeline
__lowercase : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(__UpperCamelCase , provider='''CPUExecutionProvider''' )
print('''ONNX pipeline is loadable''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
type=str,
required=True,
help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
)
parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--opset',
default=1_4,
type=int,
help='The version of the ONNX operator set to use.',
)
parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')
a_ = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 523
|
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = "x" , __UpperCamelCase = 10**-10 , __UpperCamelCase = 1 , ):
__lowercase : Optional[int] = symbols(__UpperCamelCase )
__lowercase : Tuple = lambdify(__UpperCamelCase , __UpperCamelCase )
__lowercase : Optional[Any] = lambdify(__UpperCamelCase , diff(__UpperCamelCase , __UpperCamelCase ) )
__lowercase : int = starting_point
while True:
if diff_function(__UpperCamelCase ) != 0:
__lowercase : int = prev_guess - multiplicity * func(__UpperCamelCase ) / diff_function(
__UpperCamelCase )
else:
raise ZeroDivisionError('''Could not find root''' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
__lowercase : List[str] = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")
# Find root of polynomial
# Find fourth Root of 5
print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}")
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
F"{newton_raphson('log(y) - 1', 2, variable='y')}",
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
F"{newton_raphson('exp(x) - 1', 1_0, precision=0.005)}",
)
# Find root of cos(x)
print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")
| 523
| 1
|
"""simple docstring"""
import requests
SCREAMING_SNAKE_CASE__:int = """""" # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE__:Any = """https://api.openweathermap.org/data/2.5/"""
def _lowerCamelCase( a = "Chicago" , a = APPID ):
return requests.get(URL_BASE + "weather" , params=locals() ).json()
def _lowerCamelCase( a = "Kolkata, India" , a = APPID ):
return requests.get(URL_BASE + "forecast" , params=locals() ).json()
def _lowerCamelCase( a = 55.68 , a = 12.57 , a = APPID ):
return requests.get(URL_BASE + "onecall" , params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
SCREAMING_SNAKE_CASE__:Tuple = input("""Enter a location:""").strip()
if location:
pprint(current_weather(location))
else:
break
| 528
|
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
SCREAMING_SNAKE_CASE__:List[str] = logging.getLogger(__name__)
def _lowerCamelCase( ):
__a = argparse.ArgumentParser(
description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset." )
parser.add_argument(
"--dataset_name" , type=a , default="wikitext" , help="Name of the training. Explore datasets at: hf.co/datasets." , )
parser.add_argument(
"--dataset_config" , type=a , default="wikitext-103-raw-v1" , help="Configuration name of the dataset." )
parser.add_argument(
"--tokenizer_name_or_path" , type=a , default="sayakpaul/unigram-tokenizer-wikitext" , help="Tokenizer identifier. Can be a local filepath or a Hub identifier." , )
parser.add_argument(
"--shard_size" , type=a , default=1_0_0_0 , help="Number of entries to go in a single shard." , )
parser.add_argument("--split" , type=a , default="train" , choices=["train", "test", "validation"] )
parser.add_argument(
"--limit" , default=a , type=a , help="Limit the number of shards (used for debugging)." , )
parser.add_argument(
"--max_length" , type=a , default=5_1_2 , help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
" sequence length that is a multiple of 8." , )
parser.add_argument(
"--output_dir" , default="tf-tpu" , type=a , help="Output directory where the TFRecord shards will be saved. If the"
" path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
" shards will be directly saved to a Google Cloud Storage bucket." , )
__a = parser.parse_args()
return args
def _lowerCamelCase( a ):
def fn(a ):
return tokenizer(examples["text"] )
return fn
def _lowerCamelCase( a ):
__a = []
for i in range(len(tokenized_data["input_ids"] ) ):
__a = {
"input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i] ) ),
"attention_mask": tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i] ) ),
}
__a = tf.train.Features(feature=a )
__a = tf.train.Example(features=a )
__a = example.SerializeToString()
records.append(a )
return records
def _lowerCamelCase( a ):
__a = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
__a = min(len(a ) , args.limit )
__a = dataset.select(range(a ) )
print(F"Limiting the dataset to {args.limit} entries." )
__a = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
__a = os.path.join(args.output_dir , args.split )
if not os.path.exists(a ):
os.makedirs(a )
else:
__a = os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
__a = tokenize_function(a )
__a = dataset.map(a , batched=a , num_proc=4 , remove_columns=["text"] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(a ):
# Concatenate all texts.
__a = {k: sum(examples[k] , [] ) for k in examples.keys()}
__a = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
__a = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
__a = {
k: [t[i : i + args.max_length] for i in range(0 , a , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
__a = dataset_tokenized.map(a , batched=a , batch_size=1_0_0_0 , num_proc=4 )
__a = 0
__a = 0
for shard in range(0 , len(a ) , args.shard_size ):
__a = grouped_dataset[shard : shard + args.shard_size]
__a = len(dataset_snapshot["input_ids"] )
__a = os.path.join(a , F"dataset-{shard_count}-{records_containing}.tfrecord" )
__a = get_serialized_examples(a )
with tf.io.TFRecordWriter(a ) as out_file:
for i in range(len(a ) ):
__a = serialized_examples[i]
out_file.write(a )
print("Wrote file {} containing {} records".format(a , a ) )
shard_count += 1
total_records += records_containing
with open(F"split-{args.split}-records-count.txt" , "w" ) as f:
print(F"Total {args.split} records: {total_records}" , file=a )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__:List[Any] = parse_args()
main(args)
| 528
| 1
|
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase( __lowerCamelCase ):
__SCREAMING_SNAKE_CASE : str = (UnCLIPScheduler,)
def __lowerCAmelCase ( self : Optional[int] , **SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
__a : Any = {
'num_train_timesteps': 1_0_0_0,
'variance_type': 'fixed_small_log',
'clip_sample': True,
'clip_sample_range': 1.0,
'prediction_type': 'epsilon',
}
config.update(**SCREAMING_SNAKE_CASE__ )
return config
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : str ):
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
for clip_sample_range in [1, 5, 1_0, 2_0]:
self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
for time_step in [0, 5_0_0, 9_9_9]:
for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__ , prev_timestep=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
__a : List[Any] = self.scheduler_classes[0]
__a : int = self.get_scheduler_config(variance_type='fixed_small_log' )
__a : Dict = scheduler_class(**SCREAMING_SNAKE_CASE__ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000e-10 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_549_625 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.9_994_987 ) ) < 1e-5
def __lowerCAmelCase ( self : Any ):
'''simple docstring'''
__a : Tuple = self.scheduler_classes[0]
__a : Dict = self.get_scheduler_config(variance_type='learned_range' )
__a : List[str] = scheduler_class(**SCREAMING_SNAKE_CASE__ )
__a : str = 0.5
assert scheduler._get_variance(1 , predicted_variance=SCREAMING_SNAKE_CASE__ ) - -10.1_712_790 < 1e-5
assert scheduler._get_variance(4_8_7 , predicted_variance=SCREAMING_SNAKE_CASE__ ) - -5.7_998_052 < 1e-5
assert scheduler._get_variance(9_9_9 , predicted_variance=SCREAMING_SNAKE_CASE__ ) - -0.0_010_011 < 1e-5
def __lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
__a : List[Any] = self.scheduler_classes[0]
__a : Optional[int] = self.get_scheduler_config()
__a : Union[str, Any] = scheduler_class(**SCREAMING_SNAKE_CASE__ )
__a : Union[str, Any] = scheduler.timesteps
__a : Tuple = self.dummy_model()
__a : Optional[Any] = self.dummy_sample_deter
__a : Dict = torch.manual_seed(0 )
for i, t in enumerate(SCREAMING_SNAKE_CASE__ ):
# 1. predict noise residual
__a : List[Any] = model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# 2. predict previous mean of sample x_t-1
__a : Dict = scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ ).prev_sample
__a : List[str] = pred_prev_sample
__a : List[Any] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
__a : Union[str, Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 252.2_682_495 ) < 1e-2
assert abs(result_mean.item() - 0.3_284_743 ) < 1e-3
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
__a : Dict = self.scheduler_classes[0]
__a : Tuple = self.get_scheduler_config()
__a : Any = scheduler_class(**SCREAMING_SNAKE_CASE__ )
scheduler.set_timesteps(2_5 )
__a : List[Any] = scheduler.timesteps
__a : List[Any] = self.dummy_model()
__a : Optional[Any] = self.dummy_sample_deter
__a : Dict = torch.manual_seed(0 )
for i, t in enumerate(SCREAMING_SNAKE_CASE__ ):
# 1. predict noise residual
__a : int = model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if i + 1 == timesteps.shape[0]:
__a : Tuple = None
else:
__a : List[str] = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
__a : List[str] = scheduler.step(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , prev_timestep=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ ).prev_sample
__a : Tuple = pred_prev_sample
__a : str = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
__a : Tuple = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 258.2_044_983 ) < 1e-2
assert abs(result_mean.item() - 0.3_362_038 ) < 1e-3
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
pass
def __lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
pass
| 706
|
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
SCREAMING_SNAKE_CASE__ = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def UpperCAmelCase__ ( lowerCamelCase_ : List[Any] ):
__a : Optional[int] = numpy.dtype(numpy.uintaa ).newbyteorder('>' )
return numpy.frombuffer(bytestream.read(4 ) , dtype=lowerCamelCase_ )[0]
@deprecated(lowerCamelCase_ , 'Please use tf.data to implement this functionality.' )
def UpperCAmelCase__ ( lowerCamelCase_ : Dict ):
print('Extracting' , f.name )
with gzip.GzipFile(fileobj=lowerCamelCase_ ) as bytestream:
__a : Union[str, Any] = _readaa(lowerCamelCase_ )
if magic != 2_0_5_1:
raise ValueError(
'Invalid magic number %d in MNIST image file: %s' % (magic, f.name) )
__a : Any = _readaa(lowerCamelCase_ )
__a : int = _readaa(lowerCamelCase_ )
__a : List[Any] = _readaa(lowerCamelCase_ )
__a : str = bytestream.read(rows * cols * num_images )
__a : List[str] = numpy.frombuffer(lowerCamelCase_ , dtype=numpy.uinta )
__a : Optional[Any] = data.reshape(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , 1 )
return data
@deprecated(lowerCamelCase_ , 'Please use tf.one_hot on tensors.' )
def UpperCAmelCase__ ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ):
__a : List[Any] = labels_dense.shape[0]
__a : str = numpy.arange(lowerCamelCase_ ) * num_classes
__a : Any = numpy.zeros((num_labels, num_classes) )
__a : List[str] = 1
return labels_one_hot
@deprecated(lowerCamelCase_ , 'Please use tf.data to implement this functionality.' )
def UpperCAmelCase__ ( lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Optional[int]=False , lowerCamelCase_ : int=1_0 ):
print('Extracting' , f.name )
with gzip.GzipFile(fileobj=lowerCamelCase_ ) as bytestream:
__a : List[str] = _readaa(lowerCamelCase_ )
if magic != 2_0_4_9:
raise ValueError(
'Invalid magic number %d in MNIST label file: %s' % (magic, f.name) )
__a : Optional[int] = _readaa(lowerCamelCase_ )
__a : Dict = bytestream.read(lowerCamelCase_ )
__a : Union[str, Any] = numpy.frombuffer(lowerCamelCase_ , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(lowerCamelCase_ , lowerCamelCase_ )
return labels
class _UpperCamelCase:
@deprecated(
SCREAMING_SNAKE_CASE__ , 'Please use alternatives such as official/mnist/_DataSet.py'
' from tensorflow/models.' , )
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any]=False , SCREAMING_SNAKE_CASE__ : Any=False , SCREAMING_SNAKE_CASE__ : Any=dtypes.floataa , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : int=None , ):
'''simple docstring'''
__a , __a : List[Any] = random_seed.get_seed(SCREAMING_SNAKE_CASE__ )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
__a : Optional[Any] = dtypes.as_dtype(SCREAMING_SNAKE_CASE__ ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype )
if fake_data:
__a : Dict = 1_0_0_0_0
__a : Tuple = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
__a : List[str] = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
__a : Optional[int] = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
__a : str = images.astype(numpy.floataa )
__a : Optional[Any] = numpy.multiply(SCREAMING_SNAKE_CASE__ , 1.0 / 255.0 )
__a : int = images
__a : Optional[Any] = labels
__a : Tuple = 0
__a : Tuple = 0
@property
def __lowerCAmelCase ( self : Any ):
'''simple docstring'''
return self._images
@property
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
return self._labels
@property
def __lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
return self._num_examples
@property
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
return self._epochs_completed
def __lowerCAmelCase ( self : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str=False , SCREAMING_SNAKE_CASE__ : str=True ):
'''simple docstring'''
if fake_data:
__a : List[Any] = [1] * 7_8_4
__a : Optional[int] = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(SCREAMING_SNAKE_CASE__ )],
[fake_label for _ in range(SCREAMING_SNAKE_CASE__ )],
)
__a : Optional[Any] = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
__a : Union[str, Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(SCREAMING_SNAKE_CASE__ )
__a : Union[str, Any] = self.images[perma]
__a : List[Any] = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
__a : List[str] = self._num_examples - start
__a : Tuple = self._images[start : self._num_examples]
__a : Union[str, Any] = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
__a : str = numpy.arange(self._num_examples )
numpy.random.shuffle(SCREAMING_SNAKE_CASE__ )
__a : Optional[int] = self.images[perm]
__a : Any = self.labels[perm]
# Start next epoch
__a : Dict = 0
__a : List[Any] = batch_size - rest_num_examples
__a : str = self._index_in_epoch
__a : List[Any] = self._images[start:end]
__a : List[Any] = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
__a : List[Any] = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(lowerCamelCase_ , 'Please write your own downloading logic.' )
def UpperCAmelCase__ ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : str , lowerCamelCase_ : List[Any] ):
if not gfile.Exists(lowerCamelCase_ ):
gfile.MakeDirs(lowerCamelCase_ )
__a : Optional[int] = os.path.join(lowerCamelCase_ , lowerCamelCase_ )
if not gfile.Exists(lowerCamelCase_ ):
urllib.request.urlretrieve(lowerCamelCase_ , lowerCamelCase_ ) # noqa: S310
with gfile.GFile(lowerCamelCase_ ) as f:
__a : str = f.size()
print('Successfully downloaded' , lowerCamelCase_ , lowerCamelCase_ , 'bytes.' )
return filepath
@deprecated(
lowerCamelCase_ , 'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')' )
def UpperCAmelCase__ ( lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Tuple=False , lowerCamelCase_ : Optional[Any]=False , lowerCamelCase_ : Any=dtypes.floataa , lowerCamelCase_ : Optional[int]=True , lowerCamelCase_ : Union[str, Any]=5_0_0_0 , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Dict=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=lowerCamelCase_ , one_hot=lowerCamelCase_ , dtype=lowerCamelCase_ , seed=lowerCamelCase_ )
__a : List[str] = fake()
__a : Union[str, Any] = fake()
__a : Optional[Any] = fake()
return _Datasets(train=lowerCamelCase_ , validation=lowerCamelCase_ , test=lowerCamelCase_ )
if not source_url: # empty string check
__a : Dict = DEFAULT_SOURCE_URL
__a : int = 'train-images-idx3-ubyte.gz'
__a : List[Any] = 'train-labels-idx1-ubyte.gz'
__a : Any = 't10k-images-idx3-ubyte.gz'
__a : Optional[int] = 't10k-labels-idx1-ubyte.gz'
__a : Optional[int] = _maybe_download(
lowerCamelCase_ , lowerCamelCase_ , source_url + train_images_file )
with gfile.Open(lowerCamelCase_ , 'rb' ) as f:
__a : List[Any] = _extract_images(lowerCamelCase_ )
__a : Any = _maybe_download(
lowerCamelCase_ , lowerCamelCase_ , source_url + train_labels_file )
with gfile.Open(lowerCamelCase_ , 'rb' ) as f:
__a : str = _extract_labels(lowerCamelCase_ , one_hot=lowerCamelCase_ )
__a : List[str] = _maybe_download(
lowerCamelCase_ , lowerCamelCase_ , source_url + test_images_file )
with gfile.Open(lowerCamelCase_ , 'rb' ) as f:
__a : Union[str, Any] = _extract_images(lowerCamelCase_ )
__a : Dict = _maybe_download(
lowerCamelCase_ , lowerCamelCase_ , source_url + test_labels_file )
with gfile.Open(lowerCamelCase_ , 'rb' ) as f:
__a : Optional[Any] = _extract_labels(lowerCamelCase_ , one_hot=lowerCamelCase_ )
if not 0 <= validation_size <= len(lowerCamelCase_ ):
__a : Optional[Any] = (
'Validation size should be between 0 and '
f'''{len(lowerCamelCase_ )}. Received: {validation_size}.'''
)
raise ValueError(lowerCamelCase_ )
__a : int = train_images[:validation_size]
__a : Any = train_labels[:validation_size]
__a : Optional[Any] = train_images[validation_size:]
__a : int = train_labels[validation_size:]
__a : Any = {'dtype': dtype, 'reshape': reshape, 'seed': seed}
__a : str = _DataSet(lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
__a : Any = _DataSet(lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
__a : str = _DataSet(lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
return _Datasets(train=lowerCamelCase_ , validation=lowerCamelCase_ , test=lowerCamelCase_ )
| 577
| 0
|
"""simple docstring"""
from __future__ import annotations
import math
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _UpperCAmelCase ):
lowercase__: Union[str, Any] = size
# approximate the overall size of segment tree with given value
lowercase__: Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
lowercase__: Union[str, Any] = [0 for i in range(0 , 4 * size )]
lowercase__: Optional[Any] = [0 for i in range(0 , 4 * size )] # flag for lazy update
def _snake_case ( self , _UpperCAmelCase ):
return idx * 2
def _snake_case ( self , _UpperCAmelCase ):
return idx * 2 + 1
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if left_element == right_element:
lowercase__: Optional[int] = a[left_element - 1]
else:
lowercase__: Any = (left_element + right_element) // 2
self.build(self.left(A__ ) , A__ , A__ , A__ )
self.build(self.right(A__ ) , mid + 1 , A__ , A__ )
lowercase__: List[Any] = max(
self.segment_tree[self.left(A__ )] , self.segment_tree[self.right(A__ )] )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if self.flag[idx] is True:
lowercase__: Dict = self.lazy[idx]
lowercase__: str = False
if left_element != right_element:
lowercase__: Any = self.lazy[idx]
lowercase__: Dict = self.lazy[idx]
lowercase__: Dict = True
lowercase__: int = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
lowercase__: Optional[int] = val
if left_element != right_element:
lowercase__: Optional[int] = val
lowercase__: List[str] = val
lowercase__: Dict = True
lowercase__: Union[str, Any] = True
return True
lowercase__: List[Any] = (left_element + right_element) // 2
self.update(self.left(A__ ) , A__ , A__ , A__ , A__ , A__ )
self.update(self.right(A__ ) , mid + 1 , A__ , A__ , A__ , A__ )
lowercase__: Union[str, Any] = max(
self.segment_tree[self.left(A__ )] , self.segment_tree[self.right(A__ )] )
return True
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if self.flag[idx] is True:
lowercase__: Optional[Any] = self.lazy[idx]
lowercase__: Tuple = False
if left_element != right_element:
lowercase__: Any = self.lazy[idx]
lowercase__: Union[str, Any] = self.lazy[idx]
lowercase__: Dict = True
lowercase__: Dict = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
lowercase__: Optional[int] = (left_element + right_element) // 2
lowercase__: Optional[Any] = self.query(self.left(A__ ) , A__ , A__ , A__ , A__ )
lowercase__: Optional[int] = self.query(self.right(A__ ) , mid + 1 , A__ , A__ , A__ )
return max(A__ , A__ )
def __str__( self ):
return str([self.query(1 , 1 , self.size , A__ , A__ ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
__A = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
__A = 1_5
__A = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 586
|
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
A_ : Any = logging.get_logger(__name__)
class _a :
'''simple docstring'''
UpperCAmelCase__: str = None
@experimental
def UpperCamelCase (lowercase_: int , lowercase_: Any , lowercase_: str , lowercase_: str , lowercase_: List[str] , lowercase_: List[Any] , lowercase_: Union[str, Any] ) -> Union[str, Any]:
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
return _map_with_joblib(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
def UpperCamelCase (lowercase_: Tuple , lowercase_: List[Any] , lowercase_: Tuple , lowercase_: Tuple , lowercase_: List[str] , lowercase_: Dict , lowercase_: Tuple ) -> Tuple:
A__ : Union[str, Any] = num_proc if num_proc <= len(lowercase_ ) else len(lowercase_ )
A__ : str = [] # We organize the splits ourselve (contiguous splits)
for index in range(lowercase_ ):
A__ : Tuple = len(lowercase_ ) // num_proc
A__ : Tuple = len(lowercase_ ) % num_proc
A__ : Optional[Any] = div * index + min(lowercase_ , lowercase_ )
A__ : Union[str, Any] = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(lowercase_ ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
f"""Error dividing inputs iterable among processes. """
f"""Total number of objects {len(lowercase_ )}, """
f"""length: {sum(len(i[1] ) for i in split_kwds )}""" )
logger.info(
f"""Spawning {num_proc} processes for {len(lowercase_ )} objects in slices of {[len(i[1] ) for i in split_kwds]}""" )
A__ , A__ : Optional[int] = None, None
if not disable_tqdm:
A__ , A__ : List[str] = (RLock(),), tqdm.set_lock
with Pool(lowercase_ , initargs=lowercase_ , initializer=lowercase_ ) as pool:
A__ : Optional[Any] = pool.map(lowercase_ , lowercase_ )
logger.info(f"""Finished {num_proc} processes""" )
A__ : Tuple = [obj for proc_res in mapped for obj in proc_res]
logger.info(f"""Unpacked {len(lowercase_ )} objects""" )
return mapped
def UpperCamelCase (lowercase_: Union[str, Any] , lowercase_: Dict , lowercase_: str , lowercase_: Union[str, Any] , lowercase_: List[Any] , lowercase_: Dict , lowercase_: Optional[Any] ) -> Union[str, Any]:
# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
# and it requires monkey-patching joblib internal classes which is subject to change
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=lowercase_ ):
return joblib.Parallel()(
joblib.delayed(lowercase_ )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def UpperCamelCase (lowercase_: str ) -> str:
A__ : int = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
A__ : Optional[Any] = None
| 456
| 0
|
from __future__ import annotations
def lowerCamelCase_ ( _a : list[list[int]] ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = len(_a )
# We need to create solution object to save path.
UpperCAmelCase_ : List[Any] = [[0 for _ in range(_a )] for _ in range(_a )]
UpperCAmelCase_ : List[Any] = run_maze(_a , 0 , 0 , _a )
if solved:
print("""\n""".join(str(_a ) for row in solutions ) )
else:
print("""No solution exists!""" )
return solved
def lowerCamelCase_ ( _a : list[list[int]] , _a : int , _a : int , _a : list[list[int]] ):
'''simple docstring'''
UpperCAmelCase_ : str = len(_a )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase_ : Tuple = 1
return True
UpperCAmelCase_ : List[str] = (not i < 0) and (not j < 0) # Check lower bounds
UpperCAmelCase_ : List[Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
UpperCAmelCase_ : str = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase_ : List[str] = 1
# check for directions
if (
run_maze(_a , i + 1 , _a , _a )
or run_maze(_a , _a , j + 1 , _a )
or run_maze(_a , i - 1 , _a , _a )
or run_maze(_a , _a , j - 1 , _a )
):
return True
UpperCAmelCase_ : Optional[Any] = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 701
|
from __future__ import annotations
from typing import Any
class _snake_case :
'''simple docstring'''
def __init__( self: Optional[int] ,lowerCamelCase_: int = 6 ) -> None:
UpperCAmelCase_ : Node | None = None
UpperCAmelCase_ : Node | None = None
self.create_linked_list(lowerCamelCase_ )
def A__ ( self: Optional[int] ,lowerCamelCase_: int ) -> None:
UpperCAmelCase_ : List[Any] = Node()
UpperCAmelCase_ : List[str] = current_node
UpperCAmelCase_ : List[Any] = current_node
UpperCAmelCase_ : Any = current_node
for _ in range(1 ,lowerCamelCase_ ):
UpperCAmelCase_ : Optional[int] = Node()
UpperCAmelCase_ : Optional[Any] = current_node
UpperCAmelCase_ : List[str] = previous_node
UpperCAmelCase_ : str = current_node
UpperCAmelCase_ : Dict = self.front
UpperCAmelCase_ : List[Any] = previous_node
def A__ ( self: Any ) -> bool:
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def A__ ( self: List[str] ) -> Any | None:
self.check_can_perform_operation()
return self.front.data if self.front else None
def A__ ( self: Tuple ,lowerCamelCase_: Any ) -> None:
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
UpperCAmelCase_ : str = self.rear.next
if self.rear:
UpperCAmelCase_ : int = data
def A__ ( self: Optional[int] ) -> Any:
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
UpperCAmelCase_ : Union[str, Any] = self.front.data
UpperCAmelCase_ : Dict = None
return data
UpperCAmelCase_ : Union[str, Any] = self.front
UpperCAmelCase_ : Optional[int] = old_front.next
UpperCAmelCase_ : Union[str, Any] = old_front.data
UpperCAmelCase_ : Optional[Any] = None
return data
def A__ ( self: str ) -> None:
if self.is_empty():
raise Exception("""Empty Queue""" )
def A__ ( self: int ) -> None:
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""" )
class _snake_case :
'''simple docstring'''
def __init__( self: Tuple ) -> None:
UpperCAmelCase_ : Any | None = None
UpperCAmelCase_ : Node | None = None
UpperCAmelCase_ : Node | None = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322
| 0
|
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __lowerCAmelCase (lowercase_ , lowercase_ , lowercase_ ):
'''simple docstring'''
@register_to_config
def __init__(self : List[str] , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : float , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : str , UpperCamelCase : bool = False , ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.Embedding(UpperCamelCase , UpperCamelCase )
lowercase__ = nn.Embedding(UpperCamelCase , UpperCamelCase )
lowercase__ = False
lowercase__ = nn.Dropout(p=UpperCamelCase )
lowercase__ = TaConfig(
vocab_size=UpperCamelCase , d_model=UpperCamelCase , num_heads=UpperCamelCase , d_kv=UpperCamelCase , d_ff=UpperCamelCase , dropout_rate=UpperCamelCase , feed_forward_proj=UpperCamelCase , is_decoder=UpperCamelCase , is_encoder_decoder=UpperCamelCase , )
lowercase__ = nn.ModuleList()
for lyr_num in range(UpperCamelCase ):
lowercase__ = TaBlock(UpperCamelCase )
self.encoders.append(UpperCamelCase )
lowercase__ = TaLayerNorm(UpperCamelCase )
lowercase__ = nn.Dropout(p=UpperCamelCase )
def UpperCamelCase__ (self : Dict , UpperCamelCase : List[Any] , UpperCamelCase : Dict ):
'''simple docstring'''
lowercase__ = self.token_embedder(UpperCamelCase )
lowercase__ = encoder_input_tokens.shape[1]
lowercase__ = torch.arange(UpperCamelCase , device=encoder_input_tokens.device )
x += self.position_encoding(UpperCamelCase )
lowercase__ = self.dropout_pre(UpperCamelCase )
# inverted the attention mask
lowercase__ = encoder_input_tokens.size()
lowercase__ = self.get_extended_attention_mask(UpperCamelCase , UpperCamelCase )
for lyr in self.encoders:
lowercase__ = lyr(UpperCamelCase , UpperCamelCase )[0]
lowercase__ = self.layer_norm(UpperCamelCase )
return self.dropout_post(UpperCamelCase ), encoder_inputs_mask
| 460
|
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A = 1_000 ) -> int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 460
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ : Optional[int] = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 714
|
"""simple docstring"""
import numpy as np
def UpperCAmelCase__ ( A__ ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 274
| 0
|
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A_ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ = 16_000 ):
lowerCamelCase_ = int(round(sample_rate * max_length ) )
if len(lowerCAmelCase__ ) <= sample_length:
return wav
lowerCamelCase_ = randint(0 ,len(lowerCAmelCase__ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __lowerCamelCase :
a__: Optional[str] = field(default=lowerCAmelCase , metadata={'help': 'Name of a dataset from the datasets package'} )
a__: Optional[str] = field(
default=lowerCAmelCase , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
a__: Optional[str] = field(
default=lowerCAmelCase , metadata={'help': 'A file containing the training audio paths and labels.'} )
a__: Optional[str] = field(
default=lowerCAmelCase , metadata={'help': 'A file containing the validation audio paths and labels.'} )
a__: str = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
a__: str = field(
default='validation' , metadata={
'help': (
'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
a__: str = field(
default='audio' , metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''} , )
a__: str = field(
default='label' , metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''} )
a__: Optional[int] = field(
default=lowerCAmelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
a__: Optional[int] = field(
default=lowerCAmelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
a__: float = field(
default=20 , metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'} , )
@dataclass
class __lowerCamelCase :
a__: str = field(
default='facebook/wav2vec2-base' , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} , )
a__: Optional[str] = field(
default=lowerCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
a__: Optional[str] = field(
default=lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'} )
a__: str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
a__: Optional[str] = field(
default=lowerCAmelCase , metadata={'help': 'Name or path of preprocessor config.'} )
a__: bool = field(
default=lowerCAmelCase , metadata={'help': 'Whether to freeze the feature encoder layers of the model.'} )
a__: bool = field(
default=lowerCAmelCase , metadata={'help': 'Whether to generate an attention mask in the feature extractor.'} )
a__: bool = field(
default=lowerCAmelCase , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
a__: Optional[bool] = field(
default=lowerCAmelCase , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
a__: bool = field(
default=lowerCAmelCase , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def UpperCAmelCase__ ( self ):
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , UpperCAmelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowercase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCamelCase_ = 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.
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_audio_classification''' ,lowerCAmelCase__ ,lowerCAmelCase__ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,datefmt='''%m/%d/%Y %H:%M:%S''' ,handlers=[logging.StreamHandler(sys.stdout )] ,)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCamelCase_ = training_args.get_process_log_level()
logger.setLevel(lowerCAmelCase__ )
transformers.utils.logging.set_verbosity(lowerCAmelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} "
+ f"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(f"Training/evaluation parameters {training_args}" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
lowerCamelCase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCamelCase_ = 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 train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset and prepare it for the audio classification task.
lowerCamelCase_ = DatasetDict()
lowerCamelCase_ = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=data_args.train_split_name ,use_auth_token=True if model_args.use_auth_token else None ,)
lowerCamelCase_ = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=data_args.eval_split_name ,use_auth_token=True if model_args.use_auth_token else None ,)
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f"{', '.join(raw_datasets['train'].column_names )}." )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f"{', '.join(raw_datasets['train'].column_names )}." )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
lowerCamelCase_ = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path ,return_attention_mask=model_args.attention_mask ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
lowerCamelCase_ = raw_datasets.cast_column(
data_args.audio_column_name ,datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
lowerCamelCase_ = feature_extractor.model_input_names[0]
def train_transforms(lowerCAmelCase__ ):
lowerCamelCase_ = []
for audio in batch[data_args.audio_column_name]:
lowerCamelCase_ = random_subsample(
audio['''array'''] ,max_length=data_args.max_length_seconds ,sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(lowerCAmelCase__ )
lowerCamelCase_ = feature_extractor(lowerCAmelCase__ ,sampling_rate=feature_extractor.sampling_rate )
lowerCamelCase_ = {model_input_name: inputs.get(lowerCAmelCase__ )}
lowerCamelCase_ = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(lowerCAmelCase__ ):
lowerCamelCase_ = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
lowerCamelCase_ = feature_extractor(lowerCAmelCase__ ,sampling_rate=feature_extractor.sampling_rate )
lowerCamelCase_ = {model_input_name: inputs.get(lowerCAmelCase__ )}
lowerCamelCase_ = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowerCamelCase_ = raw_datasets['''train'''].features[data_args.label_column_name].names
lowerCamelCase_ , lowerCamelCase_ = {}, {}
for i, label in enumerate(lowerCAmelCase__ ):
lowerCamelCase_ = str(lowerCAmelCase__ )
lowerCamelCase_ = label
# Load the accuracy metric from the datasets package
lowerCamelCase_ = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(lowerCAmelCase__ ):
lowerCamelCase_ = np.argmax(eval_pred.predictions ,axis=1 )
return metric.compute(predictions=lowerCAmelCase__ ,references=eval_pred.label_ids )
lowerCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path ,num_labels=len(lowerCAmelCase__ ) ,labelaid=lowerCAmelCase__ ,idalabel=lowerCAmelCase__ ,finetuning_task='''audio-classification''' ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
lowerCamelCase_ = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path ,from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) ,config=lowerCAmelCase__ ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,)
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
lowerCamelCase_ = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(lowerCAmelCase__ ,output_all_columns=lowerCAmelCase__ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowerCamelCase_ = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(lowerCAmelCase__ ,output_all_columns=lowerCAmelCase__ )
# Initialize our trainer
lowerCamelCase_ = Trainer(
model=lowerCAmelCase__ ,args=lowerCAmelCase__ ,train_dataset=raw_datasets['''train'''] if training_args.do_train else None ,eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None ,compute_metrics=lowerCAmelCase__ ,tokenizer=lowerCAmelCase__ ,)
# Training
if training_args.do_train:
lowerCamelCase_ = None
if training_args.resume_from_checkpoint is not None:
lowerCamelCase_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCamelCase_ = last_checkpoint
lowerCamelCase_ = trainer.train(resume_from_checkpoint=lowerCAmelCase__ )
trainer.save_model()
trainer.log_metrics('''train''' ,train_result.metrics )
trainer.save_metrics('''train''' ,train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowerCamelCase_ = trainer.evaluate()
trainer.log_metrics('''eval''' ,lowerCAmelCase__ )
trainer.save_metrics('''eval''' ,lowerCAmelCase__ )
# Write model card and (optionally) push to hub
lowerCamelCase_ = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCAmelCase__ )
else:
trainer.create_model_card(**lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 29
|
'''simple docstring'''
import socket
def lowerCAmelCase__ ( ):
_A : Dict = socket.socket(socket.AF_INET ,socket.SOCK_STREAM )
_A : List[Any] = socket.gethostname()
_A : List[str] = 12312
sock.connect((host, port) )
sock.send(b'Hello server!' )
with open('Received_file' ,'wb' ) as out_file:
print('File opened' )
print('Receiving data...' )
while True:
_A : Optional[int] = sock.recv(1024 )
if not data:
break
out_file.write(lowerCamelCase )
print('Successfully received the file' )
sock.close()
print('Connection closed' )
if __name__ == "__main__":
main()
| 128
| 0
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class _lowercase ( unittest.TestCase ):
def __init__( self , A__ , A__=7 , A__=3 , A__=18 , A__=30 , A__=4_00 , A__=True , A__=None , A__=True , A__=None , A__=True , ) -> Dict:
snake_case = size if size is not None else {'''shortest_edge''': 20}
snake_case = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case = parent
snake_case = batch_size
snake_case = num_channels
snake_case = image_size
snake_case = min_resolution
snake_case = max_resolution
snake_case = do_resize
snake_case = size
snake_case = do_center_crop
snake_case = crop_size
snake_case = do_flip_channel_order
def UpperCamelCase ( self ) -> Tuple:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class _lowercase ( __a , unittest.TestCase ):
_UpperCAmelCase = MobileViTImageProcessor if is_vision_available() else None
def UpperCamelCase ( self ) -> str:
snake_case = MobileViTImageProcessingTester(self )
@property
def UpperCamelCase ( self ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self ) -> Dict:
snake_case = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A__ , '''do_resize''' ) )
self.assertTrue(hasattr(A__ , '''size''' ) )
self.assertTrue(hasattr(A__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(A__ , '''center_crop''' ) )
self.assertTrue(hasattr(A__ , '''do_flip_channel_order''' ) )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
snake_case = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def UpperCamelCase ( self ) -> Any:
pass
def UpperCamelCase ( self ) -> Dict:
# Initialize image_processing
snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , Image.Image )
# Test not batched input
snake_case = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case = image_processing(A__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def UpperCamelCase ( self ) -> Dict:
# Initialize image_processing
snake_case = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , numpify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , np.ndarray )
# Test not batched input
snake_case = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case = image_processing(A__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def UpperCamelCase ( self ) -> str:
# Initialize image_processing
snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , torchify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , torch.Tensor )
# Test not batched input
snake_case = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case = image_processing(A__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 44
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
def __init__( self , A__ , A__=13 , A__=30 , A__=2 , A__=3 , A__=True , A__=True , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=10 , A__=0.0_2 , A__=3 , A__=None , ) -> List[Any]:
snake_case = parent
snake_case = batch_size
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = is_training
snake_case = use_labels
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case = (image_size // patch_size) ** 2
snake_case = num_patches + 1
def UpperCamelCase ( self ) -> int:
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> int:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = TFViTModel(config=A__ )
snake_case = model(A__ , training=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
snake_case = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[int]:
snake_case = self.type_sequence_label_size
snake_case = TFViTForImageClassification(A__ )
snake_case = model(A__ , labels=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case = 1
snake_case = TFViTForImageClassification(A__ )
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case = config_and_inputs
snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> List[Any]:
snake_case = TFViTModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> str:
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self ) -> List[Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
snake_case = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case = [*signature.parameters.keys()]
snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = TFViTModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(A__ )
def __UpperCamelCase ( ) ->Any:
snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' )
snake_case = self.default_image_processor
snake_case = prepare_img()
snake_case = image_processor(images=A__ , return_tensors='''tf''' )
# forward pass
snake_case = model(**A__ )
# verify the logits
snake_case = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
snake_case = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , A__ , atol=1e-4 )
| 44
| 1
|
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __snake_case ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def __UpperCamelCase ( self ):
return datasets.DatasetInfo(
features=datasets.Features({"""content""": datasets.Value("""string""" )} ) , supervised_keys=__lowerCamelCase , )
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_dummy_examples()} )]
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__lowerCamelCase )
class __snake_case ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def __UpperCamelCase ( self ):
return datasets.DatasetInfo(
features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) , supervised_keys=__lowerCamelCase , )
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_nested_examples()} )
]
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__lowerCamelCase )
def UpperCamelCase__ ( ) -> Any:
'''simple docstring'''
return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
def UpperCamelCase__ ( ) -> List[str]:
'''simple docstring'''
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@require_beam
def __UpperCamelCase ( self ):
snake_case__ : List[str] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case__ : List[str] = DummyBeamDataset(cache_dir=__lowerCamelCase , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__lowerCamelCase , builder.name , """default""" , """0.0.0""" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
snake_case__ : List[Any] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , __lowerCamelCase )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , __lowerCamelCase )
self.assertDictEqual(dset["""train"""][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__lowerCamelCase , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def __UpperCamelCase ( self ):
import apache_beam as beam
snake_case__ : str = beam.io.parquetio.WriteToParquet
snake_case__ : int = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case__ : Union[str, Any] = DummyBeamDataset(cache_dir=__lowerCamelCase , beam_runner="""DirectRunner""" )
with patch("""apache_beam.io.parquetio.WriteToParquet""" ) as write_parquet_mock:
snake_case__ : Union[str, Any] = partial(__lowerCamelCase , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
__lowerCamelCase , builder.name , """default""" , """0.0.0""" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
__lowerCamelCase , builder.name , """default""" , """0.0.0""" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
snake_case__ : Any = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , __lowerCamelCase )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , __lowerCamelCase )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["""train"""]["""content"""] ) , sorted(["""foo""", """bar""", """foobar"""] ) )
self.assertTrue(
os.path.exists(os.path.join(__lowerCamelCase , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def __UpperCamelCase ( self ):
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case__ : str = DummyBeamDataset(cache_dir=__lowerCamelCase )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def __UpperCamelCase ( self ):
snake_case__ : Any = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case__ : int = NestedBeamDataset(cache_dir=__lowerCamelCase , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__lowerCamelCase , builder.name , """default""" , """0.0.0""" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) )
snake_case__ : List[str] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , __lowerCamelCase )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , __lowerCamelCase )
self.assertDictEqual(dset["""train"""][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__lowerCamelCase , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
| 38
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = '▁'
lowerCAmelCase__ = {'vocab_file': 'sentencepiece.bpe.model'}
lowerCAmelCase__ = {
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
}
}
lowerCAmelCase__ = {
'facebook/mbart-large-en-ro': 10_24,
'facebook/mbart-large-cc25': 10_24,
}
# fmt: off
lowerCAmelCase__ = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class lowerCAmelCase__ ( a):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask"]
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
def __init__( self , __lowerCamelCase , __lowerCamelCase="<s>" , __lowerCamelCase="</s>" , __lowerCamelCase="</s>" , __lowerCamelCase="<s>" , __lowerCamelCase="<unk>" , __lowerCamelCase="<pad>" , __lowerCamelCase="<mask>" , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase = None , __lowerCamelCase=None , **__lowerCamelCase , ) -> int:
# Mask token behave like a normal word, i.e. include the space before it
_A : Union[str, Any] = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase , __lowerCamelCase) else mask_token
_A : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , tokenizer_file=__lowerCamelCase , src_lang=__lowerCamelCase , tgt_lang=__lowerCamelCase , additional_special_tokens=__lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCamelCase , )
_A : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(__lowerCamelCase))
_A : str = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
_A : List[Any] = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_A : Optional[int] = 1
_A : Dict = len(self.sp_model)
_A : List[str] = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__lowerCamelCase)
}
_A : Optional[int] = {v: k for k, v in self.lang_code_to_id.items()}
_A : List[str] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
_A : Tuple = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_A : int = list(self.lang_code_to_id.keys())
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens])
_A : Tuple = src_lang if src_lang is not None else "en_XX"
_A : List[Any] = self.lang_code_to_id[self._src_lang]
_A : Optional[Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
def __getstate__( self) -> List[Any]:
_A : Optional[Any] = self.__dict__.copy()
_A : List[str] = None
_A : List[str] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , __lowerCamelCase) -> Dict:
_A : int = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
_A : Optional[Any] = {}
_A : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
@property
def _lowerCamelCase ( self) -> Tuple:
return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def _lowerCamelCase ( self) -> str:
return self._src_lang
@src_lang.setter
def _lowerCamelCase ( self , __lowerCamelCase) -> None:
_A : List[Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase = None , __lowerCamelCase = False) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCamelCase , token_ids_a=__lowerCamelCase , already_has_special_tokens=__lowerCamelCase)
_A : Optional[int] = [1] * len(self.prefix_tokens)
_A : Tuple = [1] * len(self.suffix_tokens)
if token_ids_a is None:
return prefix_ones + ([0] * len(__lowerCamelCase)) + suffix_ones
return prefix_ones + ([0] * len(__lowerCamelCase)) + ([0] * len(__lowerCamelCase)) + suffix_ones
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase = None) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase = None) -> List[int]:
_A : Optional[int] = [self.sep_token_id]
_A : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase) -> Optional[int]:
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
_A : int = src_lang
_A : Any = self(__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase)
_A : Any = self.convert_tokens_to_ids(__lowerCamelCase)
_A : int = tgt_lang_id
return inputs
def _lowerCamelCase ( self) -> Dict:
_A : int = {self.convert_ids_to_tokens(__lowerCamelCase): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _lowerCamelCase ( self , __lowerCamelCase) -> List[str]:
return self.sp_model.encode(__lowerCamelCase , out_type=__lowerCamelCase)
def _lowerCamelCase ( self , __lowerCamelCase) -> Optional[Any]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_A : List[str] = self.sp_model.PieceToId(__lowerCamelCase)
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _lowerCamelCase ( self , __lowerCamelCase) -> Dict:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
def _lowerCamelCase ( self , __lowerCamelCase) -> Optional[Any]:
_A : Optional[Any] = "".join(__lowerCamelCase).replace(__lowerCamelCase , " ").strip()
return out_string
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase = None) -> Tuple[str]:
if not os.path.isdir(__lowerCamelCase):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
_A : Union[str, Any] = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCamelCase) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , __lowerCamelCase)
elif not os.path.isfile(self.vocab_file):
with open(__lowerCamelCase , "wb") as fi:
_A : List[Any] = self.sp_model.serialized_model_proto()
fi.write(__lowerCamelCase)
return (out_vocab_file,)
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase = "en_XX" , __lowerCamelCase = None , __lowerCamelCase = "ro_RO" , **__lowerCamelCase , ) -> BatchEncoding:
_A : Optional[int] = src_lang
_A : Optional[Any] = tgt_lang
return super().prepare_seqaseq_batch(__lowerCamelCase , __lowerCamelCase , **__lowerCamelCase)
def _lowerCamelCase ( self) -> int:
return self.set_src_lang_special_tokens(self.src_lang)
def _lowerCamelCase ( self) -> int:
return self.set_tgt_lang_special_tokens(self.tgt_lang)
def _lowerCamelCase ( self , __lowerCamelCase) -> None:
_A : str = self.lang_code_to_id[src_lang]
_A : Any = []
_A : Dict = [self.eos_token_id, self.cur_lang_code]
def _lowerCamelCase ( self , __lowerCamelCase) -> None:
_A : Any = self.lang_code_to_id[lang]
_A : str = []
_A : Union[str, Any] = [self.eos_token_id, self.cur_lang_code]
| 503
| 0
|
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
from transformers.models.mobilevitva.modeling_mobilevitva import (
MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
make_divisible,
)
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class _A( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
__A : List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_A , 'width_multiplier' ) )
class _A:
"""simple docstring"""
def __init__( self , _A , _A=13 , _A=64 , _A=2 , _A=3 , _A="swish" , _A=3 , _A=32 , _A=0.1 , _A=0.0_2 , _A=True , _A=True , _A=10 , _A=None , _A=0.2_5 , _A=0.0 , _A=0.0 , ):
__A : Optional[Any] = parent
__A : Dict = batch_size
__A : Union[str, Any] = image_size
__A : Dict = patch_size
__A : Tuple = num_channels
__A : List[Any] = make_divisible(512 * width_multiplier , divisor=8 )
__A : List[str] = hidden_act
__A : Union[str, Any] = conv_kernel_size
__A : Union[str, Any] = output_stride
__A : Union[str, Any] = classifier_dropout_prob
__A : str = use_labels
__A : Optional[int] = is_training
__A : Any = num_labels
__A : Any = initializer_range
__A : Tuple = scope
__A : Optional[Any] = width_multiplier
__A : str = ffn_dropout
__A : Dict = attn_dropout
def UpperCAmelCase_ ( self ):
__A : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A : Union[str, Any] = None
__A : List[str] = None
if self.use_labels:
__A : List[str] = ids_tensor([self.batch_size] , self.num_labels )
__A : Any = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__A : Any = self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCAmelCase_ ( self ):
return MobileViTVaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , width_multiplier=self.width_multiplier , ffn_dropout=self.ffn_dropout_prob , attn_dropout=self.attn_dropout_prob , )
def UpperCAmelCase_ ( self , _A , _A , _A , _A ):
__A : Union[str, Any] = MobileViTVaModel(config=_A )
model.to(_A )
model.eval()
__A : Any = model(_A )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def UpperCAmelCase_ ( self , _A , _A , _A , _A ):
__A : List[Any] = self.num_labels
__A : Union[str, Any] = MobileViTVaForImageClassification(_A )
model.to(_A )
model.eval()
__A : List[str] = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self , _A , _A , _A , _A ):
__A : Dict = self.num_labels
__A : Dict = MobileViTVaForSemanticSegmentation(_A )
model.to(_A )
model.eval()
__A : Tuple = model(_A )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
__A : Optional[int] = model(_A , labels=_A )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def UpperCAmelCase_ ( self ):
__A : Optional[Any] = self.prepare_config_and_inputs()
__A , __A , __A , __A : Optional[int] = config_and_inputs
__A : List[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class _A( snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : int = (
(MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase : int = (
{
'''feature-extraction''': MobileViTVaModel,
'''image-classification''': MobileViTVaForImageClassification,
'''image-segmentation''': MobileViTVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase : int = False
UpperCamelCase : Dict = False
UpperCamelCase : Tuple = False
UpperCamelCase : str = False
def UpperCAmelCase_ ( self ):
__A : Optional[int] = MobileViTVaModelTester(self )
__A : int = MobileViTVaConfigTester(self , config_class=_A , has_text_modality=_A )
def UpperCAmelCase_ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViTV2 does not use inputs_embeds' )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip(reason='MobileViTV2 does not support input and output embeddings' )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip(reason='MobileViTV2 does not output attentions' )
def UpperCAmelCase_ ( self ):
pass
@require_torch_multi_gpu
@unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.' )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCAmelCase_ ( self ):
pass
def UpperCAmelCase_ ( self ):
__A , __A : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : Optional[int] = model_class(_A )
__A : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A : Tuple = [*signature.parameters.keys()]
__A : Tuple = ['pixel_values']
self.assertListEqual(arg_names[:1] , _A )
def UpperCAmelCase_ ( self ):
__A : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCAmelCase_ ( self ):
def check_hidden_states_output(_A , _A , _A ):
__A : Optional[int] = model_class(_A )
model.to(_A )
model.eval()
with torch.no_grad():
__A : Dict = model(**self._prepare_for_class(_A , _A ) )
__A : Optional[int] = outputs.hidden_states
__A : Any = 5
self.assertEqual(len(_A ) , _A )
# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
__A : Optional[Any] = 2
for i in range(len(_A ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
__A , __A : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : List[str] = True
check_hidden_states_output(_A , _A , _A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A : Optional[Any] = True
check_hidden_states_output(_A , _A , _A )
def UpperCAmelCase_ ( self ):
__A : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
def UpperCAmelCase_ ( self ):
__A : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_A )
@slow
def UpperCAmelCase_ ( self ):
for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A : List[str] = MobileViTVaModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def _SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
__A : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class _A( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self ):
return (
MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256' )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self ):
__A : Dict = MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256' ).to(
_A )
__A : Tuple = self.default_image_processor
__A : Union[str, Any] = prepare_img()
__A : int = image_processor(images=_A , return_tensors='pt' ).to(_A )
# forward pass
with torch.no_grad():
__A : str = model(**_A )
# verify the logits
__A : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _A )
__A : Dict = torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1e-4 ) )
@slow
def UpperCAmelCase_ ( self ):
__A : List[str] = MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3' )
__A : Union[str, Any] = model.to(_A )
__A : int = MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3' )
__A : Union[str, Any] = prepare_img()
__A : Dict = image_processor(images=_A , return_tensors='pt' ).to(_A )
# forward pass
with torch.no_grad():
__A : int = model(**_A )
__A : Optional[int] = outputs.logits
# verify the logits
__A : List[str] = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , _A )
__A : Dict = torch.tensor(
[
[[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
[[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
[[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
] , device=_A , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , _A , atol=1e-4 ) )
@slow
def UpperCAmelCase_ ( self ):
__A : Tuple = MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3' )
__A : str = model.to(_A )
__A : Union[str, Any] = MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3' )
__A : Union[str, Any] = prepare_img()
__A : List[Any] = image_processor(images=_A , return_tensors='pt' ).to(_A )
# forward pass
with torch.no_grad():
__A : str = model(**_A )
__A : Dict = outputs.logits.detach().cpu()
__A : Optional[int] = image_processor.post_process_semantic_segmentation(outputs=_A , target_sizes=[(50, 60)] )
__A : Dict = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , _A )
__A : List[str] = image_processor.post_process_semantic_segmentation(outputs=_A )
__A : str = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , _A )
| 77
|
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase : str = HfApi()
UpperCAmelCase : List[str] = {}
# fmt: off
UpperCAmelCase : Optional[Any] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
UpperCAmelCase : Dict = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
UpperCAmelCase : Union[str, Any] = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
UpperCAmelCase : str = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
UpperCAmelCase : Optional[Any] = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
UpperCAmelCase : List[Any] = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
UpperCAmelCase : Optional[int] = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
UpperCAmelCase : Tuple = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
UpperCAmelCase : Any = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
UpperCAmelCase : Union[str, Any] = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
UpperCAmelCase : Tuple = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
UpperCAmelCase : Dict = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
UpperCAmelCase : Tuple = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
UpperCAmelCase : List[str] = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
UpperCAmelCase : Union[str, Any] = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
UpperCAmelCase : Any = api.list_models(filter='''diffusers''')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
UpperCAmelCase : Union[str, Any] = '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]
print(F"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith('''CompVis'''):
UpperCAmelCase : List[str] = UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
else:
UpperCAmelCase : List[str] = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
UpperCAmelCase : int = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
UpperCAmelCase : Optional[int] = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
UpperCAmelCase : Any = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
)
print(F"""{mod.modelId} has passed successfully!!!""")
| 77
| 1
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(A ) , '''Tatoeba directory does not exist.''' )
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __snake_case( self ):
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
return TatoebaConverter(save_dir=A_ )
@slow
def __snake_case( self ):
self.resolver.convert_models(["""heb-eng"""] )
@slow
def __snake_case( self ):
_UpperCAmelCase,_UpperCAmelCase : Any = self.resolver.write_model_card("""opus-mt-he-en""" , dry_run=A_ )
assert mmeta["long_pair"] == "heb-eng"
| 643
|
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : Tuple = _max - _min + 1
_UpperCAmelCase,_UpperCAmelCase : Union[str, Any] = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
_UpperCAmelCase : Optional[int] = i - _min
_UpperCAmelCase : Any = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
_UpperCAmelCase : List[Any] = 0
for i in range(snake_case__ ):
while holes_repeat[i] > 0:
_UpperCAmelCase : Optional[Any] = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE__ : Dict = input('Enter numbers separated by comma:\n')
SCREAMING_SNAKE_CASE__ : Optional[int] = [int(x) for x in user_input.split(',')]
print(pigeon_sort(unsorted))
| 643
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineLayer",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 384
|
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase = 1 ,__UpperCamelCase = 10_00 ) -> int:
lowerCamelCase_ = 1
lowerCamelCase_ = 0
for divide_by_number in range(__UpperCamelCase ,digit + 1 ):
lowerCamelCase_ = []
lowerCamelCase_ = numerator
for _ in range(1 ,digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(__UpperCamelCase ):
lowerCamelCase_ = len(__UpperCamelCase )
lowerCamelCase_ = divide_by_number
else:
has_been_divided.append(__UpperCamelCase )
lowerCamelCase_ = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 384
| 1
|
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __A ( SCREAMING_SNAKE_CASE_ ):
# to overwrite at feature extractactor specific tests
UpperCAmelCase__ = None
UpperCAmelCase__ = None
@property
def lowerCamelCase__ ( self : Dict ) -> Any:
return self.feat_extract_tester.prepare_feat_extract_dict()
def lowerCamelCase__ ( self : List[Any] ) -> Any:
__magic_name__: Dict = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(__snake_case , """feature_size""" ) )
self.assertTrue(hasattr(__snake_case , """sampling_rate""" ) )
self.assertTrue(hasattr(__snake_case , """padding_value""" ) )
def lowerCamelCase__ ( self : List[Any] ) -> List[Any]:
__magic_name__: List[Any] = self.feat_extract_tester.prepare_inputs_for_common()
__magic_name__: List[str] = self.feature_extraction_class(**self.feat_extract_dict )
__magic_name__: str = feat_extract.model_input_names[0]
__magic_name__: Any = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(__snake_case ) == len(__snake_case ) for x, y in zip(__snake_case , processed_features[input_name] ) ) )
__magic_name__: List[Any] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case )
__magic_name__: str = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
__magic_name__: List[str] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__magic_name__: Optional[int] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def lowerCamelCase__ ( self : Tuple ) -> List[Any]:
__magic_name__: Tuple = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case )
__magic_name__: Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
__magic_name__: Dict = feat_extract.model_input_names[0]
__magic_name__: Optional[int] = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
__magic_name__: Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__magic_name__: int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def lowerCamelCase__ ( self : Optional[int] ) -> Optional[int]:
__magic_name__: Dict = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case )
__magic_name__: Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
__magic_name__: Optional[Any] = feat_extract.model_input_names[0]
__magic_name__: Tuple = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
__magic_name__: Optional[int] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__magic_name__: Any = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def lowerCamelCase__ ( self : Optional[Any] , __snake_case : Optional[Any]=False ) -> Any:
def _inputs_have_equal_length(__snake_case : int ):
__magic_name__: Union[str, Any] = len(input[0] )
for input_slice in input[1:]:
if len(__snake_case ) != length:
return False
return True
def _inputs_are_equal(__snake_case : Tuple , __snake_case : List[str] ):
if len(__snake_case ) != len(__snake_case ):
return False
for input_slice_a, input_slice_a in zip(__snake_case , __snake_case ):
if not np.allclose(np.asarray(__snake_case ) , np.asarray(__snake_case ) , atol=1E-3 ):
return False
return True
__magic_name__: str = self.feature_extraction_class(**self.feat_extract_dict )
__magic_name__: Any = self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case )
__magic_name__: Tuple = feat_extract.model_input_names[0]
__magic_name__: Tuple = BatchFeature({input_name: speech_inputs} )
__magic_name__: List[str] = self.feat_extract_tester.seq_length_diff
__magic_name__: Optional[Any] = self.feat_extract_tester.max_seq_length + pad_diff
__magic_name__: Tuple = self.feat_extract_tester.min_seq_length
__magic_name__: List[str] = self.feat_extract_tester.batch_size
__magic_name__: int = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__magic_name__: Union[str, Any] = feat_extract.pad(__snake_case , padding=__snake_case )
__magic_name__: Optional[Any] = input_a[input_name]
__magic_name__: str = feat_extract.pad(__snake_case , padding="""longest""" )
__magic_name__: str = input_a[input_name]
__magic_name__: Optional[int] = feat_extract.pad(__snake_case , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
__magic_name__: Any = input_a[input_name]
__magic_name__: str = feat_extract.pad(__snake_case , padding="""longest""" , return_tensors="""np""" )
__magic_name__: Any = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(__snake_case ):
feat_extract.pad(__snake_case , padding="""max_length""" )[input_name]
__magic_name__: Union[str, Any] = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=__snake_case , return_tensors="""np""" )
__magic_name__: Tuple = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(__snake_case ) )
self.assertTrue(_inputs_have_equal_length(__snake_case ) )
self.assertTrue(_inputs_have_equal_length(__snake_case ) )
self.assertTrue(_inputs_are_equal(__snake_case , __snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
__magic_name__: Optional[int] = feat_extract.pad(__snake_case , pad_to_multiple_of=1_0 )
__magic_name__: int = input_a[input_name]
__magic_name__: Tuple = feat_extract.pad(__snake_case , padding="""longest""" , pad_to_multiple_of=1_0 )
__magic_name__: List[Any] = input_a[input_name]
__magic_name__: Tuple = feat_extract.pad(
__snake_case , padding="""max_length""" , pad_to_multiple_of=1_0 , max_length=__snake_case )
__magic_name__: int = input_a[input_name]
__magic_name__: Tuple = feat_extract.pad(
__snake_case , padding="""max_length""" , pad_to_multiple_of=1_0 , max_length=__snake_case , return_tensors="""np""" , )
__magic_name__: List[Any] = input_a[input_name]
self.assertTrue(all(len(__snake_case ) % 1_0 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(__snake_case , __snake_case ) )
__magic_name__: Tuple = pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
self.assertTrue(all(len(__snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
__magic_name__: Optional[int] = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1E-3 )
def lowerCamelCase__ ( self : Any , __snake_case : Optional[int]=False ) -> Optional[Any]:
def _inputs_have_equal_length(__snake_case : Union[str, Any] ):
__magic_name__: Tuple = len(input[0] )
for input_slice in input[1:]:
if len(__snake_case ) != length:
return False
return True
def _inputs_are_equal(__snake_case : List[str] , __snake_case : Optional[int] ):
if len(__snake_case ) != len(__snake_case ):
return False
for input_slice_a, input_slice_a in zip(__snake_case , __snake_case ):
if not np.allclose(np.asarray(__snake_case ) , np.asarray(__snake_case ) , atol=1E-3 ):
return False
return True
__magic_name__: List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
__magic_name__: List[str] = self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case )
__magic_name__: str = feat_extract.model_input_names[0]
__magic_name__: Optional[int] = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
__magic_name__: Optional[int] = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=__snake_case )
__magic_name__: int = input_a[input_name]
__magic_name__: Optional[int] = feat_extract.pad(__snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
__magic_name__: Dict = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(__snake_case ) )
self.assertFalse(_inputs_have_equal_length(__snake_case ) )
# truncate to smallest with np
__magic_name__: Union[str, Any] = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=__snake_case , )
__magic_name__: Optional[Any] = input_a[input_name]
__magic_name__: Dict = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
__magic_name__: Optional[Any] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(__snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(__snake_case ) )
# truncate to middle
__magic_name__: List[str] = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=__snake_case , return_tensors="""np""" , )
__magic_name__: Tuple = input_a[input_name]
__magic_name__: Optional[int] = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=__snake_case )
__magic_name__: Any = input_a[input_name]
__magic_name__: Tuple = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
__magic_name__: Tuple = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(__snake_case ) )
self.assertTrue(_inputs_have_equal_length(__snake_case ) )
self.assertTrue(_inputs_are_equal(__snake_case , __snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(__snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__snake_case ):
feat_extract.pad(__snake_case , truncation=__snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__snake_case ):
feat_extract.pad(__snake_case , padding="""longest""" , truncation=__snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__snake_case ):
feat_extract.pad(__snake_case , padding="""longest""" , truncation=__snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(__snake_case ):
feat_extract.pad(__snake_case , padding="""max_length""" , truncation=__snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__magic_name__: Tuple = 1_2
__magic_name__: Optional[Any] = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=__snake_case , truncation=__snake_case , )
__magic_name__: Tuple = input_a[input_name]
__magic_name__: List[str] = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=__snake_case , )
__magic_name__: Optional[Any] = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__magic_name__: List[Any] = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
__magic_name__: List[Any] = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(__snake_case ) )
self.assertFalse(_inputs_have_equal_length(__snake_case ) )
def lowerCamelCase__ ( self : Tuple ) -> Optional[int]:
self._check_padding(numpify=__snake_case )
def lowerCamelCase__ ( self : Union[str, Any] ) -> Dict:
self._check_padding(numpify=__snake_case )
def lowerCamelCase__ ( self : Optional[int] ) -> int:
self._check_truncation(numpify=__snake_case )
def lowerCamelCase__ ( self : int ) -> int:
self._check_truncation(numpify=__snake_case )
@require_torch
def lowerCamelCase__ ( self : List[Any] ) -> List[Any]:
__magic_name__: List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
__magic_name__: Optional[Any] = self.feat_extract_tester.prepare_inputs_for_common()
__magic_name__: Optional[int] = feat_extract.model_input_names[0]
__magic_name__: Optional[Any] = BatchFeature({input_name: speech_inputs} )
__magic_name__: List[Any] = feat_extract.pad(__snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
__magic_name__: Any = feat_extract.pad(__snake_case , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
@require_tf
def lowerCamelCase__ ( self : Union[str, Any] ) -> int:
__magic_name__: Dict = self.feature_extraction_class(**self.feat_extract_dict )
__magic_name__: str = self.feat_extract_tester.prepare_inputs_for_common()
__magic_name__: Any = feat_extract.model_input_names[0]
__magic_name__: Union[str, Any] = BatchFeature({input_name: speech_inputs} )
__magic_name__: int = feat_extract.pad(__snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
__magic_name__: Tuple = feat_extract.pad(__snake_case , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def lowerCamelCase__ ( self : Union[str, Any] ) -> int:
__magic_name__: Optional[Any] = self.feat_extract_dict
__magic_name__: Optional[Any] = True
__magic_name__: List[str] = self.feature_extraction_class(**__snake_case )
__magic_name__: List[str] = self.feat_extract_tester.prepare_inputs_for_common()
__magic_name__: Union[str, Any] = [len(__snake_case ) for x in speech_inputs]
__magic_name__: int = feat_extract.model_input_names[0]
__magic_name__: List[str] = BatchFeature({input_name: speech_inputs} )
__magic_name__: Tuple = feat_extract.pad(__snake_case , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , __snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , __snake_case )
def lowerCamelCase__ ( self : Union[str, Any] ) -> Tuple:
__magic_name__: Any = self.feat_extract_dict
__magic_name__: Optional[Any] = True
__magic_name__: str = self.feature_extraction_class(**__snake_case )
__magic_name__: Optional[Any] = self.feat_extract_tester.prepare_inputs_for_common()
__magic_name__: str = [len(__snake_case ) for x in speech_inputs]
__magic_name__: Union[str, Any] = feat_extract.model_input_names[0]
__magic_name__: Optional[int] = BatchFeature({input_name: speech_inputs} )
__magic_name__: Union[str, Any] = min(__snake_case )
__magic_name__: Dict = feat_extract.pad(
__snake_case , padding="""max_length""" , max_length=__snake_case , truncation=__snake_case , return_tensors="""np""" )
self.assertIn("""attention_mask""" , __snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 96
|
"""simple docstring"""
def a ( __UpperCAmelCase : List[Any] ) -> str:
__magic_name__: Optional[int] = [0] * len(__UpperCAmelCase )
__magic_name__: str = []
__magic_name__: Any = []
__magic_name__: Union[str, Any] = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__UpperCAmelCase ) ):
if indegree[i] == 0:
queue.append(__UpperCAmelCase )
while queue:
__magic_name__: Optional[Any] = queue.pop(0 )
cnt += 1
topo.append(__UpperCAmelCase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(__UpperCAmelCase )
if cnt != len(__UpperCAmelCase ):
print("""Cycle exists""" )
else:
print(__UpperCAmelCase )
# Adjacency List of Graph
__lowerCamelCase = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 96
| 1
|
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_lowerCamelCase : Dict = logging.get_logger(__name__)
class lowerCAmelCase__ ( __magic_name__ ):
'''simple docstring'''
def __init__( self , *lowercase__ , **lowercase__ ):
'''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__ )
| 516
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
_lowerCamelCase : str = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 516
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : int = logging.get_logger(__name__)
lowerCamelCase__ : str = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
UpperCamelCase = """luke"""
def __init__( self :List[Any] , lowerCamelCase_ :Optional[int]=5_02_67 , lowerCamelCase_ :List[Any]=50_00_00 , lowerCamelCase_ :str=7_68 , lowerCamelCase_ :Optional[Any]=2_56 , lowerCamelCase_ :List[Any]=12 , lowerCamelCase_ :List[Any]=12 , lowerCamelCase_ :Any=30_72 , lowerCamelCase_ :Optional[int]="gelu" , lowerCamelCase_ :Dict=0.1 , lowerCamelCase_ :Any=0.1 , lowerCamelCase_ :str=5_12 , lowerCamelCase_ :Tuple=2 , lowerCamelCase_ :Optional[Any]=0.0_2 , lowerCamelCase_ :Optional[int]=1E-12 , lowerCamelCase_ :Tuple=True , lowerCamelCase_ :int=None , lowerCamelCase_ :Dict=1 , lowerCamelCase_ :str=0 , lowerCamelCase_ :int=2 , **lowerCamelCase_ :List[str] , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase_ , bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : Optional[Any] = entity_vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size
SCREAMING_SNAKE_CASE : Dict = entity_emb_size
SCREAMING_SNAKE_CASE : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : str = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : Dict = type_vocab_size
SCREAMING_SNAKE_CASE : List[Any] = initializer_range
SCREAMING_SNAKE_CASE : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[Any] = use_entity_aware_attention
SCREAMING_SNAKE_CASE : str = classifier_dropout
| 698
|
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
lowerCamelCase__ : List[Any] = "\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
lowerCamelCase__ : List[str] = "\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
lowerCamelCase__ : List[Any] = "\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase__( datasets.Metric ):
'''simple docstring'''
def __lowerCAmelCase ( self :Optional[int] ) -> int:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://github.com/krishnap25/mauve''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/krishnap25/mauve'''] , reference_urls=[
'''https://arxiv.org/abs/2102.01454''',
'''https://github.com/krishnap25/mauve''',
] , )
def __lowerCAmelCase ( self :Union[str, Any] , lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :List[str] , lowerCamelCase_ :Optional[Any]=None , lowerCamelCase_ :str=None , lowerCamelCase_ :Tuple=None , lowerCamelCase_ :Optional[Any]=None , lowerCamelCase_ :Optional[int]="auto" , lowerCamelCase_ :Dict=-1 , lowerCamelCase_ :str=0.9 , lowerCamelCase_ :str=5 , lowerCamelCase_ :Tuple=5_00 , lowerCamelCase_ :str="gpt2-large" , lowerCamelCase_ :List[Any]=-1 , lowerCamelCase_ :Dict=10_24 , lowerCamelCase_ :Tuple=25 , lowerCamelCase_ :List[Any]=5 , lowerCamelCase_ :Dict=True , lowerCamelCase_ :List[Any]=25 , ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = compute_mauve(
p_text=lowerCamelCase_ , q_text=lowerCamelCase_ , p_features=lowerCamelCase_ , q_features=lowerCamelCase_ , p_tokens=lowerCamelCase_ , q_tokens=lowerCamelCase_ , num_buckets=lowerCamelCase_ , pca_max_data=lowerCamelCase_ , kmeans_explained_var=lowerCamelCase_ , kmeans_num_redo=lowerCamelCase_ , kmeans_max_iter=lowerCamelCase_ , featurize_model_name=lowerCamelCase_ , device_id=lowerCamelCase_ , max_text_length=lowerCamelCase_ , divergence_curve_discretization_size=lowerCamelCase_ , mauve_scaling_factor=lowerCamelCase_ , verbose=lowerCamelCase_ , seed=lowerCamelCase_ , )
return out
| 698
| 1
|
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__A : List[str] = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n'
__A : List[Any] = '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'
__A : Any = '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for \'record\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'prediction_text\': the predicted answer text\n - for \'multirc\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question-answer pair as specified by the dataset\n - \'prediction\': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for \'record\': list of question-answers dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'answers\': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for \'record\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1\': F1 score\n - for \'multirc\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1_m\': Per-question macro-F1 score\n - \'f1_a\': Average F1 score over all answers\n - for \'axb\':\n \'matthews_correlation\': Matthew Correlation\n - for \'cb\':\n - \'accuracy\': Accuracy\n - \'f1\': F1 score\n - for all others:\n - \'accuracy\': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'matthews_correlation\': 1.0}\n'
def __UpperCamelCase ( _A : str , _A : int ) ->List[str]:
"""simple docstring"""
return float((preds == labels).mean() )
def __UpperCamelCase ( _A : Any , _A : Union[str, Any] , _A : Any="binary" ) ->Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =simple_accuracy(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowerCamelCase_ =float(fa_score(y_true=__SCREAMING_SNAKE_CASE , y_pred=__SCREAMING_SNAKE_CASE , average=__SCREAMING_SNAKE_CASE ) )
return {
"accuracy": acc,
"f1": fa,
}
def __UpperCamelCase ( _A : Union[str, Any] , _A : Dict ) ->List[str]:
"""simple docstring"""
lowerCamelCase_ ={}
for id_pred, label in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ =f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
lowerCamelCase_ =id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
lowerCamelCase_ =[(pred, label)]
lowerCamelCase_ , lowerCamelCase_ =[], []
for question, preds_labels in question_map.items():
lowerCamelCase_ , lowerCamelCase_ =zip(*__SCREAMING_SNAKE_CASE )
lowerCamelCase_ =fa_score(y_true=__SCREAMING_SNAKE_CASE , y_pred=__SCREAMING_SNAKE_CASE , average="""macro""" )
fas.append(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ =int(sum(pred == label for pred, label in preds_labels ) == len(__SCREAMING_SNAKE_CASE ) )
ems.append(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ =float(sum(__SCREAMING_SNAKE_CASE ) / len(__SCREAMING_SNAKE_CASE ) )
lowerCamelCase_ =sum(__SCREAMING_SNAKE_CASE ) / len(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ =float(fa_score(y_true=__SCREAMING_SNAKE_CASE , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _SCREAMING_SNAKE_CASE ( datasets.Metric):
def _snake_case ( self )-> int:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def _snake_case ( self )-> List[str]:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )-> Dict:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
elif self.config_name == "cb":
return acc_and_fa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , fa_avg="""macro""" )
elif self.config_name == "record":
lowerCamelCase_ =[
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
lowerCamelCase_ ={pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )[0]
elif self.config_name == "multirc":
return evaluate_multirc(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 701
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__A : int = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
_UpperCamelCase:Any = "albert"
def __init__( self , _SCREAMING_SNAKE_CASE=3_0000 , _SCREAMING_SNAKE_CASE=128 , _SCREAMING_SNAKE_CASE=4096 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=1_6384 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE="gelu_new" , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1E-12 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE="absolute" , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=3 , **_SCREAMING_SNAKE_CASE , )-> Optional[int]:
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =vocab_size
lowerCamelCase_ =embedding_size
lowerCamelCase_ =hidden_size
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_hidden_groups
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =inner_group_num
lowerCamelCase_ =hidden_act
lowerCamelCase_ =intermediate_size
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =attention_probs_dropout_prob
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =type_vocab_size
lowerCamelCase_ =initializer_range
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =classifier_dropout_prob
lowerCamelCase_ =position_embedding_type
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
@property
def _snake_case ( self )-> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCamelCase_ ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCamelCase_ ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 75
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = '''transfo-xl'''
UpperCAmelCase : Union[str, Any] = ['''mems''']
UpperCAmelCase : Dict = {
'''n_token''': '''vocab_size''',
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Dict , _UpperCAmelCase : int=267_735 , _UpperCAmelCase : Union[str, Any]=[20_000, 40_000, 200_000] , _UpperCAmelCase : Union[str, Any]=1_024 , _UpperCAmelCase : Any=1_024 , _UpperCAmelCase : int=16 , _UpperCAmelCase : Optional[Any]=64 , _UpperCAmelCase : Optional[Any]=4_096 , _UpperCAmelCase : Optional[int]=4 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Tuple=18 , _UpperCAmelCase : Dict=1_600 , _UpperCAmelCase : int=1_000 , _UpperCAmelCase : Any=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : int=0 , _UpperCAmelCase : Union[str, Any]=-1 , _UpperCAmelCase : Any=True , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : Any=0.0 , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Dict="normal" , _UpperCAmelCase : List[str]=0.01 , _UpperCAmelCase : Dict=0.01 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : str=1E-5 , _UpperCAmelCase : Dict=0 , **_UpperCAmelCase : Any , ):
_A = vocab_size
_A = []
self.cutoffs.extend(_UpperCAmelCase )
if proj_share_all_but_first:
_A = [False] + [True] * len(self.cutoffs )
else:
_A = [False] + [False] * len(self.cutoffs )
_A = d_model
_A = d_embed
_A = d_head
_A = d_inner
_A = div_val
_A = pre_lnorm
_A = n_layer
_A = n_head
_A = mem_len
_A = same_length
_A = attn_type
_A = clamp_len
_A = sample_softmax
_A = adaptive
_A = dropout
_A = dropatt
_A = untie_r
_A = init
_A = init_range
_A = proj_init_std
_A = init_std
_A = layer_norm_epsilon
super().__init__(eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : Optional[int] ):
# Message copied from Transformer-XL documentation
logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
return -1
@max_position_embeddings.setter
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : Dict ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 7
|
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
def wrapper(*_UpperCAmelCase, **_UpperCAmelCase ):
lowerCAmelCase : str = timeit.default_timer()
lowerCAmelCase : str = func(*_UpperCAmelCase, **_UpperCAmelCase )
lowerCAmelCase : Optional[int] = timeit.default_timer() - starttime
return delta
lowerCAmelCase : Union[str, Any] = func.__name__
return wrapper
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase=100, _UpperCAmelCase=None ) -> Any:
'''simple docstring'''
lowerCAmelCase : Dict = []
lowerCAmelCase : Optional[int] = seq_shapes or {}
for i in range(_UpperCAmelCase ):
lowerCAmelCase : Any = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(_UpperCAmelCase, _ArrayXD ):
lowerCAmelCase : Dict = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(_UpperCAmelCase, datasets.Value ):
if v.dtype == "string":
lowerCAmelCase : Any = 'The small grey turtle was surprisingly fast when challenged.'
else:
lowerCAmelCase : Optional[Any] = np.random.randint(10, size=1 ).astype(v.dtype ).item()
elif isinstance(_UpperCAmelCase, datasets.Sequence ):
while isinstance(_UpperCAmelCase, datasets.Sequence ):
lowerCAmelCase : int = v.feature
lowerCAmelCase : Optional[int] = seq_shapes[k]
lowerCAmelCase : str = np.random.rand(*_UpperCAmelCase ).astype(v.dtype )
lowerCAmelCase : Any = data
dummy_data.append((i, example) )
return dummy_data
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase=100, _UpperCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase : Any = generate_examples(_UpperCAmelCase, num_examples=_UpperCAmelCase, seq_shapes=_UpperCAmelCase )
with ArrowWriter(features=_UpperCAmelCase, path=_UpperCAmelCase ) as writer:
for key, record in dummy_data:
lowerCAmelCase : Any = features.encode_example(_UpperCAmelCase )
writer.write(_UpperCAmelCase )
lowerCAmelCase , lowerCAmelCase : Optional[int] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
lowerCAmelCase : int = datasets.Dataset.from_file(filename=_UpperCAmelCase, info=datasets.DatasetInfo(features=_UpperCAmelCase ) )
return dataset
| 343
| 0
|
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_snake_case : Dict = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case : List[Any] = [ord(letter) for letter in string.ascii_lowercase]
_snake_case : Optional[Any] = {ord(char) for char in VALID_CHARS}
_snake_case : int = ['the', 'be', 'to', 'of', 'and', 'in', 'that', 'have']
def A__ ( UpperCamelCase , UpperCamelCase ):
A = ""
A = 42
A = 42
A = 42
for keychar, cipherchar in zip(cycle(snake_case__ ) , snake_case__ ):
A = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(snake_case__ )
return decoded
def A__ ( UpperCamelCase ):
A = []
for key in product(snake_case__ , repeat=3 ):
A = try_key(snake_case__ , snake_case__ )
if encoded is not None:
possibles.append(snake_case__ )
return possibles
def A__ ( UpperCamelCase , UpperCamelCase ):
return [possible for possible in possibles if common_word in possible.lower()]
def A__ ( UpperCamelCase = "p059_cipher.txt" ):
A = 42
A = 42
A = 42
A = 42
A = Path(snake_case__ ).parent.joinpath(snake_case__ ).read_text(encoding="utf-8" )
A = [int(snake_case__ ) for number in data.strip().split("," )]
A = filter_valid_chars(snake_case__ )
for common_word in COMMON_WORDS:
A = filter_common_word(snake_case__ , snake_case__ )
if len(snake_case__ ) == 1:
break
A = possibles[0]
return sum(ord(snake_case__ ) for char in decoded_text )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 706
|
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
_snake_case : Tuple = logging.get_logger(__name__)
_snake_case : Union[str, Any] = 'T5Config'
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
A = jnp.zeros_like(UpperCamelCase )
A = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
A = shifted_input_ids.at[:, 0].set(UpperCamelCase )
A = jnp.where(shifted_input_ids == -100 , UpperCamelCase , UpperCamelCase )
return shifted_input_ids
class _UpperCAmelCase ( lowercase_ ):
UpperCamelCase = '''mt5'''
UpperCamelCase = MTaConfig
class _UpperCAmelCase ( lowercase_ ):
UpperCamelCase = '''mt5'''
UpperCamelCase = MTaConfig
class _UpperCAmelCase ( lowercase_ ):
UpperCamelCase = '''mt5'''
UpperCamelCase = MTaConfig
| 524
| 0
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def _UpperCamelCase ():
"""simple docstring"""
UpperCamelCase__ = ArgumentParser("""Accelerate CLI tool""" , usage="""accelerate <command> [<args>]""" , allow_abbrev=lowerCamelCase__ )
UpperCamelCase__ = parser.add_subparsers(help="""accelerate command helpers""" )
# Register commands
get_config_parser(subparsers=lowerCamelCase__ )
env_command_parser(subparsers=lowerCamelCase__ )
launch_command_parser(subparsers=lowerCamelCase__ )
tpu_command_parser(subparsers=lowerCamelCase__ )
test_command_parser(subparsers=lowerCamelCase__ )
# Let's go
UpperCamelCase__ = parser.parse_args()
if not hasattr(lowerCamelCase__ , """func""" ):
parser.print_help()
exit(1 )
# Run
args.func(lowerCamelCase__ )
if __name__ == "__main__":
main()
| 619
|
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionControlNetImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'} )
lowerCAmelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE_( self ) -> int:
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
torch.manual_seed(0 )
lowerCamelCase_ = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
lowerCamelCase_ = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , clip_sample=lowercase , set_alpha_to_one=lowercase , )
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCamelCase_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
lowerCamelCase_ = CLIPTextModel(lowercase )
lowerCamelCase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCamelCase_ = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=0 ) -> int:
if str(lowercase ).startswith("mps" ):
lowerCamelCase_ = torch.manual_seed(lowercase )
else:
lowerCamelCase_ = torch.Generator(device=lowercase ).manual_seed(lowercase )
lowerCamelCase_ = 2
lowerCamelCase_ = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=lowercase , device=torch.device(lowercase ) , )
lowerCamelCase_ = floats_tensor(control_image.shape , rng=random.Random(lowercase ) ).to(lowercase )
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase_ = Image.fromarray(np.uinta(lowercase ) ).convert("RGB" ).resize((64, 64) )
lowerCamelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": control_image,
}
return inputs
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
self._test_inference_batch_single_identical(expected_max_diff=2e-3 )
class _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = StableDiffusionControlNetImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
torch.manual_seed(0 )
def init_weights(lowercase ):
if isinstance(lowercase , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowerCamelCase_ = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(lowercase )
torch.manual_seed(0 )
lowerCamelCase_ = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(lowercase )
torch.manual_seed(0 )
lowerCamelCase_ = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , clip_sample=lowercase , set_alpha_to_one=lowercase , )
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCamelCase_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
lowerCamelCase_ = CLIPTextModel(lowercase )
lowerCamelCase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCamelCase_ = MultiControlNetModel([controlneta, controlneta] )
lowerCamelCase_ = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=0 ) -> List[Any]:
if str(lowercase ).startswith("mps" ):
lowerCamelCase_ = torch.manual_seed(lowercase )
else:
lowerCamelCase_ = torch.Generator(device=lowercase ).manual_seed(lowercase )
lowerCamelCase_ = 2
lowerCamelCase_ = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=lowercase , device=torch.device(lowercase ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=lowercase , device=torch.device(lowercase ) , ),
]
lowerCamelCase_ = floats_tensor(control_image[0].shape , rng=random.Random(lowercase ) ).to(lowercase )
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase_ = Image.fromarray(np.uinta(lowercase ) ).convert("RGB" ).resize((64, 64) )
lowerCamelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
"control_image": control_image,
}
return inputs
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = self.pipeline_class(**lowercase )
pipe.to(lowercase )
lowerCamelCase_ = 1_0.0
lowerCamelCase_ = 4
lowerCamelCase_ = self.get_dummy_inputs(lowercase )
lowerCamelCase_ = steps
lowerCamelCase_ = scale
lowerCamelCase_ = pipe(**lowercase )[0]
lowerCamelCase_ = self.get_dummy_inputs(lowercase )
lowerCamelCase_ = steps
lowerCamelCase_ = scale
lowerCamelCase_ = pipe(**lowercase , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
lowerCamelCase_ = self.get_dummy_inputs(lowercase )
lowerCamelCase_ = steps
lowerCamelCase_ = scale
lowerCamelCase_ = pipe(**lowercase , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
lowerCamelCase_ = self.get_dummy_inputs(lowercase )
lowerCamelCase_ = steps
lowerCamelCase_ = scale
lowerCamelCase_ = pipe(**lowercase , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(expected_max_diff=2e-3 )
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(lowercase )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny" )
lowerCamelCase_ = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , safety_checker=lowercase , controlnet=lowercase )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = torch.Generator(device="cpu" ).manual_seed(0 )
lowerCamelCase_ = "evil space-punk bird"
lowerCamelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ).resize((512, 512) )
lowerCamelCase_ = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png" ).resize((512, 512) )
lowerCamelCase_ = pipe(
lowercase , lowercase , control_image=lowercase , generator=lowercase , output_type="np" , num_inference_steps=50 , strength=0.6 , )
lowerCamelCase_ = output.images[0]
assert image.shape == (512, 512, 3)
lowerCamelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy" )
assert np.abs(expected_image - image ).max() < 9e-2
| 463
| 0
|
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 10 ) -> str:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or n < 0:
raise ValueError("Invalid input" )
__snake_case = 10**n
__snake_case = 2_84_33 * (pow(2 , 7_83_04_57 , _UpperCAmelCase )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(10) = }''')
| 717
|
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( _UpperCAmelCase : Dict ) -> int: # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( ) -> Dict:
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
__snake_case = [1, 2, 3]
with pytest.raises(_UpperCAmelCase ):
with parallel_backend("unsupported backend" ):
map_nested(_UpperCAmelCase , _UpperCAmelCase , num_proc=2 )
with pytest.raises(_UpperCAmelCase ):
with parallel_backend("unsupported backend" ):
map_nested(_UpperCAmelCase , _UpperCAmelCase , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" , [2, -1] )
def __UpperCAmelCase ( _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
__snake_case = [1, 2]
__snake_case = {"a": 1, "b": 2}
__snake_case = {"a": [1, 2], "b": [3, 4]}
__snake_case = {"a": {"1": 1}, "b": 2}
__snake_case = {"a": 1, "b": 2, "c": 3, "d": 4}
__snake_case = [2, 3]
__snake_case = {"a": 2, "b": 3}
__snake_case = {"a": [2, 3], "b": [4, 5]}
__snake_case = {"a": {"1": 2}, "b": 3}
__snake_case = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(_UpperCAmelCase , _UpperCAmelCase , num_proc=_UpperCAmelCase ) == expected_map_nested_sa
assert map_nested(_UpperCAmelCase , _UpperCAmelCase , num_proc=_UpperCAmelCase ) == expected_map_nested_sa
assert map_nested(_UpperCAmelCase , _UpperCAmelCase , num_proc=_UpperCAmelCase ) == expected_map_nested_sa
assert map_nested(_UpperCAmelCase , _UpperCAmelCase , num_proc=_UpperCAmelCase ) == expected_map_nested_sa
assert map_nested(_UpperCAmelCase , _UpperCAmelCase , num_proc=_UpperCAmelCase ) == expected_map_nested_sa
| 680
| 0
|
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase : List[str] = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
lowerCamelCase : Any = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
lowerCamelCase : Optional[int] = max(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_ ) , b_binary.zfill(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 340
|
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_snake_case = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
__A : Tuple = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__A : List[str] = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__A : Any = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__A : Optional[Any] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def _snake_case ( self , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : Any = ZeroShotClassificationPipeline(
model=__A , tokenizer=__A , candidate_labels=["polics", "health"] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def _snake_case ( self , __A , __A ):
"""simple docstring"""
lowerCamelCase : List[str] = classifier("Who are you voting for in 2020?" , candidate_labels="politics" )
self.assertEqual(__A , {"sequence": ANY(__A ), "labels": [ANY(__A )], "scores": [ANY(__A )]} )
# No kwarg
lowerCamelCase : str = classifier("Who are you voting for in 2020?" , ["politics"] )
self.assertEqual(__A , {"sequence": ANY(__A ), "labels": [ANY(__A )], "scores": [ANY(__A )]} )
lowerCamelCase : str = classifier("Who are you voting for in 2020?" , candidate_labels=["politics"] )
self.assertEqual(__A , {"sequence": ANY(__A ), "labels": [ANY(__A )], "scores": [ANY(__A )]} )
lowerCamelCase : Optional[int] = classifier("Who are you voting for in 2020?" , candidate_labels="politics, public health" )
self.assertEqual(
__A , {"sequence": ANY(__A ), "labels": [ANY(__A ), ANY(__A )], "scores": [ANY(__A ), ANY(__A )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"] ) ) , 1.0 )
lowerCamelCase : str = classifier("Who are you voting for in 2020?" , candidate_labels=["politics", "public health"] )
self.assertEqual(
__A , {"sequence": ANY(__A ), "labels": [ANY(__A ), ANY(__A )], "scores": [ANY(__A ), ANY(__A )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"] ) ) , 1.0 )
lowerCamelCase : List[str] = classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template="This text is about {}" )
self.assertEqual(__A , {"sequence": ANY(__A ), "labels": [ANY(__A )], "scores": [ANY(__A )]} )
# https://github.com/huggingface/transformers/issues/13846
lowerCamelCase : str = classifier(["I am happy"] , ["positive", "negative"] )
self.assertEqual(
__A , [
{"sequence": ANY(__A ), "labels": [ANY(__A ), ANY(__A )], "scores": [ANY(__A ), ANY(__A )]}
for i in range(1 )
] , )
lowerCamelCase : Union[str, Any] = classifier(["I am happy", "I am sad"] , ["positive", "negative"] )
self.assertEqual(
__A , [
{"sequence": ANY(__A ), "labels": [ANY(__A ), ANY(__A )], "scores": [ANY(__A ), ANY(__A )]}
for i in range(2 )
] , )
with self.assertRaises(__A ):
classifier("" , candidate_labels="politics" )
with self.assertRaises(__A ):
classifier(__A , candidate_labels="politics" )
with self.assertRaises(__A ):
classifier("Who are you voting for in 2020?" , candidate_labels="" )
with self.assertRaises(__A ):
classifier("Who are you voting for in 2020?" , candidate_labels=__A )
with self.assertRaises(__A ):
classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template="Not formatting template" , )
with self.assertRaises(__A ):
classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template=__A , )
self.run_entailment_id(__A )
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : int = zero_shot_classifier.model.config
lowerCamelCase : Optional[Any] = config.labelaid
lowerCamelCase : Union[str, Any] = zero_shot_classifier.entailment_id
lowerCamelCase : Tuple = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
lowerCamelCase : Any = {"entailment": 0, "neutral": 1, "contradiction": 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
lowerCamelCase : Optional[int] = {"ENTAIL": 0, "NON-ENTAIL": 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
lowerCamelCase : List[Any] = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
lowerCamelCase : Any = original_labelaid
self.assertEqual(__A , zero_shot_classifier.entailment_id )
@require_torch
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="pt" , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"Who are you voting for in 2020?" * 100 , candidate_labels=["politics", "public health", "science"] )
@require_torch
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : List[str] = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="pt" , )
lowerCamelCase : int = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__A ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.333, 0.333, 0.333],
} , )
@require_tf
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="tf" , )
lowerCamelCase : Union[str, Any] = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__A ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.333, 0.333, 0.333],
} , )
@slow
@require_torch
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = pipeline("zero-shot-classification" , model="roberta-large-mnli" , framework="pt" )
lowerCamelCase : Tuple = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__A ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.976, 0.015, 0.009],
} , )
lowerCamelCase : Union[str, Any] = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data." , candidate_labels=["machine learning", "statistics", "translation", "vision"] , multi_label=__A , )
self.assertEqual(
nested_simplify(__A ) , {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.817, 0.713, 0.018, 0.018],
} , )
@slow
@require_tf
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Dict = pipeline("zero-shot-classification" , model="roberta-large-mnli" , framework="tf" )
lowerCamelCase : Optional[int] = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__A ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.976, 0.015, 0.009],
} , )
lowerCamelCase : List[Any] = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data." , candidate_labels=["machine learning", "statistics", "translation", "vision"] , multi_label=__A , )
self.assertEqual(
nested_simplify(__A ) , {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.817, 0.713, 0.018, 0.018],
} , )
| 340
| 1
|
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCamelCase ( ) -> Any:
'''simple docstring'''
lowercase_ :Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
lowercase_ :List[Any] = parser.add_subparsers(help='''diffusers-cli command helpers''' )
# Register commands
EnvironmentCommand.register_subcommand(_a )
# Let's go
lowercase_ :Union[str, Any] = parser.parse_args()
if not hasattr(_a , '''func''' ):
parser.print_help()
exit(1 )
# Run
lowercase_ :Optional[Any] = args.func(_a )
service.run()
if __name__ == "__main__":
main()
| 700
|
def UpperCamelCase ( _a , _a ) -> int:
'''simple docstring'''
while a != 0:
lowercase_ , lowercase_ :Union[str, Any] = b % a, a
return b
def UpperCamelCase ( _a , _a ) -> int:
'''simple docstring'''
if gcd(_a , _a ) != 1:
lowercase_ :Union[str, Any] = f"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(_a )
lowercase_ , lowercase_ , lowercase_ :Any = 1, 0, a
lowercase_ , lowercase_ , lowercase_ :List[str] = 0, 1, m
while va != 0:
lowercase_ :Tuple = ua // va
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ :Optional[int] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 441
| 0
|
import requests
from bsa import BeautifulSoup
def _A ( lowerCamelCase = "https://www.worldometers.info/coronavirus" ):
a__ : List[str] = BeautifulSoup(requests.get(lowerCamelCase ).text , "html.parser" )
a__ : List[Any] = soup.findAll("h1" )
a__ : List[str] = 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(lowerCamelCase , lowerCamelCase )}
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')
| 112
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
SCREAMING_SNAKE_CASE__ : int = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
SCREAMING_SNAKE_CASE__ : List[str] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : Any = """whisper"""
_UpperCamelCase : Union[str, Any] = ["""past_key_values"""]
_UpperCamelCase : Union[str, Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , snake_case=51_865 , snake_case=80 , snake_case=6 , snake_case=4 , snake_case=6 , snake_case=4 , snake_case=1_536 , snake_case=1_536 , snake_case=0.0 , snake_case=0.0 , snake_case=50_257 , snake_case=True , snake_case=True , snake_case="gelu" , snake_case=256 , snake_case=0.0 , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=False , snake_case=1_500 , snake_case=448 , snake_case=50_256 , snake_case=50_256 , snake_case=50_256 , snake_case=None , snake_case=[220, 50_256] , snake_case=False , snake_case=256 , snake_case=False , snake_case=0.05 , snake_case=10 , snake_case=2 , snake_case=0.0 , snake_case=10 , snake_case=0 , snake_case=7 , **snake_case , ) -> Dict:
"""simple docstring"""
a__ : Optional[Any] = vocab_size
a__ : int = num_mel_bins
a__ : Dict = d_model
a__ : List[Any] = encoder_layers
a__ : List[Any] = encoder_attention_heads
a__ : Optional[int] = decoder_layers
a__ : int = decoder_attention_heads
a__ : Optional[Any] = decoder_ffn_dim
a__ : List[Any] = encoder_ffn_dim
a__ : int = dropout
a__ : Optional[int] = attention_dropout
a__ : Tuple = activation_dropout
a__ : Optional[Any] = activation_function
a__ : List[Any] = init_std
a__ : List[Any] = encoder_layerdrop
a__ : Dict = decoder_layerdrop
a__ : List[Any] = use_cache
a__ : Union[str, Any] = encoder_layers
a__ : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
a__ : Tuple = max_source_positions
a__ : Optional[Any] = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
a__ : Optional[Any] = classifier_proj_size
a__ : int = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
a__ : Tuple = apply_spec_augment
a__ : int = mask_time_prob
a__ : Optional[Any] = mask_time_length
a__ : List[str] = mask_time_min_masks
a__ : List[str] = mask_feature_prob
a__ : Dict = mask_feature_length
a__ : Any = mask_feature_min_masks
a__ : List[str] = median_filter_width
super().__init__(
pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , is_encoder_decoder=snake_case , decoder_start_token_id=snake_case , suppress_tokens=snake_case , begin_suppress_tokens=snake_case , **snake_case , )
class __lowerCAmelCase ( _UpperCamelCase ):
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
a__ : Dict = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
] )
if self.use_past:
a__ : Tuple = {0: "batch"}
else:
a__ : Union[str, Any] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(snake_case , direction="inputs" )
return common_inputs
def _snake_case ( self , snake_case , snake_case = -1 , snake_case = -1 , snake_case = False , snake_case = None , snake_case = 22_050 , snake_case = 5.0 , snake_case = 220 , ) -> Mapping[str, Any]:
"""simple docstring"""
a__ : int = OrderedDict()
a__ : List[str] = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=snake_case , framework=snake_case , sampling_rate=snake_case , time_duration=snake_case , frequency=snake_case , )
a__ : Optional[int] = encoder_inputs["input_features"].shape[2]
a__ : str = encoder_sequence_length // 2 if self.use_past else seq_length
a__ : Optional[int] = super().generate_dummy_inputs(
preprocessor.tokenizer , snake_case , snake_case , snake_case , snake_case )
a__ : Any = encoder_inputs.pop("input_features" )
a__ : Dict = decoder_inputs.pop("decoder_input_ids" )
if "past_key_values" in decoder_inputs:
a__ : Tuple = decoder_inputs.pop("past_key_values" )
return dummy_inputs
@property
def _snake_case ( self ) -> float:
"""simple docstring"""
return 1E-3
| 112
| 1
|
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class lowerCamelCase__ :
"""simple docstring"""
def _a (self , __a , __a ):
'''simple docstring'''
pass
def _a (self ):
'''simple docstring'''
pass
def _a (self ):
'''simple docstring'''
pass
def _a (self , __a , __a , __a , __a , __a=None , **__a ):
'''simple docstring'''
lowerCamelCase = VisionTextDualEncoderConfig.from_vision_text_configs(__a , __a )
lowerCamelCase = TFVisionTextDualEncoderModel(__a )
lowerCamelCase = model(input_ids=__a , pixel_values=__a , attention_mask=__a )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim) )
def _a (self , __a , __a , __a , __a , __a=None , **__a ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase = self.get_vision_text_model(__a , __a )
lowerCamelCase = TFVisionTextDualEncoderModel(vision_model=__a , text_model=__a )
lowerCamelCase = model(input_ids=__a , pixel_values=__a , attention_mask=__a )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a (self , __a , __a , __a , __a , __a=None , **__a ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase = self.get_vision_text_model(__a , __a )
lowerCamelCase = {"vision_model": vision_model, "text_model": text_model}
lowerCamelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__a )
lowerCamelCase = model(input_ids=__a , pixel_values=__a , attention_mask=__a )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a (self , __a , __a , __a , __a , __a=None , **__a ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase = self.get_vision_text_model(__a , __a )
lowerCamelCase = TFVisionTextDualEncoderModel(vision_model=__a , text_model=__a )
lowerCamelCase = model(input_ids=__a , pixel_values=__a , attention_mask=__a )
lowerCamelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__a )
lowerCamelCase = TFVisionTextDualEncoderModel.from_pretrained(__a )
lowerCamelCase = model(input_ids=__a , pixel_values=__a , attention_mask=__a )
lowerCamelCase = after_output[0].numpy()
lowerCamelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__a , 1E-5 )
def _a (self , __a , __a , __a , __a , __a=None , **__a ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase = self.get_vision_text_model(__a , __a )
lowerCamelCase = TFVisionTextDualEncoderModel(vision_model=__a , text_model=__a )
lowerCamelCase = model(
input_ids=__a , pixel_values=__a , attention_mask=__a , output_attentions=__a )
lowerCamelCase = output.vision_model_output.attentions
self.assertEqual(len(__a ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase = to_atuple(vision_model.config.image_size )
lowerCamelCase = to_atuple(vision_model.config.patch_size )
lowerCamelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowerCamelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowerCamelCase = output.text_model_output.attentions
self.assertEqual(len(__a ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a (self , __a , __a , __a ):
'''simple docstring'''
lowerCamelCase = np.abs((a - b) ).max()
self.assertLessEqual(__a , __a , F"""Difference between torch and flax is {diff} (>= {tol}).""" )
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**__a )
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**__a )
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**__a )
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.prepare_config_and_inputs()
self.check_save_load(**__a )
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**__a )
@slow
def _a (self ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase = self.get_pretrained_model_and_inputs()
lowerCamelCase = model_a(**__a )
lowerCamelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(__a )
lowerCamelCase = TFVisionTextDualEncoderModel.from_pretrained(__a )
lowerCamelCase = model_a(**__a )
lowerCamelCase = after_outputs[0].numpy()
lowerCamelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__a , 1E-5 )
@require_tf
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase):
"""simple docstring"""
def _a (self ):
'''simple docstring'''
lowerCamelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert" )
lowerCamelCase = 13
lowerCamelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowerCamelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowerCamelCase = random_attention_mask([batch_size, 4] )
lowerCamelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _a (self , __a , __a ):
'''simple docstring'''
lowerCamelCase = TFViTModel(__a , name="vision_model" )
lowerCamelCase = TFBertModel(__a , name="text_model" )
return vision_model, text_model
def _a (self ):
'''simple docstring'''
lowerCamelCase = TFViTModelTester(self )
lowerCamelCase = TFBertModelTester(self )
lowerCamelCase = vit_model_tester.prepare_config_and_inputs()
lowerCamelCase = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase , lowerCamelCase , lowerCamelCase = vision_config_and_inputs
(
(
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase):
"""simple docstring"""
def _a (self ):
'''simple docstring'''
lowerCamelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta" )
lowerCamelCase = 13
lowerCamelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowerCamelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowerCamelCase = random_attention_mask([batch_size, 4] )
lowerCamelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _a (self , __a , __a , __a , __a , __a=None , **__a ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase = self.get_vision_text_model(__a , __a )
lowerCamelCase = TFVisionTextDualEncoderModel(vision_model=__a , text_model=__a )
lowerCamelCase = model(
input_ids=__a , pixel_values=__a , attention_mask=__a , output_attentions=__a )
lowerCamelCase = output.vision_model_output.attentions
self.assertEqual(len(__a ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
lowerCamelCase = to_atuple(vision_model.config.image_size )
lowerCamelCase = to_atuple(vision_model.config.patch_size )
lowerCamelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowerCamelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowerCamelCase = output.text_model_output.attentions
self.assertEqual(len(__a ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a (self , __a , __a ):
'''simple docstring'''
lowerCamelCase = TFDeiTModel(__a , name="vision_model" )
lowerCamelCase = TFRobertaModel(__a , name="text_model" )
return vision_model, text_model
def _a (self ):
'''simple docstring'''
lowerCamelCase = TFDeiTModelTester(self )
lowerCamelCase = TFRobertaModelTester(self )
lowerCamelCase = vit_model_tester.prepare_config_and_inputs()
lowerCamelCase = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase , lowerCamelCase , lowerCamelCase = vision_config_and_inputs
(
(
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase):
"""simple docstring"""
def _a (self ):
'''simple docstring'''
lowerCamelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert" )
lowerCamelCase = 13
lowerCamelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowerCamelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowerCamelCase = random_attention_mask([batch_size, 4] )
lowerCamelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def _a (self , __a , __a ):
'''simple docstring'''
lowerCamelCase = TFCLIPVisionModel(__a , name="vision_model" )
lowerCamelCase = TFBertModel(__a , name="text_model" )
return vision_model, text_model
def _a (self ):
'''simple docstring'''
lowerCamelCase = TFCLIPVisionModelTester(self )
lowerCamelCase = TFBertModelTester(self )
lowerCamelCase = clip_model_tester.prepare_config_and_inputs()
lowerCamelCase = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase , lowerCamelCase = vision_config_and_inputs
(
(
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) , (
lowerCamelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
@slow
def _a (self ):
'''simple docstring'''
lowerCamelCase = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=__a )
lowerCamelCase = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
lowerCamelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
lowerCamelCase = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=__a , padding=__a , return_tensors="np" )
lowerCamelCase = model(**__a )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
lowerCamelCase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , __a , atol=1E-3 ) )
| 484
|
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
_A = 42
_A = 42
class lowerCamelCase__ :
"""simple docstring"""
def __init__(self , __a ):
'''simple docstring'''
lowerCamelCase = [[] for _ in range(__a )]
lowerCamelCase = size
def __getitem__(self , __a ):
'''simple docstring'''
return iter(self._graph[vertex] )
@property
def _a (self ):
'''simple docstring'''
return self._size
def _a (self , __a , __a , __a ):
'''simple docstring'''
if weight not in (0, 1):
raise ValueError("Edge weight must be either 0 or 1." )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("Vertex indexes must be in [0; size)." )
self._graph[from_vertex].append(Edge(__a , __a ) )
def _a (self , __a , __a ):
'''simple docstring'''
lowerCamelCase = deque([start_vertex] )
lowerCamelCase = [None] * self.size
lowerCamelCase = 0
while queue:
lowerCamelCase = queue.popleft()
lowerCamelCase = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
lowerCamelCase = current_distance + edge.weight
lowerCamelCase = distances[edge.destination_vertex]
if (
isinstance(__a , __a )
and new_distance >= dest_vertex_distance
):
continue
lowerCamelCase = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("No path from start_vertex to finish_vertex." )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 484
| 1
|
from PIL import Image
def lowercase_ ( SCREAMING_SNAKE_CASE : Image ):
"""simple docstring"""
snake_case__, snake_case__ : Tuple =image.size
snake_case__ : List[Any] =0
snake_case__ : List[str] =image.load()
for i in range(SCREAMING_SNAKE_CASE ):
for j in range(SCREAMING_SNAKE_CASE ):
snake_case__ : Any =pixels[j, i]
mean += pixel
mean //= width * height
for j in range(SCREAMING_SNAKE_CASE ):
for i in range(SCREAMING_SNAKE_CASE ):
snake_case__ : int =2_55 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
lowerCamelCase__ = mean_threshold(Image.open('''path_to_image''').convert('''L'''))
image.save('''output_image_path''')
| 381
|
def lowercase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
snake_case__ : int =len(SCREAMING_SNAKE_CASE )
snake_case__ : int =len(SCREAMING_SNAKE_CASE )
snake_case__ : int =(
first_str_length if first_str_length > second_str_length else second_str_length
)
snake_case__ : list =[]
for char_count in range(SCREAMING_SNAKE_CASE ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(alternative_string_arrange('''AB''', '''XYZ'''), end=''' ''')
| 381
| 1
|
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
__SCREAMING_SNAKE_CASE : Dict = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__SCREAMING_SNAKE_CASE : int = subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode('utf-8').split()
__SCREAMING_SNAKE_CASE : str = '|'.join(sys.argv[1:])
__SCREAMING_SNAKE_CASE : str = re.compile(RF'''^({joined_dirs}).*?\.py$''')
__SCREAMING_SNAKE_CASE : Optional[Any] = [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 580
|
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class lowercase_ ( nn.Module ):
def __init__( self ):
super().__init__()
_snake_case : Optional[int] = nn.Linear(3 , 4 )
_snake_case : Any = nn.BatchNormad(4 )
_snake_case : List[str] = nn.Linear(4 , 5 )
def UpperCamelCase ( self , lowercase_ ):
return self.lineara(self.batchnorm(self.lineara(lowercase_ ) ) )
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self , lowercase_ , *lowercase_ , **lowercase_ ):
return (args[0] + 1,) + args[1:], kwargs
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self , lowercase_ , lowercase_ ):
return output + 1
class lowercase_ ( unittest.TestCase ):
def UpperCamelCase ( self ):
_snake_case : List[str] = ModelForTest()
_snake_case : List[str] = ModelHook()
add_hook_to_module(lowercase_ , lowercase_ )
self.assertEqual(test_model._hf_hook , lowercase_ )
self.assertTrue(hasattr(lowercase_ , "_old_forward" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , "forward" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["x"] )
remove_hook_from_module(lowercase_ )
self.assertFalse(hasattr(lowercase_ , "_hf_hook" ) )
self.assertFalse(hasattr(lowercase_ , "_old_forward" ) )
def UpperCamelCase ( self ):
_snake_case : Optional[Any] = ModelForTest()
_snake_case : Any = ModelHook()
add_hook_to_module(lowercase_ , lowercase_ )
add_hook_to_module(lowercase_ , lowercase_ , append=lowercase_ )
self.assertEqual(isinstance(test_model._hf_hook , lowercase_ ) , lowercase_ )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(lowercase_ , "_old_forward" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , "forward" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["x"] )
remove_hook_from_module(lowercase_ )
self.assertFalse(hasattr(lowercase_ , "_hf_hook" ) )
self.assertFalse(hasattr(lowercase_ , "_old_forward" ) )
def UpperCamelCase ( self ):
_snake_case : Union[str, Any] = ModelForTest()
_snake_case : Tuple = torch.randn(2 , 3 )
_snake_case : List[str] = test_model(x + 1 )
_snake_case : str = test_model(x + 2 )
_snake_case : int = PreForwardHook()
add_hook_to_module(lowercase_ , lowercase_ )
_snake_case : Any = test_model(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1e-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_snake_case : str = PreForwardHook()
add_hook_to_module(lowercase_ , lowercase_ )
_snake_case : Union[str, Any] = test_model(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1e-5 ) )
# You need to use the sequential hook to chain two or more hooks
_snake_case : Optional[Any] = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(lowercase_ , lowercase_ )
_snake_case : Optional[Any] = test_model(lowercase_ )
assert torch.allclose(lowercase_ , lowercase_ , atol=1e-5 )
def UpperCamelCase ( self ):
_snake_case : Optional[Any] = ModelForTest()
_snake_case : Dict = torch.randn(2 , 3 )
_snake_case : List[str] = test_model(lowercase_ )
_snake_case : Any = PostForwardHook()
add_hook_to_module(lowercase_ , lowercase_ )
_snake_case : Optional[int] = test_model(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , output + 1 , atol=1e-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_snake_case : Tuple = PostForwardHook()
add_hook_to_module(lowercase_ , lowercase_ )
_snake_case : Any = test_model(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , output + 1 , atol=1e-5 ) )
# You need to use the sequential hook to chain two or more hooks
_snake_case : Union[str, Any] = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(lowercase_ , lowercase_ )
_snake_case : Any = test_model(lowercase_ )
assert torch.allclose(lowercase_ , output + 2 , atol=1e-5 )
def UpperCamelCase ( self ):
_snake_case : Dict = ModelForTest()
_snake_case : List[str] = torch.randn(2 , 3 )
_snake_case : int = test_model(lowercase_ )
_snake_case : str = PostForwardHook()
add_hook_to_module(lowercase_ , lowercase_ )
_snake_case : Dict = test_model(lowercase_ )
self.assertTrue(torch.allclose(lowercase_ , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_snake_case : Dict = True
_snake_case : str = test_model(lowercase_ )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def UpperCamelCase ( self ):
_snake_case : List[Any] = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_snake_case : str = torch.randn(2 , 3 )
_snake_case : int = model(lowercase_ )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(lowercase_ , AlignDevicesHook(io_same_device=lowercase_ ) )
_snake_case : str = torch.randn(2 , 3 ).to(0 )
_snake_case : Dict = model(lowercase_ )
self.assertEqual(output.device , torch.device(0 ) )
def UpperCamelCase ( self ):
_snake_case : Any = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
_snake_case : Tuple = {"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**lowercase_ ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**lowercase_ ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**lowercase_ ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
_snake_case : Optional[Any] = torch.device(hook_kwargs["execution_device"] )
self.assertEqual(model.batchnorm.running_mean.device , lowercase_ )
_snake_case : List[str] = torch.randn(2 , 3 )
_snake_case : Any = model(lowercase_ )
self.assertEqual(output.device , lowercase_ )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
_snake_case : Dict = {
"execution_device": 0 if torch.cuda.is_available() else "cpu",
"offload": True,
"offload_buffers": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**lowercase_ ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**lowercase_ ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**lowercase_ ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
_snake_case : List[str] = torch.randn(2 , 3 )
_snake_case : List[str] = model(lowercase_ )
self.assertEqual(output.device , lowercase_ )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
def UpperCamelCase ( self ):
_snake_case : Tuple = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
_snake_case : Any = 0 if torch.cuda.is_available() else "cpu"
attach_align_device_hook(lowercase_ , execution_device=lowercase_ , offload=lowercase_ )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
_snake_case : Optional[int] = torch.device(lowercase_ )
self.assertEqual(model.batchnorm.running_mean.device , lowercase_ )
_snake_case : Dict = torch.randn(2 , 3 )
_snake_case : int = model(lowercase_ )
self.assertEqual(output.device , lowercase_ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(lowercase_ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
attach_align_device_hook(lowercase_ , execution_device=lowercase_ , offload=lowercase_ , offload_buffers=lowercase_ )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
_snake_case : int = torch.randn(2 , 3 )
_snake_case : str = model(lowercase_ )
self.assertEqual(output.device , lowercase_ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(lowercase_ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
def UpperCamelCase ( self ):
_snake_case : Optional[int] = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
_snake_case : int = 0 if torch.cuda.is_available() else "cpu"
attach_align_device_hook(
lowercase_ , execution_device=lowercase_ , offload=lowercase_ , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
_snake_case : int = torch.device(lowercase_ )
self.assertEqual(model.batchnorm.running_mean.device , lowercase_ )
_snake_case : Union[str, Any] = torch.randn(2 , 3 )
_snake_case : Dict = model(lowercase_ )
self.assertEqual(output.device , lowercase_ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(lowercase_ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
lowercase_ , execution_device=lowercase_ , offload=lowercase_ , weights_map=model.state_dict() , offload_buffers=lowercase_ , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
_snake_case : List[Any] = torch.randn(2 , 3 )
_snake_case : List[Any] = model(lowercase_ )
self.assertEqual(output.device , lowercase_ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(lowercase_ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
| 580
| 1
|
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self , __A , __A=13 , __A=3 , __A=True , __A=True , __A=0.1 , __A=0.1 , __A=224 , __A=1000 , __A=[3, 3, 6, 4] , __A=[48, 56, 112, 220] , ):
__a = parent
__a = batch_size
__a = num_channels
__a = is_training
__a = use_labels
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = num_labels
__a = image_size
__a = layer_depths
__a = embed_dims
def snake_case_ ( self ):
__a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.num_labels )
__a = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self ):
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="""gelu""" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=__A , layer_scale_init_value=1E-5 , )
def snake_case_ ( self , __A , __A , __A ):
__a = SwiftFormerModel(config=__A )
model.to(__A )
model.eval()
__a = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def snake_case_ ( self , __A , __A , __A ):
__a = self.num_labels
__a = SwiftFormerForImageClassification(__A )
model.to(__A )
model.eval()
__a = model(__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
__a = SwiftFormerForImageClassification(__A )
model.to(__A )
model.eval()
__a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a = model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case_ ( self ):
((__a) , (__a) , (__a)) = self.prepare_config_and_inputs()
__a = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( __A , __A , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
_lowerCamelCase = (
{"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def snake_case_ ( self ):
__a = SwiftFormerModelTester(self )
__a = ConfigTester(
self , config_class=__A , has_text_modality=__A , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(__A )
__a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A , nn.Linear ) )
def snake_case_ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(__A )
__a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a = [*signature.parameters.keys()]
__a = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __A )
def snake_case_ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def snake_case_ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@slow
def snake_case_ ( self ):
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = SwiftFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip(reason="""SwiftFormer does not output attentions""" )
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
def check_hidden_states_output(__A , __A , __A ):
__a = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
__a = model(**self._prepare_for_class(__A , __A ) )
__a = outputs.hidden_states
__a = 8
self.assertEqual(len(__A ) , __A ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(__A ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = True
check_hidden_states_output(__A , __A , __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a = True
check_hidden_states_output(__A , __A , __A )
def snake_case_ ( self ):
def _config_zero_init(__A ):
__a = copy.deepcopy(__A )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(__A , __A , 1E-10 )
if isinstance(getattr(__A , __A , __A ) , __A ):
__a = _config_zero_init(getattr(__A , __A ) )
setattr(__A , __A , __A )
return configs_no_init
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = _config_zero_init(__A )
for model_class in self.all_model_classes:
__a = model_class(config=__A )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().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 snake_case_ ( self ):
pass
def a ():
__a = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case_ ( self ):
return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None
@slow
def snake_case_ ( self ):
__a = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(__A )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(images=__A , return_tensors="""pt""" ).to(__A )
# forward pass
with torch.no_grad():
__a = model(**__A )
# verify the logits
__a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __A )
__a = torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __A , atol=1E-4 ) )
| 99
|
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : str = ['''image_processor''', '''tokenizer''']
UpperCAmelCase_ : str = '''ViltImageProcessor'''
UpperCAmelCase_ : Any = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , **__lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __lowerCAmelCase , )
lowerCAmelCase = kwargs.pop("""feature_extractor""")
lowerCAmelCase = 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__(__lowerCAmelCase , __lowerCAmelCase)
lowerCAmelCase = self.image_processor
def __call__( self , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = True , __lowerCAmelCase = False , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = 0 , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = False , __lowerCAmelCase = False , __lowerCAmelCase = False , __lowerCAmelCase = False , __lowerCAmelCase = True , __lowerCAmelCase = None , **__lowerCAmelCase , ):
"""simple docstring"""
lowerCAmelCase = self.tokenizer(
text=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase , stride=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , return_overflowing_tokens=__lowerCAmelCase , return_special_tokens_mask=__lowerCAmelCase , return_offsets_mapping=__lowerCAmelCase , return_length=__lowerCAmelCase , verbose=__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase , )
# add pixel_values + pixel_mask
lowerCAmelCase = self.image_processor(__lowerCAmelCase , return_tensors=__lowerCAmelCase)
encoding.update(__lowerCAmelCase)
return encoding
def a_ ( self , *__lowerCAmelCase , **__lowerCAmelCase):
"""simple docstring"""
return self.tokenizer.batch_decode(*__lowerCAmelCase , **__lowerCAmelCase)
def a_ ( self , *__lowerCAmelCase , **__lowerCAmelCase):
"""simple docstring"""
return self.tokenizer.decode(*__lowerCAmelCase , **__lowerCAmelCase)
@property
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.tokenizer.model_input_names
lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@property
def a_ ( self):
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , __lowerCAmelCase , )
return self.image_processor_class
@property
def a_ ( self):
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , __lowerCAmelCase , )
return self.image_processor
| 370
| 0
|
"""simple docstring"""
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
if "model" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('model.', '' )
if "norm1" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('norm1', 'attention.output.LayerNorm' )
if "norm2" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('norm2', 'output.LayerNorm' )
if "norm" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('norm', 'LayerNorm' )
if "transformer" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.split('.' )[0].split('_' )[-1]
SCREAMING_SNAKE_CASE = orig_key.replace(f'''transformer_{layer_num}''', f'''encoder.layer.{layer_num}''' )
if "mha.attn" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('mha.attn', 'attention.self' )
if "mha" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('mha', 'attention' )
if "W_q" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('W_q', 'self.query' )
if "W_k" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('W_k', 'self.key' )
if "W_v" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('W_v', 'self.value' )
if "ff1" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('ff1', 'intermediate.dense' )
if "ff2" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('ff2', 'output.dense' )
if "ff" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('ff', 'output.dense' )
if "mlm_class" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('mlm.mlm_class', 'cls.predictions.decoder' )
if "mlm" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('mlm', 'cls.predictions.transform' )
if "cls" not in orig_key:
SCREAMING_SNAKE_CASE = 'yoso.' + orig_key
return orig_key
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
SCREAMING_SNAKE_CASE = val
SCREAMING_SNAKE_CASE = orig_state_dict['cls.predictions.decoder.bias']
SCREAMING_SNAKE_CASE = torch.arange(SCREAMING_SNAKE_CASE_ ).expand((1, -1) ) + 2
return orig_state_dict
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_, map_location='cpu' )['model_state_dict']
SCREAMING_SNAKE_CASE = YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = YosoForMaskedLM(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = convert_checkpoint_helper(config.max_position_embeddings, SCREAMING_SNAKE_CASE_ )
print(model.load_state_dict(SCREAMING_SNAKE_CASE_ ) )
model.eval()
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for YOSO model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
snake_case = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 705
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
# fmt: off
SCREAMING_SNAKE_CASE = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
SCREAMING_SNAKE_CASE = dict(zip(lowercase__ , range(len(lowercase__ ) ) ) )
SCREAMING_SNAKE_CASE = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
SCREAMING_SNAKE_CASE = {'unk_token': '<unk>'}
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowercase__ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(lowercase__ ) )
SCREAMING_SNAKE_CASE = {
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , lowercase__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(lowercase__ , lowercase__ )
def A ( self , **lowercase__ ) -> Optional[int]:
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowercase__ )
def A ( self , **lowercase__ ) -> Dict:
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowercase__ )
def A ( self , **lowercase__ ) -> Union[str, Any]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **lowercase__ )
def A ( self ) -> Optional[int]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def A ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(lowercase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=lowercase__ )
SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowercase__ )
self.assertIsInstance(processor_fast.tokenizer , lowercase__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowercase__ )
self.assertIsInstance(processor_fast.image_processor , lowercase__ )
def A ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=lowercase__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=lowercase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowercase__ )
def A ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = image_processor(lowercase__ , return_tensors='np' )
SCREAMING_SNAKE_CASE = processor(images=lowercase__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def A ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
SCREAMING_SNAKE_CASE = 'lower newer'
SCREAMING_SNAKE_CASE = processor(text=lowercase__ )
SCREAMING_SNAKE_CASE = tokenizer(lowercase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
SCREAMING_SNAKE_CASE = 'lower newer'
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(text=lowercase__ , images=lowercase__ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(lowercase__ ):
processor()
def A ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE = processor(images=lowercase__ , visual_prompt=lowercase__ )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'conditional_pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(lowercase__ ):
processor()
def A ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.get_image_processor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE = processor.batch_decode(lowercase__ )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ )
| 406
| 0
|
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
__a: Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
__a: Dict = 256
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
_lowerCamelCase = ['''melgan''']
def __init__( self : List[str] , lowerCamelCase : SpectrogramNotesEncoder , lowerCamelCase : SpectrogramContEncoder , lowerCamelCase : TaFilmDecoder , lowerCamelCase : DDPMScheduler , lowerCamelCase : OnnxRuntimeModel if is_onnx_available() else Any , ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
_UpperCAmelCase = math.log(1E-5 ) # Matches MelGAN training.
_UpperCAmelCase = 4.0 # Largest value for most examples
_UpperCAmelCase = 128
self.register_modules(
notes_encoder=lowerCamelCase , continuous_encoder=lowerCamelCase , decoder=lowerCamelCase , scheduler=lowerCamelCase , melgan=lowerCamelCase , )
def lowerCamelCase ( self : List[str] , lowerCamelCase : List[str] , lowerCamelCase : int=(-1.0, 1.0) , lowerCamelCase : List[str]=False ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = output_range
if clip:
_UpperCAmelCase = torch.clip(lowerCamelCase , self.min_value , self.max_value )
# Scale to [0, 1].
_UpperCAmelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def lowerCamelCase ( self : Union[str, Any] , lowerCamelCase : Dict , lowerCamelCase : Any=(-1.0, 1.0) , lowerCamelCase : Tuple=False ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = input_range
_UpperCAmelCase = torch.clip(lowerCamelCase , lowerCamelCase , lowerCamelCase ) if clip else outputs
# Scale to [0, 1].
_UpperCAmelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def lowerCamelCase ( self : List[str] , lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase = input_tokens > 0
_UpperCAmelCase , _UpperCAmelCase = self.notes_encoder(
encoder_input_tokens=lowerCamelCase , encoder_inputs_mask=lowerCamelCase )
_UpperCAmelCase , _UpperCAmelCase = self.continuous_encoder(
encoder_inputs=lowerCamelCase , encoder_inputs_mask=lowerCamelCase )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def lowerCamelCase ( self : List[Any] , lowerCamelCase : str , lowerCamelCase : int , lowerCamelCase : Tuple ) -> str:
"""simple docstring"""
_UpperCAmelCase = noise_time
if not torch.is_tensor(lowerCamelCase ):
_UpperCAmelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(lowerCamelCase ) and len(timesteps.shape ) == 0:
_UpperCAmelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
_UpperCAmelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
_UpperCAmelCase = self.decoder(
encodings_and_masks=lowerCamelCase , decoder_input_tokens=lowerCamelCase , decoder_noise_time=lowerCamelCase )
return logits
@torch.no_grad()
def __call__( self : Optional[int] , lowerCamelCase : List[List[int]] , lowerCamelCase : Optional[torch.Generator] = None , lowerCamelCase : int = 100 , lowerCamelCase : bool = True , lowerCamelCase : str = "numpy" , lowerCamelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase : int = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowerCamelCase , lowerCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(lowerCamelCase )}.""" )
_UpperCAmelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
_UpperCAmelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
_UpperCAmelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=lowerCamelCase , device=self.device )
for i, encoder_input_tokens in enumerate(lowerCamelCase ):
if i == 0:
_UpperCAmelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
_UpperCAmelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=lowerCamelCase , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
_UpperCAmelCase = ones
_UpperCAmelCase = self.scale_features(
lowerCamelCase , output_range=[-1.0, 1.0] , clip=lowerCamelCase )
_UpperCAmelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=lowerCamelCase , continuous_mask=lowerCamelCase , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
_UpperCAmelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=lowerCamelCase , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(lowerCamelCase )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
_UpperCAmelCase = self.decode(
encodings_and_masks=lowerCamelCase , input_tokens=lowerCamelCase , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , generator=lowerCamelCase ).prev_sample
_UpperCAmelCase = self.scale_to_features(lowerCamelCase , input_range=[-1.0, 1.0] )
_UpperCAmelCase = mel[:1]
_UpperCAmelCase = mel.cpu().float().numpy()
_UpperCAmelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowerCamelCase , lowerCamelCase )
logger.info("""Generated segment""" , lowerCamelCase )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
_UpperCAmelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
_UpperCAmelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=lowerCamelCase )
| 108
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str ={
'''7z''': (seven_zip_file, SevenZipExtractor),
'''bz2''': (bza_file, BzipaExtractor),
'''gzip''': (gz_file, GzipExtractor),
'''lz4''': (lza_file, LzaExtractor),
'''tar''': (tar_file, TarExtractor),
'''xz''': (xz_file, XzExtractor),
'''zip''': (zip_file, ZipExtractor),
'''zstd''': (zstd_file, ZstdExtractor),
}
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] =input_paths_and_base_extractors[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE : Union[str, Any] =f'for \'{compression_format}\' compression_format, '
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__a )
assert base_extractor.is_extractable(__a )
SCREAMING_SNAKE_CASE : Tuple =tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
base_extractor.extract(__a , __a )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE : List[str] =file_path.read_text(encoding='''utf-8''' )
else:
SCREAMING_SNAKE_CASE : Dict =output_path.read_text(encoding='''utf-8''' )
SCREAMING_SNAKE_CASE : Optional[int] =text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'''compression_format, is_archive''' , [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
] , )
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] ={
'''7z''': seven_zip_file,
'''bz2''': bza_file,
'''gzip''': gz_file,
'''lz4''': lza_file,
'''tar''': tar_file,
'''xz''': xz_file,
'''zip''': zip_file,
'''zstd''': zstd_file,
}
SCREAMING_SNAKE_CASE : Any =input_paths[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE : int =f'for \'{compression_format}\' compression_format, '
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__a )
SCREAMING_SNAKE_CASE : int =Extractor.infer_extractor_format(__a )
assert extractor_format is not None
SCREAMING_SNAKE_CASE : List[str] =tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
Extractor.extract(__a , __a , __a )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE : Any =file_path.read_text(encoding='''utf-8''' )
else:
SCREAMING_SNAKE_CASE : List[str] =output_path.read_text(encoding='''utf-8''' )
SCREAMING_SNAKE_CASE : List[str] =text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def lowerCAmelCase_ ( __a , __a ) -> Tuple:
"""simple docstring"""
import tarfile
SCREAMING_SNAKE_CASE : Any =tmp_path / '''data_dot_dot'''
directory.mkdir()
SCREAMING_SNAKE_CASE : Tuple =directory / '''tar_file_with_dot_dot.tar'''
with tarfile.TarFile(__a , '''w''' ) as f:
f.add(__a , arcname=os.path.join('''..''' , text_file.name ) )
return path
@pytest.fixture
def lowerCAmelCase_ ( __a ) -> List[str]:
"""simple docstring"""
import tarfile
SCREAMING_SNAKE_CASE : List[str] =tmp_path / '''data_sym_link'''
directory.mkdir()
SCREAMING_SNAKE_CASE : Tuple =directory / '''tar_file_with_sym_link.tar'''
os.symlink('''..''' , directory / '''subdir''' , target_is_directory=__a )
with tarfile.TarFile(__a , '''w''' ) as f:
f.add(str(directory / '''subdir''' ) , arcname='''subdir''' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'''insecure_tar_file, error_log''' , [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')] , )
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , __a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int ={
'''tar_file_with_dot_dot''': tar_file_with_dot_dot,
'''tar_file_with_sym_link''': tar_file_with_sym_link,
}
SCREAMING_SNAKE_CASE : Any =insecure_tar_files[insecure_tar_file]
SCREAMING_SNAKE_CASE : Optional[Any] =tmp_path / '''extracted'''
TarExtractor.extract(__a , __a )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def lowerCAmelCase_ ( __a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] =tmpdir / '''not_a_zip_file'''
# From: https://github.com/python/cpython/pull/5053
SCREAMING_SNAKE_CASE : List[Any] =(
b'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
b'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
b'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
b'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
)
with not_a_zip_file.open('''wb''' ) as f:
f.write(__a )
assert zipfile.is_zipfile(str(__a ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(__a ) # but we're right
| 258
| 0
|
'''simple docstring'''
def _a ( a : int = 100_0000 ):
_SCREAMING_SNAKE_CASE = set(range(3 , _SCREAMING_SNAKE_CASE , 2 ) )
primes.add(2 )
for p in range(3 , _SCREAMING_SNAKE_CASE , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) )
_SCREAMING_SNAKE_CASE = [float(_SCREAMING_SNAKE_CASE ) for n in range(limit + 1 )]
for p in primes:
for n in range(_SCREAMING_SNAKE_CASE , limit + 1 , _SCREAMING_SNAKE_CASE ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 716
|
'''simple docstring'''
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class lowerCAmelCase :
def lowercase ( self ):
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.00_01 , beta_end=0.02 , thresholding=UpperCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def lowercase ( self ):
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.4_14 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.00_01 , beta_end=0.02 , thresholding=UpperCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.00_01 , beta_end=0.02 , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def lowercase ( self ):
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCamelCase )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCamelCase )
_SCREAMING_SNAKE_CASE = inputs["prompt"]
_SCREAMING_SNAKE_CASE = inputs["generator"]
_SCREAMING_SNAKE_CASE = inputs["num_inference_steps"]
_SCREAMING_SNAKE_CASE = inputs["output_type"]
if "image" in inputs:
_SCREAMING_SNAKE_CASE = inputs["image"]
else:
_SCREAMING_SNAKE_CASE = None
if "mask_image" in inputs:
_SCREAMING_SNAKE_CASE = inputs["mask_image"]
else:
_SCREAMING_SNAKE_CASE = None
if "original_image" in inputs:
_SCREAMING_SNAKE_CASE = inputs["original_image"]
else:
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = pipe.encode_prompt(UpperCamelCase )
# inputs with prompt converted to embeddings
_SCREAMING_SNAKE_CASE = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
_SCREAMING_SNAKE_CASE = image
if mask_image is not None:
_SCREAMING_SNAKE_CASE = mask_image
if original_image is not None:
_SCREAMING_SNAKE_CASE = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(UpperCamelCase , UpperCamelCase , UpperCamelCase )
_SCREAMING_SNAKE_CASE = pipe(**UpperCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCamelCase )
_SCREAMING_SNAKE_CASE = self.pipeline_class.from_pretrained(UpperCamelCase )
pipe_loaded.to(UpperCamelCase )
pipe_loaded.set_progress_bar_config(disable=UpperCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(UpperCamelCase , UpperCamelCase ) is None , F'`{optional_component}` did not stay set to None after loading.' , )
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCamelCase )
_SCREAMING_SNAKE_CASE = inputs["generator"]
_SCREAMING_SNAKE_CASE = inputs["num_inference_steps"]
_SCREAMING_SNAKE_CASE = inputs["output_type"]
# inputs with prompt converted to embeddings
_SCREAMING_SNAKE_CASE = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
_SCREAMING_SNAKE_CASE = image
if mask_image is not None:
_SCREAMING_SNAKE_CASE = mask_image
if original_image is not None:
_SCREAMING_SNAKE_CASE = original_image
_SCREAMING_SNAKE_CASE = pipe_loaded(**UpperCamelCase )[0]
_SCREAMING_SNAKE_CASE = np.abs(to_np(UpperCamelCase ) - to_np(UpperCamelCase ) ).max()
self.assertLess(UpperCamelCase , 1e-4 )
def lowercase ( self ):
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCamelCase )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCamelCase )
_SCREAMING_SNAKE_CASE = pipe(**UpperCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCamelCase )
_SCREAMING_SNAKE_CASE = self.pipeline_class.from_pretrained(UpperCamelCase )
pipe_loaded.to(UpperCamelCase )
pipe_loaded.set_progress_bar_config(disable=UpperCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCamelCase )
_SCREAMING_SNAKE_CASE = pipe_loaded(**UpperCamelCase )[0]
_SCREAMING_SNAKE_CASE = np.abs(to_np(UpperCamelCase ) - to_np(UpperCamelCase ) ).max()
self.assertLess(UpperCamelCase , 1e-4 )
| 493
| 0
|
'''simple docstring'''
from collections.abc import Callable
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
lowercase__ : float = a
lowercase__ : float = b
if function(UpperCAmelCase ) == 0: # one of the a or b is a root for the function
return a
elif function(UpperCAmelCase ) == 0:
return b
elif (
function(UpperCAmelCase ) * function(UpperCAmelCase ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('''could not find root in given interval.''' )
else:
lowercase__ : float = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(UpperCAmelCase ) == 0:
return mid
elif function(UpperCAmelCase ) * function(UpperCAmelCase ) < 0:
lowercase__ : Any = mid
else:
lowercase__ : Optional[Any] = mid
lowercase__ : Tuple = start + (end - start) / 2.0
return mid
def __UpperCamelCase ( UpperCAmelCase ):
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 10_00))
import doctest
doctest.testmod()
| 152
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Optional[Any] = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a: Dict = [
"""BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BigBirdPegasusForCausalLM""",
"""BigBirdPegasusForConditionalGeneration""",
"""BigBirdPegasusForQuestionAnswering""",
"""BigBirdPegasusForSequenceClassification""",
"""BigBirdPegasusModel""",
"""BigBirdPegasusPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
__a: int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 152
| 1
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
UpperCamelCase = None
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
UpperCamelCase = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
UpperCamelCase = {
'''albert-base-v1''': 5_1_2,
'''albert-large-v1''': 5_1_2,
'''albert-xlarge-v1''': 5_1_2,
'''albert-xxlarge-v1''': 5_1_2,
'''albert-base-v2''': 5_1_2,
'''albert-large-v2''': 5_1_2,
'''albert-xlarge-v2''': 5_1_2,
'''albert-xxlarge-v2''': 5_1_2,
}
UpperCamelCase = '''▁'''
class lowerCamelCase__ ( _a ):
lowerCamelCase_ : Optional[int] = VOCAB_FILES_NAMES
lowerCamelCase_ : Any = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ : List[Any] = AlbertTokenizer
def __init__(self : Union[str, Any] , _snake_case : Optional[int]=None , _snake_case : Union[str, Any]=None , _snake_case : List[str]=True , _snake_case : Optional[int]=True , _snake_case : int=False , _snake_case : List[str]="[CLS]" , _snake_case : str="[SEP]" , _snake_case : List[str]="<unk>" , _snake_case : Any="[SEP]" , _snake_case : Tuple="<pad>" , _snake_case : Optional[int]="[CLS]" , _snake_case : Tuple="[MASK]" , **_snake_case : List[str] , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ : Dict = (
AddedToken(_A , lstrip=_A , rstrip=_A , normalized=_A )
if isinstance(_A , _A )
else mask_token
)
super().__init__(
_A , tokenizer_file=_A , do_lower_case=_A , remove_space=_A , keep_accents=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , pad_token=_A , cls_token=_A , mask_token=_A , **_A , )
lowerCamelCase_ : List[str] = do_lower_case
lowerCamelCase_ : Optional[int] = remove_space
lowerCamelCase_ : List[str] = keep_accents
lowerCamelCase_ : Union[str, Any] = vocab_file
lowerCamelCase_ : Dict = False if not self.vocab_file else True
def UpperCAmelCase_ (self : Optional[int] , _snake_case : Optional[Any] , _snake_case : Any = None ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ : Union[str, Any] = [self.sep_token_id]
lowerCamelCase_ : Dict = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase_ (self : str , _snake_case : Optional[int] , _snake_case : Optional[Any] = None ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ : Dict = [self.sep_token_id]
lowerCamelCase_ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ (self : List[str] , _snake_case : Optional[int] , _snake_case : Dict = None ) -> int:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_A ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCamelCase_ : Any = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ):
copyfile(self.vocab_file , _A )
return (out_vocab_file,)
| 714
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 144
| 0
|
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : int = []
embed.append(
(
f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
f"stage{idx}.patch_embed.proj.weight",
) )
embed.append(
(
f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
f"stage{idx}.patch_embed.proj.bias",
) )
embed.append(
(
f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
f"stage{idx}.patch_embed.norm.weight",
) )
embed.append(
(
f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
f"stage{idx}.patch_embed.norm.bias",
) )
return embed
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Tuple = []
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
f"stage{idx}.blocks.{cnt}.attn.proj.weight",
) )
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
f"stage{idx}.blocks.{cnt}.attn.proj.bias",
) )
attention_weights.append(
(f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight") )
attention_weights.append(
(f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias") )
attention_weights.append(
(f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight") )
attention_weights.append(
(f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias") )
attention_weights.append(
(f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight") )
attention_weights.append(
(f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias") )
attention_weights.append(
(f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight") )
attention_weights.append(
(f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias") )
return attention_weights
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
'''simple docstring'''
__UpperCAmelCase : List[Any] = []
token.append((f"cvt.encoder.stages.{idx}.cls_token", '''stage2.cls_token''') )
return token
def __SCREAMING_SNAKE_CASE ( ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Dict = []
head.append(('''layernorm.weight''', '''norm.weight''') )
head.append(('''layernorm.bias''', '''norm.bias''') )
head.append(('''classifier.weight''', '''head.weight''') )
head.append(('''classifier.bias''', '''head.bias''') )
return head
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Tuple = '''imagenet-1k-id2label.json'''
__UpperCAmelCase : List[str] = 1000
__UpperCAmelCase : Tuple = '''huggingface/label-files'''
__UpperCAmelCase : Tuple = num_labels
__UpperCAmelCase : int = json.load(open(cached_download(hf_hub_url(lowercase_ , lowercase_ , repo_type='''dataset''' ) ) , '''r''' ) )
__UpperCAmelCase : Optional[Any] = {int(lowercase_ ): v for k, v in idalabel.items()}
__UpperCAmelCase : Dict = idalabel
__UpperCAmelCase : Optional[Any] = {v: k for k, v in idalabel.items()}
__UpperCAmelCase : Optional[Any] = CvtConfig(num_labels=lowercase_ , idalabel=lowercase_ , labelaid=lowercase_ )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "13":
__UpperCAmelCase : Any = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "21":
__UpperCAmelCase : Any = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__UpperCAmelCase : Optional[int] = [2, 2, 20]
__UpperCAmelCase : str = [3, 12, 16]
__UpperCAmelCase : str = [192, 768, 1024]
__UpperCAmelCase : Optional[int] = CvtForImageClassification(lowercase_ )
__UpperCAmelCase : Tuple = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
__UpperCAmelCase : Optional[Any] = image_size
__UpperCAmelCase : Any = torch.load(lowercase_ , map_location=torch.device('''cpu''' ) )
__UpperCAmelCase : List[Any] = OrderedDict()
__UpperCAmelCase : Optional[Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__UpperCAmelCase : List[Any] = list_of_state_dict + cls_token(lowercase_ )
__UpperCAmelCase : Any = list_of_state_dict + embeddings(lowercase_ )
for cnt in range(config.depth[idx] ):
__UpperCAmelCase : List[str] = list_of_state_dict + attention(lowercase_ , lowercase_ )
__UpperCAmelCase : List[Any] = list_of_state_dict + final()
for gg in list_of_state_dict:
print(lowercase_ )
for i in range(len(lowercase_ ) ):
__UpperCAmelCase : Dict = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(lowercase_ )
model.save_pretrained(lowercase_ )
image_processor.save_pretrained(lowercase_ )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--cvt_model""",
default="""cvt-w24""",
type=str,
help="""Name of the cvt model you'd like to convert.""",
)
parser.add_argument(
"""--image_size""",
default=384,
type=int,
help="""Input Image Size""",
)
parser.add_argument(
"""--cvt_file_name""",
default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
type=str,
help="""Input Image Size""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
lowerCAmelCase = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 462
|
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class lowerCamelCase ( _UpperCamelCase , unittest.TestCase ):
_lowerCAmelCase : Union[str, Any] = XLNetTokenizer
_lowerCAmelCase : int = XLNetTokenizerFast
_lowerCAmelCase : Optional[Any] = True
_lowerCAmelCase : Optional[Any] = True
def A( self):
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Optional[Any] = XLNetTokenizer(lowercase__ , keep_accents=lowercase__)
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname)
def A( self):
__UpperCAmelCase : Tuple = '''<s>'''
__UpperCAmelCase : List[str] = 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):
__UpperCAmelCase : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '''<unk>''')
self.assertEqual(vocab_keys[1] , '''<s>''')
self.assertEqual(vocab_keys[-1] , '''<eod>''')
self.assertEqual(len(lowercase__) , 1_0_0_6)
def A( self):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0)
def A( self):
__UpperCAmelCase : List[str] = XLNetTokenizer(lowercase__ , keep_accents=lowercase__)
__UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''This is a test''')
self.assertListEqual(lowercase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2])
__UpperCAmelCase : Tuple = 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 : Union[str, Any] = tokenizer.convert_tokens_to_ids(lowercase__)
self.assertListEqual(lowercase__ , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4])
__UpperCAmelCase : int = tokenizer.convert_ids_to_tokens(lowercase__)
self.assertListEqual(
lowercase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def A( self):
__UpperCAmelCase : str = XLNetTokenizer(lowercase__ , do_lower_case=lowercase__)
__UpperCAmelCase : int = 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''',
'''se''',
'''.''',
] , )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''') , ['''▁he''', '''ll''', '''o'''])
def A( self):
__UpperCAmelCase : Optional[Any] = XLNetTokenizer(lowercase__ , do_lower_case=lowercase__)
__UpperCAmelCase : Union[str, Any] = 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''',
'''se''',
'''.''',
] , )
@slow
def A( self):
__UpperCAmelCase : Tuple = XLNetTokenizer.from_pretrained('''xlnet-base-cased''')
__UpperCAmelCase : Optional[int] = tokenizer.encode('''sequence builders''' , add_special_tokens=lowercase__)
__UpperCAmelCase : Optional[int] = tokenizer.encode('''multi-sequence build''' , add_special_tokens=lowercase__)
__UpperCAmelCase : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowercase__)
__UpperCAmelCase : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowercase__ , lowercase__)
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def A( self):
# fmt: off
__UpperCAmelCase : Optional[Any] = {'''input_ids''': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase__ , model_name='''xlnet-base-cased''' , revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''' , )
| 462
| 1
|
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :int = "upernet"
def __init__( self : Optional[Any] , __a : Optional[Any]=None , __a : List[str]=512 , __a : List[Any]=0.02 , __a : Any=[1, 2, 3, 6] , __a : int=True , __a : Optional[Any]=0.4 , __a : List[Any]=384 , __a : Optional[Any]=256 , __a : Dict=1 , __a : Optional[int]=False , __a : List[str]=255 , **__a : Optional[Any] , ) -> Optional[int]:
super().__init__(**UpperCAmelCase__ )
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
_UpperCamelCase : int = CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"] )
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
_UpperCamelCase : Union[str, Any] = backbone_config.get("model_type" )
_UpperCamelCase : Dict = CONFIG_MAPPING[backbone_model_type]
_UpperCamelCase : int = config_class.from_dict(UpperCAmelCase__ )
_UpperCamelCase : List[str] = backbone_config
_UpperCamelCase : int = hidden_size
_UpperCamelCase : Optional[int] = initializer_range
_UpperCamelCase : str = pool_scales
_UpperCamelCase : List[Any] = use_auxiliary_head
_UpperCamelCase : List[str] = auxiliary_loss_weight
_UpperCamelCase : Optional[Any] = auxiliary_in_channels
_UpperCamelCase : Dict = auxiliary_channels
_UpperCamelCase : List[Any] = auxiliary_num_convs
_UpperCamelCase : Tuple = auxiliary_concat_input
_UpperCamelCase : Optional[int] = loss_ignore_index
def __SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Tuple:
_UpperCamelCase : Tuple = copy.deepcopy(self.__dict__ )
_UpperCamelCase : Dict = self.backbone_config.to_dict()
_UpperCamelCase : int = self.__class__.model_type
return output
| 721
|
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = ["model.decoder.embed_positions.weights"]
def lowercase__ ( lowercase_ ) -> Optional[Any]:
"""simple docstring"""
if "emb" in name:
_UpperCamelCase : List[str] = name.replace("emb" ,"model.decoder.embed_tokens" )
if "transformer" in name:
_UpperCamelCase : Optional[int] = name.replace("transformer" ,"model.decoder" )
if "cross_attention" in name:
_UpperCamelCase : Optional[int] = name.replace("cross_attention" ,"encoder_attn" )
if "linear1" in name:
_UpperCamelCase : Optional[Any] = name.replace("linear1" ,"fc1" )
if "linear2" in name:
_UpperCamelCase : Union[str, Any] = name.replace("linear2" ,"fc2" )
if "norm1" in name:
_UpperCamelCase : Optional[Any] = name.replace("norm1" ,"self_attn_layer_norm" )
if "norm_cross" in name:
_UpperCamelCase : Dict = name.replace("norm_cross" ,"encoder_attn_layer_norm" )
if "norm2" in name:
_UpperCamelCase : Union[str, Any] = name.replace("norm2" ,"final_layer_norm" )
if "out_norm" in name:
_UpperCamelCase : Union[str, Any] = name.replace("out_norm" ,"model.decoder.layer_norm" )
if "linears" in name:
_UpperCamelCase : List[str] = name.replace("linears" ,"lm_heads" )
if "condition_provider.conditioners.description.output_proj" in name:
_UpperCamelCase : Any = name.replace("condition_provider.conditioners.description.output_proj" ,"enc_to_dec_proj" )
return name
def lowercase__ ( lowercase_ ,lowercase_ ) -> Tuple[Dict, Dict]:
"""simple docstring"""
_UpperCamelCase : str = list(state_dict.keys() )
_UpperCamelCase : Optional[Any] = {}
for key in keys:
_UpperCamelCase : Optional[int] = state_dict.pop(lowercase_ )
_UpperCamelCase : List[Any] = rename_keys(lowercase_ )
if "in_proj_weight" in key:
# split fused qkv proj
_UpperCamelCase : Tuple = val[:hidden_size, :]
_UpperCamelCase : Optional[Any] = val[hidden_size : 2 * hidden_size, :]
_UpperCamelCase : Optional[Any] = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
_UpperCamelCase : Optional[Any] = val
else:
_UpperCamelCase : List[str] = val
return state_dict, enc_dec_proj_state_dict
def lowercase__ ( lowercase_ ) -> MusicgenDecoderConfig:
"""simple docstring"""
if checkpoint == "small":
# default config values
_UpperCamelCase : List[Any] = 1_024
_UpperCamelCase : List[str] = 24
_UpperCamelCase : Any = 16
elif checkpoint == "medium":
_UpperCamelCase : Tuple = 1_536
_UpperCamelCase : Dict = 48
_UpperCamelCase : Tuple = 24
elif checkpoint == "large":
_UpperCamelCase : int = 2_048
_UpperCamelCase : Optional[int] = 48
_UpperCamelCase : Dict = 32
else:
raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
_UpperCamelCase : str = MusicgenDecoderConfig(
hidden_size=lowercase_ ,ffn_dim=hidden_size * 4 ,num_hidden_layers=lowercase_ ,num_attention_heads=lowercase_ ,)
return config
@torch.no_grad()
def lowercase__ ( lowercase_ ,lowercase_=None ,lowercase_=None ,lowercase_="cpu" ) -> List[str]:
"""simple docstring"""
_UpperCamelCase : str = MusicGen.get_pretrained(lowercase_ ,device=lowercase_ )
_UpperCamelCase : Union[str, Any] = decoder_config_from_checkpoint(lowercase_ )
_UpperCamelCase : Optional[int] = fairseq_model.lm.state_dict()
_UpperCamelCase, _UpperCamelCase : Optional[Any] = rename_state_dict(
lowercase_ ,hidden_size=decoder_config.hidden_size )
_UpperCamelCase : Tuple = TaEncoderModel.from_pretrained("t5-base" )
_UpperCamelCase : Union[str, Any] = EncodecModel.from_pretrained("facebook/encodec_32khz" )
_UpperCamelCase : str = MusicgenForCausalLM(lowercase_ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
_UpperCamelCase, _UpperCamelCase : str = decoder.load_state_dict(lowercase_ ,strict=lowercase_ )
for key in missing_keys.copy():
if key.startswith(("text_encoder", "audio_encoder") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowercase_ )
if len(lowercase_ ) > 0:
raise ValueError(F'''Missing key(s) in state_dict: {missing_keys}''' )
if len(lowercase_ ) > 0:
raise ValueError(F'''Unexpected key(s) in state_dict: {unexpected_keys}''' )
# init the composite model
_UpperCamelCase : str = MusicgenForConditionalGeneration(text_encoder=lowercase_ ,audio_encoder=lowercase_ ,decoder=lowercase_ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowercase_ )
# check we can do a forward pass
_UpperCamelCase : List[str] = torch.arange(0 ,8 ,dtype=torch.long ).reshape(2 ,-1 )
_UpperCamelCase : Dict = input_ids.reshape(2 * 4 ,-1 )
with torch.no_grad():
_UpperCamelCase : Tuple = model(input_ids=lowercase_ ,decoder_input_ids=lowercase_ ).logits
if logits.shape != (8, 1, 2_048):
raise ValueError("Incorrect shape for logits" )
# now construct the processor
_UpperCamelCase : int = AutoTokenizer.from_pretrained("t5-base" )
_UpperCamelCase : str = AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz" ,padding_side="left" )
_UpperCamelCase : Optional[int] = MusicgenProcessor(feature_extractor=lowercase_ ,tokenizer=lowercase_ )
# set the appropriate bos/pad token ids
_UpperCamelCase : str = 2_048
_UpperCamelCase : str = 2_048
# set other default generation config params
_UpperCamelCase : Optional[Any] = int(30 * audio_encoder.config.frame_rate )
_UpperCamelCase : List[str] = True
_UpperCamelCase : int = 3.0
if pytorch_dump_folder is not None:
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
logger.info(F'''Saving model {checkpoint} to {pytorch_dump_folder}''' )
model.save_pretrained(lowercase_ )
processor.save_pretrained(lowercase_ )
if repo_id:
logger.info(F'''Pushing model {checkpoint} to {repo_id}''' )
model.push_to_hub(lowercase_ )
processor.push_to_hub(lowercase_ )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
lowerCamelCase__ = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 51
| 0
|
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__ :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : str=3 , lowerCamelCase__ : Dict=32 , lowerCamelCase__ : Dict=3 , lowerCamelCase__ : str=10 , lowerCamelCase__ : Tuple=[10, 20, 30, 40] , lowerCamelCase__ : Tuple=[1, 1, 2, 1] , lowerCamelCase__ : str=True , lowerCamelCase__ : List[str]=True , lowerCamelCase__ : Tuple="relu" , lowerCamelCase__ : List[str]=3 , lowerCamelCase__ : int=None , ):
a__ : Optional[Any] = parent
a__ : Optional[int] = batch_size
a__ : Union[str, Any] = image_size
a__ : Dict = num_channels
a__ : Any = embeddings_size
a__ : int = hidden_sizes
a__ : Optional[int] = depths
a__ : List[str] = is_training
a__ : Dict = use_labels
a__ : int = hidden_act
a__ : Tuple = num_labels
a__ : Tuple = scope
a__ : str = len(lowerCamelCase__ )
def _UpperCamelCase( self : int ):
a__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a__ : Union[str, Any] = None
if self.use_labels:
a__ : Dict = ids_tensor([self.batch_size] , self.num_labels )
a__ : Any = self.get_config()
return config, pixel_values, labels
def _UpperCamelCase( self : Optional[Any] ):
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _UpperCamelCase( self : Any , lowerCamelCase__ : Any , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Any ):
a__ : str = TFResNetModel(config=lowerCamelCase__ )
a__ : Union[str, Any] = model(lowerCamelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _UpperCamelCase( self : int , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Optional[int] ):
a__ : List[str] = self.num_labels
a__ : Any = TFResNetForImageClassification(lowerCamelCase__ )
a__ : Dict = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCamelCase( self : Optional[Any] ):
a__ : Any = self.prepare_config_and_inputs()
a__, a__, a__ : Union[str, Any] = config_and_inputs
a__ : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class A__ ( A__ , A__ , unittest.TestCase ):
"""simple docstring"""
_lowercase = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
_lowercase = (
{'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
if is_tf_available()
else {}
)
_lowercase = False
_lowercase = False
_lowercase = False
_lowercase = False
_lowercase = False
def _UpperCamelCase( self : Any ):
a__ : Tuple = TFResNetModelTester(self )
a__ : Any = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def _UpperCamelCase( self : int ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCamelCase( self : Any ):
return
@unittest.skip(reason="ResNet does not use inputs_embeds" )
def _UpperCamelCase( self : Optional[Any] ):
pass
@unittest.skip(reason="ResNet does not support input and output embeddings" )
def _UpperCamelCase( self : Any ):
pass
def _UpperCamelCase( self : Union[str, Any] ):
a__, a__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : Tuple = model_class(lowerCamelCase__ )
a__ : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ : Tuple = [*signature.parameters.keys()]
a__ : str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def _UpperCamelCase( self : List[Any] ):
a__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def _UpperCamelCase( self : int ):
def check_hidden_states_output(lowerCamelCase__ : Any , lowerCamelCase__ : Any , lowerCamelCase__ : str ):
a__ : Optional[int] = model_class(lowerCamelCase__ )
a__ : Optional[int] = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
a__ : str = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a__ : str = self.model_tester.num_stages
self.assertEqual(len(lowerCamelCase__ ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
a__, a__ : str = self.model_tester.prepare_config_and_inputs_for_common()
a__ : str = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
a__ : Optional[int] = layer_type
a__ : List[str] = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a__ : List[Any] = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def _UpperCamelCase( self : Optional[Any] ):
a__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@slow
def _UpperCamelCase( self : Dict ):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : List[str] = TFResNetModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def UpperCamelCase_ ( ) -> Tuple:
a__ : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class A__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _UpperCamelCase( self : int ):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _UpperCamelCase( self : Dict ):
a__ : List[str] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
a__ : Union[str, Any] = self.default_image_processor
a__ : Optional[Any] = prepare_img()
a__ : Dict = image_processor(images=lowerCamelCase__ , return_tensors="tf" )
# forward pass
a__ : Optional[int] = model(**lowerCamelCase__ )
# verify the logits
a__ : Union[str, Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
a__ : Union[str, Any] = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowerCamelCase__ , atol=1E-4 ) )
| 37
|
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
UpperCamelCase : Optional[int] = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def UpperCamelCase_ ( __a ) -> Any:
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def UpperCamelCase_ ( __a , __a , __a ) -> Any:
return max(metric_fn(__a , __a ) for gt in ground_truths )
def UpperCamelCase_ ( __a , __a , __a ) -> List[str]:
a__ : Tuple = [line.strip() for line in open(__a , "r" ).readlines()]
a__ : Tuple = []
if args.gold_data_mode == "qa":
a__ : Any = pd.read_csv(__a , sep="\t" , header=__a )
for answer_list in data[1]:
a__ : Union[str, Any] = ast.literal_eval(__a )
answers.append(__a )
else:
a__ : List[str] = [line.strip() for line in open(__a , "r" ).readlines()]
a__ : List[str] = [[reference] for reference in references]
a__ : List[str] = 0
for prediction, ground_truths in zip(__a , __a ):
total += 1
em += metric_max_over_ground_truths(__a , __a , __a )
fa += metric_max_over_ground_truths(__a , __a , __a )
a__ : Dict = 100.0 * em / total
a__ : Optional[Any] = 100.0 * fa / total
logger.info(f'''F1: {fa:.2f}''' )
logger.info(f'''EM: {em:.2f}''' )
def UpperCamelCase_ ( __a , __a , __a ) -> Optional[Any]:
a__ : Optional[Any] = args.k
a__ : str = [line.strip() for line in open(__a , "r" ).readlines()]
a__ : Tuple = [line.strip() for line in open(__a , "r" ).readlines()]
a__ : Tuple = 0
for hypo, reference in zip(__a , __a ):
a__ : Any = set(hypo.split("\t" )[:k] )
a__ : Union[str, Any] = set(reference.split("\t" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
a__ : Union[str, Any] = 100.0 * em / total
logger.info(f'''Precision@{k}: {em: .2f}''' )
def UpperCamelCase_ ( __a , __a , __a ) -> Optional[Any]:
def strip_title(__a ):
if title.startswith("\"" ):
a__ : Optional[Any] = title[1:]
if title.endswith("\"" ):
a__ : Union[str, Any] = title[:-1]
return title
a__ : Optional[int] = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__a , return_tensors="pt" , padding=__a , truncation=__a , )["input_ids"].to(args.device )
a__ : Optional[int] = rag_model.rag.question_encoder(__a )
a__ : Union[str, Any] = question_enc_outputs[0]
a__ : Optional[int] = rag_model.retriever(
__a , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors="pt" , )
a__ : List[Any] = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
a__ : int = []
for docs in all_docs:
a__ : Optional[int] = [strip_title(__a ) for title in docs["title"]]
provenance_strings.append("\t".join(__a ) )
return provenance_strings
def UpperCamelCase_ ( __a , __a , __a ) -> Dict:
with torch.no_grad():
a__ : Optional[int] = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__a , return_tensors="pt" , padding=__a , truncation=__a )
a__ : Any = inputs_dict.input_ids.to(args.device )
a__ : Dict = inputs_dict.attention_mask.to(args.device )
a__ : Optional[int] = rag_model.generate( # rag_model overwrites generate
__a , attention_mask=__a , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=__a , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
a__ : int = rag_model.retriever.generator_tokenizer.batch_decode(__a , skip_special_tokens=__a )
if args.print_predictions:
for q, a in zip(__a , __a ):
logger.info("Q: {} - A: {}".format(__a , __a ) )
return answers
def UpperCamelCase_ ( ) -> List[str]:
a__ : int = argparse.ArgumentParser()
parser.add_argument(
"--model_type" , choices=["rag_sequence", "rag_token", "bart"] , type=__a , help=(
"RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
" model_name_or_path"
) , )
parser.add_argument(
"--index_name" , default=__a , choices=["exact", "compressed", "legacy"] , type=__a , help="RAG model retriever type" , )
parser.add_argument(
"--index_path" , default=__a , type=__a , help="Path to the retrieval index" , )
parser.add_argument("--n_docs" , default=5 , type=__a , help="Number of retrieved docs" )
parser.add_argument(
"--model_name_or_path" , default=__a , type=__a , required=__a , help="Path to pretrained checkpoints or model identifier from huggingface.co/models" , )
parser.add_argument(
"--eval_mode" , choices=["e2e", "retrieval"] , default="e2e" , type=__a , help=(
"Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
" precision@k."
) , )
parser.add_argument("--k" , default=1 , type=__a , help="k for the precision@k calculation" )
parser.add_argument(
"--evaluation_set" , default=__a , type=__a , required=__a , help="Path to a file containing evaluation samples" , )
parser.add_argument(
"--gold_data_path" , default=__a , type=__a , required=__a , help="Path to a tab-separated file with gold samples" , )
parser.add_argument(
"--gold_data_mode" , default="qa" , type=__a , choices=["qa", "ans"] , help=(
"Format of the gold data file"
"qa - a single line in the following format: question [tab] answer_list"
"ans - a single line of the gold file contains the expected answer string"
) , )
parser.add_argument(
"--predictions_path" , type=__a , default="predictions.txt" , help="Name of the predictions file, to be stored in the checkpoints directory" , )
parser.add_argument(
"--eval_all_checkpoints" , action="store_true" , help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number" , )
parser.add_argument(
"--eval_batch_size" , default=8 , type=__a , help="Batch size per GPU/CPU for evaluation." , )
parser.add_argument(
"--recalculate" , help="Recalculate predictions even if the prediction file exists" , action="store_true" , )
parser.add_argument(
"--num_beams" , default=4 , type=__a , help="Number of beams to be used when generating answers" , )
parser.add_argument("--min_length" , default=1 , type=__a , help="Min length of the generated answers" )
parser.add_argument("--max_length" , default=50 , type=__a , help="Max length of the generated answers" )
parser.add_argument(
"--print_predictions" , action="store_true" , help="If True, prints predictions while evaluating." , )
parser.add_argument(
"--print_docs" , action="store_true" , help="If True, prints docs retried while generating." , )
a__ : int = parser.parse_args()
a__ : Dict = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
return args
def UpperCamelCase_ ( __a ) -> Optional[int]:
a__ : Tuple = {}
if args.model_type is None:
a__ : List[str] = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("rag" ):
a__ : int = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
a__ : Tuple = args.n_docs
if args.index_name is not None:
a__ : Any = args.index_name
if args.index_path is not None:
a__ : int = args.index_path
else:
a__ : Optional[Any] = BartForConditionalGeneration
a__ : Tuple = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("Evaluate the following checkpoints: %s" , __a )
a__ : Any = get_scores if args.eval_mode == "e2e" else get_precision_at_k
a__ : Union[str, Any] = evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path ) )
score_fn(__a , args.predictions_path , args.gold_data_path )
continue
logger.info("***** Running evaluation for {} *****".format(__a ) )
logger.info(" Batch size = %d" , args.eval_batch_size )
logger.info(" Predictions will be stored under {}".format(args.predictions_path ) )
if args.model_type.startswith("rag" ):
a__ : str = RagRetriever.from_pretrained(__a , **__a )
a__ : Optional[int] = model_class.from_pretrained(__a , retriever=__a , **__a )
model.retriever.init_retrieval()
else:
a__ : Dict = model_class.from_pretrained(__a , **__a )
model.to(args.device )
with open(args.evaluation_set , "r" ) as eval_file, open(args.predictions_path , "w" ) as preds_file:
a__ : List[Any] = []
for line in tqdm(__a ):
questions.append(line.strip() )
if len(__a ) == args.eval_batch_size:
a__ : Union[str, Any] = evaluate_batch_fn(__a , __a , __a )
preds_file.write("\n".join(__a ) + "\n" )
preds_file.flush()
a__ : Any = []
if len(__a ) > 0:
a__ : List[str] = evaluate_batch_fn(__a , __a , __a )
preds_file.write("\n".join(__a ) )
preds_file.flush()
score_fn(__a , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
UpperCamelCase : List[Any] = get_args()
main(args)
| 37
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowercase = logging.get_logger(__name__)
def _lowerCAmelCase ( __lowerCamelCase:Union[tf.Tensor, np.ndarray] ):
'''simple docstring'''
if isinstance(__lowerCamelCase , np.ndarray ):
return list(tensor.shape )
__magic_name__ = tf.shape(__lowerCamelCase )
if tensor.shape == tf.TensorShape(__lowerCamelCase ):
return dynamic
__magic_name__ = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase )]
def _lowerCAmelCase ( __lowerCamelCase:tf.Tensor , __lowerCamelCase:Optional[int] = None , __lowerCamelCase:Optional[str] = None ):
'''simple docstring'''
return tf.nn.softmax(logits=logits + 1E-9 , axis=__lowerCamelCase , name=__lowerCamelCase )
def _lowerCAmelCase ( __lowerCamelCase:Optional[int] , __lowerCamelCase:List[Any] , __lowerCamelCase:Tuple , __lowerCamelCase:Optional[int]=1E-5 , __lowerCamelCase:Optional[int]=-1 ):
'''simple docstring'''
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )
# Get mean and variance on the axis to be normalized
__magic_name__ , __magic_name__ = tf.nn.moments(__lowerCamelCase , axes=[axis] , keepdims=__lowerCamelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
__magic_name__ = [1] * inputs.shape.rank
__magic_name__ = shape_list(__lowerCamelCase )[axis]
__magic_name__ = tf.reshape(__lowerCamelCase , __lowerCamelCase )
__magic_name__ = tf.reshape(__lowerCamelCase , __lowerCamelCase )
# Compute layer normalization using the batch_normalization
# function.
__magic_name__ = tf.nn.batch_normalization(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , offset=__lowerCamelCase , scale=__lowerCamelCase , variance_epsilon=__lowerCamelCase , )
return outputs
def _lowerCAmelCase ( __lowerCamelCase:int , __lowerCamelCase:Any=0 , __lowerCamelCase:str=-1 ):
'''simple docstring'''
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
__magic_name__ = tf.shape(__lowerCamelCase )
__magic_name__ = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__magic_name__ = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(__lowerCamelCase , __lowerCamelCase )
def _lowerCAmelCase ( __lowerCamelCase:tf.Tensor ):
'''simple docstring'''
if not isinstance(__lowerCamelCase , tf.Tensor ):
__magic_name__ = tf.convert_to_tensor(__lowerCamelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__magic_name__ = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__magic_name__ = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
__magic_name__ = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _lowerCAmelCase ( __lowerCamelCase:tf.Tensor , __lowerCamelCase:int , __lowerCamelCase:str = "input_ids" ):
'''simple docstring'''
tf.debugging.assert_less(
__lowerCamelCase , tf.cast(__lowerCamelCase , dtype=tensor.dtype ) , message=(
f'''The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase )}) must be smaller than the embedding '''
f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def _lowerCAmelCase ( __lowerCamelCase:List[str] , __lowerCamelCase:Optional[int] , __lowerCamelCase:int ):
'''simple docstring'''
__magic_name__ = 6_4_5_1_2
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
__magic_name__ = [x for x in data if len(__lowerCamelCase ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"The following attributes cannot be saved to HDF5 file because "
f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
f'''bytes: {bad_attributes}''' )
__magic_name__ = np.asarray(__lowerCamelCase )
__magic_name__ = 1
__magic_name__ = np.array_split(__lowerCamelCase , __lowerCamelCase )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
__magic_name__ = np.array_split(__lowerCamelCase , __lowerCamelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(__lowerCamelCase ):
__magic_name__ = chunk_data
else:
__magic_name__ = data
def _lowerCAmelCase ( __lowerCamelCase:int , __lowerCamelCase:Tuple ):
'''simple docstring'''
if name in group.attrs:
__magic_name__ = [n.decode("utf8" ) if hasattr(__lowerCamelCase , "decode" ) else n for n in group.attrs[name]]
else:
__magic_name__ = []
__magic_name__ = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("utf8" ) if hasattr(__lowerCamelCase , "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
chunk_id += 1
return data
def _lowerCAmelCase ( __lowerCamelCase:List[str] ):
'''simple docstring'''
def _expand_single_ad_tensor(__lowerCamelCase:List[Any] ):
if isinstance(__lowerCamelCase , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(__lowerCamelCase , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , __lowerCamelCase )
| 468
|
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
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 TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class A_ :
def __init__( self : List[str] , __lowerCamelCase : str , __lowerCamelCase : Dict=1_3 , __lowerCamelCase : int=3_0 , __lowerCamelCase : List[Any]=2 , __lowerCamelCase : List[Any]=3 , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : int=3_2 , __lowerCamelCase : int=2 , __lowerCamelCase : Optional[Any]=4 , __lowerCamelCase : List[str]=3_7 , __lowerCamelCase : List[str]="gelu" , __lowerCamelCase : Tuple=0.1 , __lowerCamelCase : int=0.1 , __lowerCamelCase : Optional[int]=1_0 , __lowerCamelCase : Tuple=0.02 , __lowerCamelCase : Any=3 , __lowerCamelCase : Union[str, Any]=0.6 , __lowerCamelCase : Optional[int]=None , ) -> Union[str, Any]:
__magic_name__ = parent
__magic_name__ = batch_size
__magic_name__ = image_size
__magic_name__ = patch_size
__magic_name__ = num_channels
__magic_name__ = is_training
__magic_name__ = use_labels
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = intermediate_size
__magic_name__ = hidden_act
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = type_sequence_label_size
__magic_name__ = initializer_range
__magic_name__ = mask_ratio
__magic_name__ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
__magic_name__ = (image_size // patch_size) ** 2
__magic_name__ = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _snake_case ( self : Optional[Any] ) -> List[Any]:
__magic_name__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ = None
if self.use_labels:
__magic_name__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : Optional[int] ) -> Dict:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_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=__lowerCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _snake_case ( self : Tuple , __lowerCamelCase : Dict , __lowerCamelCase : Tuple , __lowerCamelCase : Optional[int] ) -> Optional[int]:
__magic_name__ = TFViTMAEModel(config=__lowerCamelCase )
__magic_name__ = model(__lowerCamelCase , training=__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : List[Any] , __lowerCamelCase : str ) -> Union[str, Any]:
__magic_name__ = TFViTMAEForPreTraining(__lowerCamelCase )
__magic_name__ = model(__lowerCamelCase , training=__lowerCamelCase )
# expected sequence length = num_patches
__magic_name__ = (self.image_size // self.patch_size) ** 2
__magic_name__ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
__magic_name__ = 1
__magic_name__ = TFViTMAEForPreTraining(__lowerCamelCase )
__magic_name__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__magic_name__ = model(__lowerCamelCase , training=__lowerCamelCase )
__magic_name__ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _snake_case ( self : Tuple ) -> Optional[Any]:
__magic_name__ = self.prepare_config_and_inputs()
((__magic_name__) , (__magic_name__) , (__magic_name__)) = config_and_inputs
__magic_name__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class A_ ( snake_case_ , snake_case_ , unittest.TestCase ):
UpperCAmelCase__ = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
UpperCAmelCase__ = {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
def _snake_case ( self : Any ) -> int:
__magic_name__ = TFViTMAEModelTester(self )
__magic_name__ = ConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase , hidden_size=3_7 )
def _snake_case ( self : Dict ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def _snake_case ( self : Any ) -> Optional[Any]:
pass
def _snake_case ( self : Optional[Any] ) -> Tuple:
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ = model_class(__lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
__magic_name__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowerCamelCase , tf.keras.layers.Layer ) )
def _snake_case ( self : Optional[Any] ) -> Optional[Any]:
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ = model_class(__lowerCamelCase )
__magic_name__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ = [*signature.parameters.keys()]
__magic_name__ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def _snake_case ( self : int ) -> Union[str, Any]:
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _snake_case ( self : str ) -> Any:
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase )
def _snake_case ( self : str ) -> List[Any]:
# make the mask reproducible
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
__magic_name__ = model_class(__lowerCamelCase )
__magic_name__ = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase )
__magic_name__ = model(__lowerCamelCase , noise=__lowerCamelCase )
__magic_name__ = copy.deepcopy(self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
__magic_name__ = model(**__lowerCamelCase , noise=__lowerCamelCase )
__magic_name__ = outputs_dict[0].numpy()
__magic_name__ = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) , 1e-6 )
def _snake_case ( self : int ) -> Union[str, Any]:
# make the mask reproducible
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__lowerCamelCase : Tuple ):
__magic_name__ = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__lowerCamelCase ):
__magic_name__ = v.numpy()
else:
__magic_name__ = np.array(__lowerCamelCase )
return inputs_np_dict
for model_class in self.all_model_classes:
__magic_name__ = model_class(__lowerCamelCase )
__magic_name__ = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase )
__magic_name__ = prepare_numpy_arrays(__lowerCamelCase )
__magic_name__ = model(__lowerCamelCase , noise=__lowerCamelCase )
__magic_name__ = model(**__lowerCamelCase , noise=__lowerCamelCase )
self.assert_outputs_same(__lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : Dict ) -> List[Any]:
# make masks reproducible
np.random.seed(2 )
__magic_name__ = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
__magic_name__ = tf.constant(__lowerCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
__magic_name__ = tf_noise
super().check_pt_tf_models(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : List[Any] ) -> List[str]:
# make mask reproducible
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__lowerCamelCase )
if module_member_name.endswith("MainLayer" )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("MainLayer" )] == model_class.__name__[: -len("Model" )]
for module_member in (getattr(__lowerCamelCase , __lowerCamelCase ),)
if isinstance(__lowerCamelCase , __lowerCamelCase )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__lowerCamelCase , "_keras_serializable" , __lowerCamelCase )
}
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
__magic_name__ = tf.convert_to_tensor(__lowerCamelCase )
inputs_dict.update({"noise": noise} )
for main_layer_class in tf_main_layer_classes:
__magic_name__ = main_layer_class(__lowerCamelCase )
__magic_name__ = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
__magic_name__ = tf.keras.Model(__lowerCamelCase , outputs=main_layer(__lowerCamelCase ) )
__magic_name__ = model(__lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ = os.path.join(__lowerCamelCase , "keras_model.h5" )
model.save(__lowerCamelCase )
__magic_name__ = tf.keras.models.load_model(
__lowerCamelCase , custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__lowerCamelCase , tf.keras.Model )
__magic_name__ = model(__lowerCamelCase )
self.assert_outputs_same(__lowerCamelCase , __lowerCamelCase )
@slow
def _snake_case ( self : Union[str, Any] ) -> str:
# make mask reproducible
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
__magic_name__ = model_class(__lowerCamelCase )
__magic_name__ = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase )
__magic_name__ = model(__lowerCamelCase , noise=__lowerCamelCase )
if model_class.__name__ == "TFViTMAEModel":
__magic_name__ = outputs.last_hidden_state.numpy()
__magic_name__ = 0
else:
__magic_name__ = outputs.logits.numpy()
__magic_name__ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowerCamelCase , saved_model=__lowerCamelCase )
__magic_name__ = model_class.from_pretrained(__lowerCamelCase )
__magic_name__ = model(__lowerCamelCase , noise=__lowerCamelCase )
if model_class.__name__ == "TFViTMAEModel":
__magic_name__ = after_outputs["last_hidden_state"].numpy()
__magic_name__ = 0
else:
__magic_name__ = after_outputs["logits"].numpy()
__magic_name__ = 0
__magic_name__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__lowerCamelCase , 1e-5 )
def _snake_case ( self : List[str] ) -> Optional[Any]:
# make mask reproducible
np.random.seed(2 )
__magic_name__ , __magic_name__ = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ = int((config.image_size // config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
__magic_name__ = model_class(__lowerCamelCase )
__magic_name__ = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase )
__magic_name__ = model(__lowerCamelCase , noise=__lowerCamelCase )
__magic_name__ = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__lowerCamelCase )
__magic_name__ = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
__magic_name__ = model_class.from_config(model.config )
__magic_name__ = new_model(__lowerCamelCase ) # Build model
new_model.set_weights(model.get_weights() )
__magic_name__ = new_model(__lowerCamelCase , noise=__lowerCamelCase )
self.assert_outputs_same(__lowerCamelCase , __lowerCamelCase )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def _snake_case ( self : str ) -> List[Any]:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def _snake_case ( self : Any ) -> List[str]:
pass
@slow
def _snake_case ( self : List[Any] ) -> Any:
__magic_name__ = TFViTMAEModel.from_pretrained("google/vit-base-patch16-224" )
self.assertIsNotNone(__lowerCamelCase )
def _lowerCAmelCase ( ):
'''simple docstring'''
__magic_name__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class A_ ( unittest.TestCase ):
@cached_property
def _snake_case ( self : Union[str, Any] ) -> Union[str, Any]:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def _snake_case ( self : Union[str, Any] ) -> Union[str, Any]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
__magic_name__ = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" )
__magic_name__ = self.default_image_processor
__magic_name__ = prepare_img()
__magic_name__ = image_processor(images=__lowerCamelCase , return_tensors="tf" )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
__magic_name__ = ViTMAEConfig()
__magic_name__ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
__magic_name__ = np.random.uniform(size=(1, num_patches) )
# forward pass
__magic_name__ = model(**__lowerCamelCase , noise=__lowerCamelCase )
# verify the logits
__magic_name__ = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
__magic_name__ = tf.convert_to_tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] , __lowerCamelCase , atol=1e-4 )
| 468
| 1
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Tuple = """▁"""
lowerCAmelCase : int = {"""vocab_file""": """spiece.model"""}
lowerCAmelCase : Any = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}
lowerCAmelCase : Tuple = {
"""google/pegasus-xsum""": 512,
}
lowerCAmelCase : List[str] = logging.get_logger(__name__)
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["input_ids", "attention_mask"]
def __init__( self , _a , _a="<pad>" , _a="</s>" , _a="<unk>" , _a="<mask_2>" , _a="<mask_1>" , _a=None , _a=103 , _a = None , **_a , ):
"""simple docstring"""
lowerCamelCase = offset
if additional_special_tokens is not None:
if not isinstance(_a , _a ):
raise TypeError(
f'additional_special_tokens should be of type {type(_a )}, but is'
f' {type(_a )}' )
lowerCamelCase = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(_a ) , self.offset - 1 )
]
if len(set(_a ) ) != len(_a ):
raise ValueError(
"""Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
lowerCamelCase = additional_special_tokens_extended
else:
lowerCamelCase = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
lowerCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_a , unk_token=_a , mask_token=_a , pad_token=_a , mask_token_sent=_a , offset=_a , additional_special_tokens=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
lowerCamelCase = mask_token_sent
lowerCamelCase = vocab_file
lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
# add special tokens to encoder dict
lowerCamelCase = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowerCamelCase = {v: k for k, v in self.encoder.items()}
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.sp_model ) + self.offset
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
lowerCamelCase = self.__dict__.copy()
lowerCamelCase = None
return state
def __setstate__( self , _a ):
"""simple docstring"""
lowerCamelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCamelCase = {}
lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
return self.sp_model.encode(_a , out_type=_a )
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowerCamelCase = self.sp_model.piece_to_id(_a )
return sp_id + self.offset
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowerCamelCase = self.sp_model.IdToPiece(index - self.offset )
return token
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
lowerCamelCase = []
lowerCamelCase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_a ) + token
lowerCamelCase = []
else:
current_sub_tokens.append(_a )
out_string += self.sp_model.decode(_a )
return out_string.strip()
def _lowerCAmelCase ( self , _a=False ):
"""simple docstring"""
return 1
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
lowerCamelCase = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _lowerCAmelCase ( self , _a , _a = None , _a = False ):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(_a )
elif token_ids_a is None:
return self._special_token_mask(_a ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _lowerCAmelCase ( self , _a , _a=None ):
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCamelCase = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , """wb""" ) as fi:
lowerCamelCase = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 543
|
"""simple docstring"""
import random
def a__ ( snake_case__ , snake_case__ , snake_case__ = False ) -> dict:
lowerCamelCase = {i: [] for i in range(snake_case__ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(snake_case__ )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(snake_case__ ):
for j in range(i + 1 , snake_case__ ):
if random.random() < probability:
graph[i].append(snake_case__ )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(snake_case__ )
return graph
def a__ ( snake_case__ ) -> dict:
return {
i: [j for j in range(snake_case__ ) if i != j] for i in range(snake_case__ )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 543
| 1
|
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__UpperCAmelCase =sys.version_info >= (3, 10)
def __a ( A=None , A=None ) -> List[str]:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=A )
@dataclass
class lowerCAmelCase__ :
lowercase__ : int
lowercase__ : float
lowercase__ : str
lowercase__ : bool
@dataclass
class lowerCAmelCase__ :
lowercase__ : int = 42
lowercase__ : str = field(default="""toto""" , metadata={"""help""": """help message"""} )
@dataclass
class lowerCAmelCase__ :
lowercase__ : bool = False
lowercase__ : bool = True
lowercase__ : Optional[bool] = None
class lowerCAmelCase__ ( UpperCAmelCase_ ):
lowercase__ : Dict = """titi"""
lowercase__ : Dict = """toto"""
class lowerCAmelCase__ ( UpperCAmelCase_ ):
lowercase__ : Union[str, Any] = """titi"""
lowercase__ : str = """toto"""
lowercase__ : Optional[int] = 42
@dataclass
class lowerCAmelCase__ :
lowercase__ : BasicEnum = "toto"
def lowercase_ ( self ):
'''simple docstring'''
A__ = BasicEnum(self.foo )
@dataclass
class lowerCAmelCase__ :
lowercase__ : MixedTypeEnum = "toto"
def lowercase_ ( self ):
'''simple docstring'''
A__ = MixedTypeEnum(self.foo )
@dataclass
class lowerCAmelCase__ :
lowercase__ : Optional[int] = None
lowercase__ : Optional[float] = field(default=UpperCAmelCase_ , metadata={"""help""": """help message"""} )
lowercase__ : Optional[str] = None
lowercase__ : Optional[List[str]] = list_field(default=[] )
lowercase__ : Optional[List[int]] = list_field(default=[] )
@dataclass
class lowerCAmelCase__ :
lowercase__ : List[int] = list_field(default=[] )
lowercase__ : List[int] = list_field(default=[1, 2, 3] )
lowercase__ : List[str] = list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
lowercase__ : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowerCAmelCase__ :
lowercase__ : List[int] = field()
lowercase__ : str = field()
lowercase__ : BasicEnum = field()
def lowercase_ ( self ):
'''simple docstring'''
A__ = BasicEnum(self.required_enum )
@dataclass
class lowerCAmelCase__ :
lowercase__ : int
lowercase__ : "BasicEnum" = field()
lowercase__ : "Optional[bool]" = None
lowercase__ : "str" = field(default="""toto""" , metadata={"""help""": """help message"""} )
lowercase__ : "List[str]" = list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
if is_python_no_less_than_3_10:
@dataclass
class lowerCAmelCase__ :
lowercase__ : bool = False
lowercase__ : bool = True
lowercase__ : bool | None = None
@dataclass
class lowerCAmelCase__ :
lowercase__ : int | None = None
lowercase__ : float | None = field(default=UpperCAmelCase_ , metadata={"""help""": """help message"""} )
lowercase__ : str | None = None
lowercase__ : list[str] | None = list_field(default=[] )
lowercase__ : list[int] | None = list_field(default=[] )
class lowerCAmelCase__ ( unittest.TestCase ):
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
A__ = {k: v for k, v in vars(UpperCamelCase__ ).items() if k != "container"}
A__ = {k: v for k, v in vars(UpperCamelCase__ ).items() if k != "container"}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("choices" , UpperCamelCase__ ) and yy.get("choices" , UpperCamelCase__ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["type"](UpperCamelCase__ ) , yy["type"](UpperCamelCase__ ) )
del xx["type"], yy["type"]
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=UpperCamelCase__ , required=UpperCamelCase__ )
expected.add_argument("--bar" , type=UpperCamelCase__ , required=UpperCamelCase__ )
expected.add_argument("--baz" , type=UpperCamelCase__ , required=UpperCamelCase__ )
expected.add_argument("--flag" , type=UpperCamelCase__ , default=UpperCamelCase__ , const=UpperCamelCase__ , nargs="?" )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
A__ = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
((A__) , ) = parser.parse_args_into_dataclasses(UpperCamelCase__ , look_for_args_file=UpperCamelCase__ )
self.assertFalse(example.flag )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = argparse.ArgumentParser()
expected.add_argument("--foo" , default=42 , type=UpperCamelCase__ )
expected.add_argument("--baz" , default="toto" , type=UpperCamelCase__ , help="help message" )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=UpperCamelCase__ , default=UpperCamelCase__ , const=UpperCamelCase__ , nargs="?" )
expected.add_argument("--baz" , type=UpperCamelCase__ , default=UpperCamelCase__ , const=UpperCamelCase__ , nargs="?" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("--no_baz" , action="store_false" , default=UpperCamelCase__ , dest="baz" )
expected.add_argument("--opt" , type=UpperCamelCase__ , default=UpperCamelCase__ )
A__ = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCamelCase__ )
for dataclass_type in dataclass_types:
A__ = HfArgumentParser(UpperCamelCase__ )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
A__ = parser.parse_args([] )
self.assertEqual(UpperCamelCase__ , Namespace(foo=UpperCamelCase__ , baz=UpperCamelCase__ , opt=UpperCamelCase__ ) )
A__ = parser.parse_args(["--foo", "--no_baz"] )
self.assertEqual(UpperCamelCase__ , Namespace(foo=UpperCamelCase__ , baz=UpperCamelCase__ , opt=UpperCamelCase__ ) )
A__ = parser.parse_args(["--foo", "--baz"] )
self.assertEqual(UpperCamelCase__ , Namespace(foo=UpperCamelCase__ , baz=UpperCamelCase__ , opt=UpperCamelCase__ ) )
A__ = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"] )
self.assertEqual(UpperCamelCase__ , Namespace(foo=UpperCamelCase__ , baz=UpperCamelCase__ , opt=UpperCamelCase__ ) )
A__ = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"] )
self.assertEqual(UpperCamelCase__ , Namespace(foo=UpperCamelCase__ , baz=UpperCamelCase__ , opt=UpperCamelCase__ ) )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=["titi", "toto", 42] , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
A__ = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
A__ = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
A__ = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
A__ = parser.parse_args_into_dataclasses(["--foo", "titi"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
A__ = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
A__ = parser.parse_args_into_dataclasses(["--foo", "42"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowercase_ ( self ):
'''simple docstring'''
@dataclass
class lowerCAmelCase__ :
lowercase__ : Literal["titi", "toto", 42] = "toto"
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=("titi", "toto", 42) , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
A__ = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
A__ = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
A__ = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = argparse.ArgumentParser()
expected.add_argument("--foo_int" , nargs="+" , default=[] , type=UpperCamelCase__ )
expected.add_argument("--bar_int" , nargs="+" , default=[1, 2, 3] , type=UpperCamelCase__ )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=UpperCamelCase__ )
expected.add_argument("--foo_float" , nargs="+" , default=[0.1, 0.2, 0.3] , type=UpperCamelCase__ )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
A__ = parser.parse_args([] )
self.assertEqual(
UpperCamelCase__ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["Hallo", "Bonjour", "Hello"] , foo_float=[0.1, 0.2, 0.3] ) , )
A__ = parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split() )
self.assertEqual(UpperCamelCase__ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["a", "b", "c"] , foo_float=[0.1, 0.7] ) )
def lowercase_ ( self ):
'''simple docstring'''
A__ = argparse.ArgumentParser()
expected.add_argument("--foo" , default=UpperCamelCase__ , type=UpperCamelCase__ )
expected.add_argument("--bar" , default=UpperCamelCase__ , type=UpperCamelCase__ , help="help message" )
expected.add_argument("--baz" , default=UpperCamelCase__ , type=UpperCamelCase__ )
expected.add_argument("--ces" , nargs="+" , default=[] , type=UpperCamelCase__ )
expected.add_argument("--des" , nargs="+" , default=[] , type=UpperCamelCase__ )
A__ = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCamelCase__ )
for dataclass_type in dataclass_types:
A__ = HfArgumentParser(UpperCamelCase__ )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
A__ = parser.parse_args([] )
self.assertEqual(UpperCamelCase__ , Namespace(foo=UpperCamelCase__ , bar=UpperCamelCase__ , baz=UpperCamelCase__ , ces=[] , des=[] ) )
A__ = parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split() )
self.assertEqual(UpperCamelCase__ , Namespace(foo=12 , bar=3.14 , baz="42" , ces=["a", "b", "c"] , des=[1, 2, 3] ) )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = argparse.ArgumentParser()
expected.add_argument("--required_list" , nargs="+" , type=UpperCamelCase__ , required=UpperCamelCase__ )
expected.add_argument("--required_str" , type=UpperCamelCase__ , required=UpperCamelCase__ )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=UpperCamelCase__ , )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=UpperCamelCase__ , required=UpperCamelCase__ )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=UpperCamelCase__ , )
expected.add_argument("--opt" , type=UpperCamelCase__ , default=UpperCamelCase__ )
expected.add_argument("--baz" , default="toto" , type=UpperCamelCase__ , help="help message" )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=UpperCamelCase__ )
self.argparsersEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
A__ = parser.parse_dict(UpperCamelCase__ )[0]
A__ = BasicExample(**UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
"extra": 42,
}
self.assertRaises(UpperCamelCase__ , parser.parse_dict , UpperCamelCase__ , allow_extra_keys=UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A__ = os.path.join(UpperCamelCase__ , "temp_json" )
os.mkdir(UpperCamelCase__ )
with open(temp_local_path + ".json" , "w+" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
A__ = parser.parse_yaml_file(Path(temp_local_path + ".json" ) )[0]
A__ = BasicExample(**UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A__ = os.path.join(UpperCamelCase__ , "temp_yaml" )
os.mkdir(UpperCamelCase__ )
with open(temp_local_path + ".yaml" , "w+" ) as f:
yaml.dump(UpperCamelCase__ , UpperCamelCase__ )
A__ = parser.parse_yaml_file(Path(temp_local_path + ".yaml" ) )[0]
A__ = BasicExample(**UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = HfArgumentParser(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
| 261
|
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
__UpperCAmelCase =re.compile(r"""([A-Z]+)([A-Z][a-z])""")
__UpperCAmelCase =re.compile(r"""([a-z\d])([A-Z])""")
__UpperCAmelCase =re.compile(r"""(?<!_)_(?!_)""")
__UpperCAmelCase =re.compile(r"""(_{2,})""")
__UpperCAmelCase =r"""^\w+(\.\w+)*$"""
__UpperCAmelCase =r"""<>:/\|?*"""
def __a ( A ) -> Tuple:
'''simple docstring'''
A__ = _uppercase_uppercase_re.sub(R"\1_\2" , A )
A__ = _lowercase_uppercase_re.sub(R"\1_\2" , A )
return name.lower()
def __a ( A ) -> int:
'''simple docstring'''
A__ = _single_underscore_re.split(A )
A__ = [_multiple_underscores_re.split(A ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(A ) if n != "" )
def __a ( A ) -> Optional[Any]:
'''simple docstring'''
if os.path.basename(A ) != name:
raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
return camelcase_to_snakecase(A )
def __a ( A , A ) -> Optional[int]:
'''simple docstring'''
if os.path.basename(A ) != name:
raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
if not re.match(_split_re , A ):
raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'.""" )
return f"""{filename_prefix_for_name(A )}-{split}"""
def __a ( A , A , A , A=None ) -> List[Any]:
'''simple docstring'''
A__ = filename_prefix_for_split(A , A )
if filetype_suffix:
prefix += f""".{filetype_suffix}"""
A__ = os.path.join(A , A )
return f"""{filepath}*"""
def __a ( A , A , A , A=None , A=None ) -> List[Any]:
'''simple docstring'''
A__ = filename_prefix_for_split(A , A )
A__ = os.path.join(A , A )
if shard_lengths:
A__ = len(A )
A__ = [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(A )]
if filetype_suffix:
A__ = [filename + f""".{filetype_suffix}""" for filename in filenames]
return filenames
else:
A__ = prefix
if filetype_suffix:
filename += f""".{filetype_suffix}"""
return [filename]
| 261
| 1
|
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = [
'''DownloadConfig''',
'''DownloadManager''',
'''DownloadMode''',
'''StreamingDownloadManager''',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 58
|
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_lowercase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
_lowercase = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
_lowercase = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
] , )
def _lowercase ( self ):
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("""float""" ) ),
"references": datasets.Sequence(datasets.Value("""float""" ) ),
}
else:
return {
"predictions": datasets.Value("""float""" ),
"references": datasets.Value("""float""" ),
}
def _lowercase ( self , _lowercase , _lowercase , _lowercase=None , _lowercase="uniform_average" , _lowercase=True ):
"""simple docstring"""
_lowerCAmelCase = mean_squared_error(
_lowercase , _lowercase , sample_weight=_lowercase , multioutput=_lowercase , squared=_lowercase )
return {"mse": mse}
| 5
| 0
|
from cva import destroyAllWindows, imread, imshow, waitKey
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__snake_case ):
for j in range(__snake_case ):
SCREAMING_SNAKE_CASE = [2_55, 2_55, 2_55] - img[i][j]
return img
if __name__ == "__main__":
# read original image
SCREAMING_SNAKE_CASE_ = imread("""image_data/lena.jpg""", 1)
# convert to its negative
SCREAMING_SNAKE_CASE_ = convert_to_negative(img)
# show result image
imshow("""negative of original image""", img)
waitKey(0)
destroyAllWindows()
| 707
|
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
for line in lines:
SCREAMING_SNAKE_CASE = re.sub(r"""#.*""" , """""" , _SCREAMING_SNAKE_CASE ) # remove comments
if line:
filtered_lines.append(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = """\n""".join(_SCREAMING_SNAKE_CASE )
# Make a hash from all this code
SCREAMING_SNAKE_CASE = full_str.encode("""utf-8""" )
return shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest()
# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_ = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_ = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_ = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_ = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 116
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : List[str] ={
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[Any] =[
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
__snake_case : int =_LazyModule(__name__, globals()['__file__'], _import_structure)
| 647
|
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
snake_case_ =None
snake_case_ =False
snake_case_ =False
snake_case_ =False
snake_case_ =None
snake_case_ =None
snake_case_ =False
snake_case_ =False
snake_case_ =False
snake_case_ =True
snake_case_ =None
snake_case_ =1
snake_case_ =None
snake_case_ =False
snake_case_ =None
snake_case_ =None
def lowerCAmelCase__ (self ) -> "DownloadConfig":
"""simple docstring"""
return self.__class__(**{k: copy.deepcopy(__lowerCamelCase ) for k, v in self.__dict__.items()} )
| 647
| 1
|
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__lowerCAmelCase : Dict = {value: key for key, value in MORSE_CODE_DICT.items()}
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = """Morse code here!"""
print(lowerCamelCase__ )
lowerCAmelCase__ = encrypt(lowerCamelCase__ )
print(lowerCamelCase__ )
lowerCAmelCase__ = decrypt(lowerCamelCase__ )
print(lowerCamelCase__ )
if __name__ == "__main__":
main()
| 674
|
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ = 50 ):
"""simple docstring"""
lowerCAmelCase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F"{solution() = }")
| 674
| 1
|
import numpy as np
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 1e-12 , _SCREAMING_SNAKE_CASE = 100 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(_SCREAMING_SNAKE_CASE )[0] == np.shape(_SCREAMING_SNAKE_CASE )[1]
# Ensure proper dimensionality.
assert np.shape(_SCREAMING_SNAKE_CASE )[0] == np.shape(_SCREAMING_SNAKE_CASE )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(_SCREAMING_SNAKE_CASE ) == np.iscomplexobj(_SCREAMING_SNAKE_CASE )
_A = np.iscomplexobj(_SCREAMING_SNAKE_CASE )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(_SCREAMING_SNAKE_CASE , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
_A = False
_A = 0
_A = 0
_A = 1e12
while not convergence:
# Multiple matrix by the vector.
_A = np.dot(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Normalize the resulting output vector.
_A = w / np.linalg.norm(_SCREAMING_SNAKE_CASE )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
_A = vector.conj().T if is_complex else vector.T
_A = np.dot(_SCREAMING_SNAKE_CASE , np.dot(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# Check convergence.
_A = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
_A = True
_A = lambda_
if is_complex:
_A = np.real(lambda_ )
return lambda_, vector
def __lowerCAmelCase( ) -> None:
"""simple docstring"""
_A = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
_A = np.array([41, 4, 20] )
_A = real_input_matrix.astype(np.complexaaa )
_A = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
_A = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
_A = real_input_matrix
_A = real_vector
elif problem_type == "complex":
_A = complex_input_matrix
_A = complex_vector
# Our implementation.
_A, _A = power_iteration(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
_A, _A = np.linalg.eigh(_SCREAMING_SNAKE_CASE )
# Last eigenvalue is the maximum one.
_A = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
_A = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1e-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(_SCREAMING_SNAKE_CASE ) - np.abs(_SCREAMING_SNAKE_CASE ) ) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 27
|
"""simple docstring"""
import numpy
# List of input, output pairs
SCREAMING_SNAKE_CASE__ : Optional[Any] =(
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE__ : str =(((515, 22, 13), 555), ((61, 35, 49), 150))
SCREAMING_SNAKE_CASE__ : int =[2, 4, 1, 5]
SCREAMING_SNAKE_CASE__ : Any =len(train_data)
SCREAMING_SNAKE_CASE__ : List[Any] =0.009
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="train" ) ->List[str]:
return calculate_hypothesis_value(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) - output(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->Tuple:
_lowerCamelCase : int = 0
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Union[str, Any]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Optional[Any]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=m ) ->List[str]:
_lowerCamelCase : Tuple = 0
for i in range(SCREAMING_SNAKE_CASE_ ):
if index == -1:
summation_value += _error(SCREAMING_SNAKE_CASE_ )
else:
summation_value += _error(SCREAMING_SNAKE_CASE_ ) * train_data[i][0][index]
return summation_value
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->List[str]:
_lowerCamelCase : Optional[Any] = summation_of_cost_derivative(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) / m
return cost_derivative_value
def UpperCamelCase ( ) ->Optional[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
_lowerCamelCase : Dict = 0.000002
_lowerCamelCase : List[str] = 0
_lowerCamelCase : Union[str, Any] = 0
while True:
j += 1
_lowerCamelCase : str = [0, 0, 0, 0]
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
_lowerCamelCase : Optional[int] = get_cost_derivative(i - 1 )
_lowerCamelCase : Optional[Any] = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ , rtol=SCREAMING_SNAKE_CASE_ , ):
break
_lowerCamelCase : List[str] = temp_parameter_vector
print(('''Number of iterations:''', j) )
def UpperCamelCase ( ) ->Optional[Any]:
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
print(('''Actual output value:''', output(SCREAMING_SNAKE_CASE_ , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(SCREAMING_SNAKE_CASE_ , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print('\nTesting gradient descent for a linear hypothesis function.\n')
test_gradient_descent()
| 434
| 0
|
"""simple docstring"""
import sys
A = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCAmelCase__ ( lowerCamelCase__ = N ) -> int:
A = -sys.maxsize - 1
for i in range(len(lowerCamelCase__ ) - 12 ):
A = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
A = product
return largest_product
if __name__ == "__main__":
print(F'{solution() = }')
| 109
|
"""simple docstring"""
import re
def lowerCAmelCase__ ( lowerCamelCase__ ) -> list:
return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]' , str_ )]
def lowerCAmelCase__ ( lowerCamelCase__ ) -> str:
A = split_input(str_ )
return "".join(
[''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
try:
A = split_input(lowerCamelCase__ )
if upper:
A = ''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
A = ''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def lowerCAmelCase__ ( lowerCamelCase__ ) -> str:
return to_simple_case(lowerCamelCase__ )
def lowerCAmelCase__ ( lowerCamelCase__ ) -> str:
try:
A = to_simple_case(lowerCamelCase__ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
return to_complex_case(lowerCamelCase__ , lowerCamelCase__ , '_' )
def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
return to_complex_case(lowerCamelCase__ , lowerCamelCase__ , '-' )
if __name__ == "__main__":
__import__('doctest').testmod()
| 109
| 1
|
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : Optional[Any] = ''''''
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 SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : Tuple = [chr(i + 6_5 ) for i in range(2_6 )]
# Remove duplicate characters from key
UpperCamelCase__ : str = remove_duplicates(key.upper() )
UpperCamelCase__ : Any = len(UpperCamelCase__ )
# First fill cipher with key characters
UpperCamelCase__ : Optional[int] = {alphabet[i]: char for i, char in enumerate(UpperCamelCase__ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(UpperCamelCase__ ) , 2_6 ):
UpperCamelCase__ : List[Any] = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
UpperCamelCase__ : List[Any] = alphabet[i - offset]
UpperCamelCase__ : List[Any] = char
return cipher_alphabet
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ ):
return "".join(cipher_map.get(UpperCamelCase__ , UpperCamelCase__ ) for ch in message.upper() )
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase__ : Dict = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(UpperCamelCase__ , UpperCamelCase__ ) for ch in message.upper() )
def SCREAMING_SNAKE_CASE_ ( ):
UpperCamelCase__ : Any = input('''Enter message to encode or decode: ''' ).strip()
UpperCamelCase__ : Tuple = input('''Enter keyword: ''' ).strip()
UpperCamelCase__ : List[str] = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
UpperCamelCase__ : Optional[int] = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
UpperCamelCase__ : Any = create_cipher_map(UpperCamelCase__ )
print(func(UpperCamelCase__ , UpperCamelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 285
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase =logging.get_logger(__name__)
lowerCamelCase ={
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
),
"distilbert-base-uncased-finetuned-sst-2-english": (
"https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
),
}
class _lowerCamelCase ( UpperCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = '''distilbert'''
SCREAMING_SNAKE_CASE_ = {
'''hidden_size''': '''dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
}
def __init__( self , __SCREAMING_SNAKE_CASE=3_0_5_2_2 , __SCREAMING_SNAKE_CASE=5_1_2 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=6 , __SCREAMING_SNAKE_CASE=1_2 , __SCREAMING_SNAKE_CASE=7_6_8 , __SCREAMING_SNAKE_CASE=4 * 7_6_8 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.2 , __SCREAMING_SNAKE_CASE=0 , **__SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : List[Any] = vocab_size
UpperCamelCase__ : Dict = max_position_embeddings
UpperCamelCase__ : Any = sinusoidal_pos_embds
UpperCamelCase__ : Dict = n_layers
UpperCamelCase__ : List[Any] = n_heads
UpperCamelCase__ : Dict = dim
UpperCamelCase__ : Dict = hidden_dim
UpperCamelCase__ : Optional[int] = dropout
UpperCamelCase__ : Optional[Any] = attention_dropout
UpperCamelCase__ : Tuple = activation
UpperCamelCase__ : Optional[int] = initializer_range
UpperCamelCase__ : Optional[int] = qa_dropout
UpperCamelCase__ : str = seq_classif_dropout
super().__init__(**__SCREAMING_SNAKE_CASE , pad_token_id=__SCREAMING_SNAKE_CASE )
class _lowerCamelCase ( UpperCamelCase_ ):
"""simple docstring"""
@property
def __SCREAMING_SNAKE_CASE ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
UpperCamelCase__ : Union[str, Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCamelCase__ : int = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 285
| 1
|
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
__A : List[Any] = logging.get_logger(__name__)
@add_end_docstrings(
lowerCAmelCase__ , r"\n top_k (`int`, defaults to 5):\n The number of predictions to return.\n targets (`str` or `List[str]`, *optional*):\n When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n token will be used (with a warning, and that might be slower).\n\n " , )
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> np.ndarray:
if self.framework == "tf":
lowerCamelCase_ =tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
lowerCamelCase_ =torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=_SCREAMING_SNAKE_CASE )
else:
raise ValueError("""Unsupported framework""" )
return masked_index
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> np.ndarray:
lowerCamelCase_ =self.get_masked_index(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
"""fill-mask""" , self.model.base_model_prefix , f'No mask_token ({self.tokenizer.mask_token}) found on the input' , )
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> Optional[int]:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE )-> Dict[str, GenericTensor]:
if return_tensors is None:
lowerCamelCase_ =self.framework
lowerCamelCase_ =self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE )
self.ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE )
return model_inputs
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> Optional[Any]:
lowerCamelCase_ =self.model(**_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =model_inputs["""input_ids"""]
return model_outputs
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=None )-> Tuple:
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
lowerCamelCase_ =target_ids.shape[0]
lowerCamelCase_ =model_outputs["""input_ids"""][0]
lowerCamelCase_ =model_outputs["""logits"""]
if self.framework == "tf":
lowerCamelCase_ =tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
lowerCamelCase_ =outputs.numpy()
lowerCamelCase_ =outputs[0, masked_index, :]
lowerCamelCase_ =stable_softmax(_SCREAMING_SNAKE_CASE , axis=-1 )
if target_ids is not None:
lowerCamelCase_ =tf.gather_nd(tf.squeeze(_SCREAMING_SNAKE_CASE , 0 ) , target_ids.reshape(-1 , 1 ) )
lowerCamelCase_ =tf.expand_dims(_SCREAMING_SNAKE_CASE , 0 )
lowerCamelCase_ =tf.math.top_k(_SCREAMING_SNAKE_CASE , k=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ , lowerCamelCase_ =topk.values.numpy(), topk.indices.numpy()
else:
lowerCamelCase_ =torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=_SCREAMING_SNAKE_CASE ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
lowerCamelCase_ =outputs[0, masked_index, :]
lowerCamelCase_ =logits.softmax(dim=-1 )
if target_ids is not None:
lowerCamelCase_ =probs[..., target_ids]
lowerCamelCase_ , lowerCamelCase_ =probs.topk(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =[]
lowerCamelCase_ =values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
lowerCamelCase_ =[]
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
lowerCamelCase_ =input_ids.numpy().copy()
if target_ids is not None:
lowerCamelCase_ =target_ids[p].tolist()
lowerCamelCase_ =p
# Filter padding out:
lowerCamelCase_ =tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
lowerCamelCase_ =self.tokenizer.decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ ={"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p] ), """sequence""": sequence}
row.append(_SCREAMING_SNAKE_CASE )
result.append(_SCREAMING_SNAKE_CASE )
if single_mask:
return result[0]
return result
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None )-> int:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCamelCase_ =[targets]
try:
lowerCamelCase_ =self.tokenizer.get_vocab()
except Exception:
lowerCamelCase_ ={}
lowerCamelCase_ =[]
for target in targets:
lowerCamelCase_ =vocab.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if id_ is None:
lowerCamelCase_ =self.tokenizer(
_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , max_length=1 , truncation=_SCREAMING_SNAKE_CASE , )["""input_ids"""]
if len(_SCREAMING_SNAKE_CASE ) == 0:
logger.warning(
f'The specified target token `{target}` does not exist in the model vocabulary. '
"""We cannot replace it with anything meaningful, ignoring it""" )
continue
lowerCamelCase_ =input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
f'The specified target token `{target}` does not exist in the model vocabulary. '
f'Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.' )
target_ids.append(id_ )
lowerCamelCase_ =list(set(_SCREAMING_SNAKE_CASE ) )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("""At least one target must be provided when passed.""" )
lowerCamelCase_ =np.array(_SCREAMING_SNAKE_CASE )
return target_ids
def _snake_case ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None )-> int:
lowerCamelCase_ ={}
if targets is not None:
lowerCamelCase_ =self.get_target_ids(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ =target_ids
if top_k is not None:
lowerCamelCase_ =top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
"""fill-mask""" , self.model.base_model_prefix , """The tokenizer does not define a `mask_token`.""" )
return {}, {}, postprocess_params
def __call__( self , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )-> str:
lowerCamelCase_ =super().__call__(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) == 1:
return outputs[0]
return outputs
| 75
|
def __UpperCamelCase ( _A : str , _A : int ) ->str:
"""simple docstring"""
lowerCamelCase_ =[[] for _ in range(_A )]
lowerCamelCase_ =key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(_A ) <= key:
return input_string
for position, character in enumerate(_A ):
lowerCamelCase_ =position % (lowest * 2) # puts it in bounds
lowerCamelCase_ =min(_A , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(_A )
lowerCamelCase_ =["""""".join(_A ) for row in temp_grid]
lowerCamelCase_ ="""""".join(_A )
return output_string
def __UpperCamelCase ( _A : str , _A : int ) ->str:
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
lowerCamelCase_ =[[] for _ in range(_A )] # generates template
for position in range(len(_A ) ):
lowerCamelCase_ =position % (lowest * 2) # puts it in bounds
lowerCamelCase_ =min(_A , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
lowerCamelCase_ =0
for row in temp_grid: # fills in the characters
lowerCamelCase_ =input_string[counter : counter + len(_A )]
grid.append(list(_A ) )
counter += len(_A )
lowerCamelCase_ ="""""" # reads as zigzag
for position in range(len(_A ) ):
lowerCamelCase_ =position % (lowest * 2) # puts it in bounds
lowerCamelCase_ =min(_A , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def __UpperCamelCase ( _A : str ) ->dict[int, str]:
"""simple docstring"""
lowerCamelCase_ ={}
for key_guess in range(1 , len(_A ) ): # tries every key
lowerCamelCase_ =decrypt(_A , _A )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75
| 1
|
from __future__ import annotations
def lowerCAmelCase__ ( a__ , a__ ) ->bool:
'''simple docstring'''
_UpperCamelCase = get_failure_array(a__ )
# 2) Step through text searching for pattern
_UpperCamelCase , _UpperCamelCase = 0, 0 # index into text, pattern
while i < len(a__ ):
if pattern[j] == text[i]:
if j == (len(a__ ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
_UpperCamelCase = failure[j - 1]
continue
i += 1
return False
def lowerCAmelCase__ ( a__ ) ->list[int]:
'''simple docstring'''
_UpperCamelCase = [0]
_UpperCamelCase = 0
_UpperCamelCase = 1
while j < len(a__ ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
_UpperCamelCase = failure[i - 1]
continue
j += 1
failure.append(a__ )
return failure
if __name__ == "__main__":
# Test 1)
lowerCamelCase__ = '''abc1abc12'''
lowerCamelCase__ = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
lowerCamelCase__ = '''alskfjaldsk23adsfabcabc'''
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
lowerCamelCase__ = '''ABABX'''
lowerCamelCase__ = '''ABABZABABYABABX'''
assert kmp(pattern, text)
# Test 3)
lowerCamelCase__ = '''AAAB'''
lowerCamelCase__ = '''ABAAAAAB'''
assert kmp(pattern, text)
# Test 4)
lowerCamelCase__ = '''abcdabcy'''
lowerCamelCase__ = '''abcxabcdabxabcdabcdabcy'''
assert kmp(pattern, text)
# Test 5)
lowerCamelCase__ = '''aabaabaaa'''
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 547
|
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any]=2 , lowercase_ : Dict=56 , lowercase_ : str=True , lowercase_ : Optional[Any]=True , lowercase_ : List[Any]=True , lowercase_ : Union[str, Any]=True , lowercase_ : Any=99 , lowercase_ : Optional[int]=32 , lowercase_ : Tuple=2 , lowercase_ : int=2 , lowercase_ : List[str]=7 , lowercase_ : Any="gelu_new" , lowercase_ : List[str]=0.1 , lowercase_ : str=0.1 , lowercase_ : List[Any]=512 , lowercase_ : List[str]=16 , lowercase_ : Optional[int]=2 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : Union[str, Any]=4 , lowercase_ : Union[str, Any]="block_sparse" , lowercase_ : Tuple=True , lowercase_ : Dict=False , lowercase_ : Dict=2 , lowercase_ : Dict=3 , ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_attention_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_choices
_UpperCamelCase = rescale_embeddings
_UpperCamelCase = attention_type
_UpperCamelCase = use_bias
_UpperCamelCase = block_size
_UpperCamelCase = num_random_blocks
def __UpperCAmelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = None
if self.use_attention_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 = BigBirdConfig(
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 , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )
return config, input_ids, token_type_ids, attention_mask
def __UpperCAmelCase ( self : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_flax
class _UpperCAmelCase ( lowerCAmelCase, unittest.TestCase ):
'''simple docstring'''
__A = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
__A = False
__A = False
def __UpperCAmelCase ( self : Optional[int]) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = FlaxBigBirdModelTester(self)
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self : Union[str, Any]) -> List[str]:
"""simple docstring"""
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self : Any) -> int:
"""simple docstring"""
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self : List[Any]) -> Union[str, Any]:
"""simple docstring"""
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self : str) -> Union[str, Any]:
"""simple docstring"""
super().test_hidden_states_output()
@slow
def __UpperCAmelCase ( self : Tuple) -> Optional[int]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCamelCase = model_class_name.from_pretrained("google/bigbird-roberta-base")
self.assertIsNotNone(lowercase_)
def __UpperCAmelCase ( self : Tuple) -> str:
"""simple docstring"""
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def __UpperCAmelCase ( self : Tuple) -> Dict:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = self._prepare_for_class(lowercase_ , lowercase_)
_UpperCamelCase = model_class(lowercase_)
@jax.jit
def model_jitted(lowercase_ : Dict , lowercase_ : List[Any]=None , **lowercase_ : Tuple):
return model(input_ids=lowercase_ , attention_mask=lowercase_ , **lowercase_)
with self.subTest("JIT Enabled"):
_UpperCamelCase = model_jitted(**lowercase_).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
_UpperCamelCase = model_jitted(**lowercase_).to_tuple()
self.assertEqual(len(lowercase_) , len(lowercase_))
for jitted_output, output in zip(lowercase_ , lowercase_):
self.assertEqual(jitted_output.shape , output.shape)
def __UpperCAmelCase ( self : Any , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : str=1e-5 , lowercase_ : int="outputs" , lowercase_ : List[str]=None) -> Tuple:
"""simple docstring"""
if name.startswith("outputs.attentions"):
return
else:
super().check_pt_flax_outputs(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_)
| 547
| 1
|
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
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : str = {
"""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 UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__magic_name__ = "gptj"
__magic_name__ = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , snake_case__=5_0400 , snake_case__=2048 , snake_case__=4096 , snake_case__=28 , snake_case__=16 , snake_case__=64 , snake_case__=None , snake_case__="gelu_new" , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.0 , snake_case__=1E-5 , snake_case__=0.02 , snake_case__=True , snake_case__=5_0256 , snake_case__=5_0256 , snake_case__=False , **snake_case__ , ):
'''simple docstring'''
_lowerCAmelCase : int = vocab_size
_lowerCAmelCase : Optional[int] = n_positions
_lowerCAmelCase : Optional[int] = n_embd
_lowerCAmelCase : Optional[int] = n_layer
_lowerCAmelCase : str = n_head
_lowerCAmelCase : Tuple = n_inner
_lowerCAmelCase : Tuple = rotary_dim
_lowerCAmelCase : Optional[int] = activation_function
_lowerCAmelCase : Any = resid_pdrop
_lowerCAmelCase : List[str] = embd_pdrop
_lowerCAmelCase : int = attn_pdrop
_lowerCAmelCase : Any = layer_norm_epsilon
_lowerCAmelCase : Optional[int] = initializer_range
_lowerCAmelCase : List[str] = use_cache
_lowerCAmelCase : Dict = bos_token_id
_lowerCAmelCase : Any = eos_token_id
super().__init__(
bos_token_id=snake_case__ , eos_token_id=snake_case__ , tie_word_embeddings=snake_case__ , **snake_case__ )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__ = "default" , snake_case__ = None , snake_case__ = False , ):
'''simple docstring'''
super().__init__(snake_case__ , task=snake_case__ , patching_specs=snake_case__ , use_past=snake_case__ )
if not getattr(self._config , 'pad_token_id' , snake_case__ ):
# TODO: how to do that better?
_lowerCAmelCase : Any = 0
@property
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : str = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(snake_case__ , direction='inputs' )
_lowerCAmelCase : int = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_lowerCAmelCase : int = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def a ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def a ( self ):
'''simple docstring'''
return self._config.n_head
def a ( self , snake_case__ , snake_case__ = -1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = super(snake_case__ , self ).generate_dummy_inputs(
snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
# We need to order the input in the way they appears in the forward()
_lowerCAmelCase : Any = 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
_lowerCAmelCase : Optional[Any] = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_lowerCAmelCase : Any = seqlen + 2
_lowerCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_lowerCAmelCase : Tuple = [
(torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(self.num_layers )
]
_lowerCAmelCase : Tuple = common_inputs['attention_mask']
if self.use_past:
_lowerCAmelCase : Any = ordered_inputs['attention_mask'].dtype
_lowerCAmelCase : Union[str, Any] = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(snake_case__ , snake_case__ , dtype=snake_case__ )] , dim=1 )
return ordered_inputs
@property
def a ( self ):
'''simple docstring'''
return 13
| 714
|
'''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
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : str = {
"""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 UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__magic_name__ = "gptj"
__magic_name__ = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , snake_case__=5_0400 , snake_case__=2048 , snake_case__=4096 , snake_case__=28 , snake_case__=16 , snake_case__=64 , snake_case__=None , snake_case__="gelu_new" , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.0 , snake_case__=1E-5 , snake_case__=0.02 , snake_case__=True , snake_case__=5_0256 , snake_case__=5_0256 , snake_case__=False , **snake_case__ , ):
'''simple docstring'''
_lowerCAmelCase : int = vocab_size
_lowerCAmelCase : Optional[int] = n_positions
_lowerCAmelCase : Optional[int] = n_embd
_lowerCAmelCase : Optional[int] = n_layer
_lowerCAmelCase : str = n_head
_lowerCAmelCase : Tuple = n_inner
_lowerCAmelCase : Tuple = rotary_dim
_lowerCAmelCase : Optional[int] = activation_function
_lowerCAmelCase : Any = resid_pdrop
_lowerCAmelCase : List[str] = embd_pdrop
_lowerCAmelCase : int = attn_pdrop
_lowerCAmelCase : Any = layer_norm_epsilon
_lowerCAmelCase : Optional[int] = initializer_range
_lowerCAmelCase : List[str] = use_cache
_lowerCAmelCase : Dict = bos_token_id
_lowerCAmelCase : Any = eos_token_id
super().__init__(
bos_token_id=snake_case__ , eos_token_id=snake_case__ , tie_word_embeddings=snake_case__ , **snake_case__ )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__ = "default" , snake_case__ = None , snake_case__ = False , ):
'''simple docstring'''
super().__init__(snake_case__ , task=snake_case__ , patching_specs=snake_case__ , use_past=snake_case__ )
if not getattr(self._config , 'pad_token_id' , snake_case__ ):
# TODO: how to do that better?
_lowerCAmelCase : Any = 0
@property
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : str = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(snake_case__ , direction='inputs' )
_lowerCAmelCase : int = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_lowerCAmelCase : int = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def a ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def a ( self ):
'''simple docstring'''
return self._config.n_head
def a ( self , snake_case__ , snake_case__ = -1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = super(snake_case__ , self ).generate_dummy_inputs(
snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
# We need to order the input in the way they appears in the forward()
_lowerCAmelCase : Any = 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
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_lowerCAmelCase : Any = seqlen + 2
_lowerCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_lowerCAmelCase : Tuple = [
(torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(self.num_layers )
]
_lowerCAmelCase : Tuple = common_inputs['attention_mask']
if self.use_past:
_lowerCAmelCase : Any = ordered_inputs['attention_mask'].dtype
_lowerCAmelCase : Union[str, Any] = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(snake_case__ , snake_case__ , dtype=snake_case__ )] , dim=1 )
return ordered_inputs
@property
def a ( self ):
'''simple docstring'''
return 13
| 630
| 0
|
'''simple docstring'''
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
SCREAMING_SNAKE_CASE = '\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results["exact_match"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 33.3\n\n'
SCREAMING_SNAKE_CASE = '\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
"""simple docstring"""
def A__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , reference_urls=[] , )
def A__ ( self : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : int=False , UpperCAmelCase : Tuple=False , UpperCAmelCase : List[str]=False , ) -> Dict:
'''simple docstring'''
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
lowercase : str =np.array([re.sub(UpperCAmelCase , '''''' , UpperCAmelCase ) for x in predictions] )
lowercase : List[Any] =np.array([re.sub(UpperCAmelCase , '''''' , UpperCAmelCase ) for x in references] )
else:
lowercase : int =np.asarray(UpperCAmelCase )
lowercase : str =np.asarray(UpperCAmelCase )
if ignore_case:
lowercase : Optional[int] =np.char.lower(UpperCAmelCase )
lowercase : int =np.char.lower(UpperCAmelCase )
if ignore_punctuation:
lowercase : str =string.punctuation.maketrans('''''' , '''''' , string.punctuation )
lowercase : int =np.char.translate(UpperCAmelCase , table=UpperCAmelCase )
lowercase : Union[str, Any] =np.char.translate(UpperCAmelCase , table=UpperCAmelCase )
if ignore_numbers:
lowercase : int =string.digits.maketrans('''''' , '''''' , string.digits )
lowercase : List[Any] =np.char.translate(UpperCAmelCase , table=UpperCAmelCase )
lowercase : int =np.char.translate(UpperCAmelCase , table=UpperCAmelCase )
lowercase : List[Any] =predictions == references
return {"exact_match": np.mean(UpperCAmelCase ) * 100}
| 94
|
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
__lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
__lowercase = '''
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> repo = "openai/shap-e-img2img"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
>>> image = load_image(image_url).convert("RGB")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
```
'''
@dataclass
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : Union[PIL.Image.Image, np.ndarray]
class a__( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
"""simple docstring"""
super().__init__()
self.register_modules(
prior=__lowerCAmelCase , image_encoder=__lowerCAmelCase , image_processor=__lowerCAmelCase , scheduler=__lowerCAmelCase , renderer=__lowerCAmelCase , )
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase):
"""simple docstring"""
if latents is None:
lowerCAmelCase = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase , device=__lowerCAmelCase , dtype=__lowerCAmelCase)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
lowerCAmelCase = latents.to(__lowerCAmelCase)
lowerCAmelCase = latents * scheduler.init_noise_sigma
return latents
def a_ ( self , __lowerCAmelCase=0):
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""")
lowerCAmelCase = torch.device(f"cuda:{gpu_id}")
lowerCAmelCase = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__lowerCAmelCase , __lowerCAmelCase)
@property
def a_ ( self):
"""simple docstring"""
if self.device != torch.device("""meta""") or not hasattr(self.image_encoder , """_hf_hook"""):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(__lowerCAmelCase , """_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
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
"""simple docstring"""
if isinstance(__lowerCAmelCase , __lowerCAmelCase) and isinstance(image[0] , torch.Tensor):
lowerCAmelCase = torch.cat(__lowerCAmelCase , axis=0) if image[0].ndim == 4 else torch.stack(__lowerCAmelCase , axis=0)
if not isinstance(__lowerCAmelCase , torch.Tensor):
lowerCAmelCase = self.image_processor(__lowerCAmelCase , return_tensors="""pt""").pixel_values[0].unsqueeze(0)
lowerCAmelCase = image.to(dtype=self.image_encoder.dtype , device=__lowerCAmelCase)
lowerCAmelCase = self.image_encoder(__lowerCAmelCase)["""last_hidden_state"""]
lowerCAmelCase = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
lowerCAmelCase = image_embeds.repeat_interleave(__lowerCAmelCase , dim=0)
if do_classifier_free_guidance:
lowerCAmelCase = torch.zeros_like(__lowerCAmelCase)
# 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
lowerCAmelCase = torch.cat([negative_image_embeds, image_embeds])
return image_embeds
@torch.no_grad()
@replace_example_docstring(__lowerCAmelCase)
def __call__( self , __lowerCAmelCase , __lowerCAmelCase = 1 , __lowerCAmelCase = 25 , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = 4.0 , __lowerCAmelCase = 64 , __lowerCAmelCase = "pil" , __lowerCAmelCase = True , ):
"""simple docstring"""
if isinstance(__lowerCAmelCase , PIL.Image.Image):
lowerCAmelCase = 1
elif isinstance(__lowerCAmelCase , torch.Tensor):
lowerCAmelCase = image.shape[0]
elif isinstance(__lowerCAmelCase , __lowerCAmelCase) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image)):
lowerCAmelCase = len(__lowerCAmelCase)
else:
raise ValueError(
f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__lowerCAmelCase)}")
lowerCAmelCase = self._execution_device
lowerCAmelCase = batch_size * num_images_per_prompt
lowerCAmelCase = guidance_scale > 1.0
lowerCAmelCase = self._encode_image(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase)
# prior
self.scheduler.set_timesteps(__lowerCAmelCase , device=__lowerCAmelCase)
lowerCAmelCase = self.scheduler.timesteps
lowerCAmelCase = self.prior.config.num_embeddings
lowerCAmelCase = self.prior.config.embedding_dim
lowerCAmelCase = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
lowerCAmelCase = latents.reshape(latents.shape[0] , __lowerCAmelCase , __lowerCAmelCase)
for i, t in enumerate(self.progress_bar(__lowerCAmelCase)):
# expand the latents if we are doing classifier free guidance
lowerCAmelCase = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
lowerCAmelCase = self.scheduler.scale_model_input(__lowerCAmelCase , __lowerCAmelCase)
lowerCAmelCase = self.prior(
__lowerCAmelCase , timestep=__lowerCAmelCase , proj_embedding=__lowerCAmelCase , ).predicted_image_embedding
# remove the variance
lowerCAmelCase , lowerCAmelCase = noise_pred.split(
scaled_model_input.shape[2] , dim=2) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
lowerCAmelCase , lowerCAmelCase = noise_pred.chunk(2)
lowerCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
lowerCAmelCase = self.scheduler.step(
__lowerCAmelCase , timestep=__lowerCAmelCase , sample=__lowerCAmelCase , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=__lowerCAmelCase)
lowerCAmelCase = []
for i, latent in enumerate(__lowerCAmelCase):
print()
lowerCAmelCase = self.renderer.decode(
latent[None, :] , __lowerCAmelCase , size=__lowerCAmelCase , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(__lowerCAmelCase)
lowerCAmelCase = torch.stack(__lowerCAmelCase)
if output_type not in ["np", "pil"]:
raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}")
lowerCAmelCase = images.cpu().numpy()
if output_type == "pil":
lowerCAmelCase = [self.numpy_to_pil(__lowerCAmelCase) for image in images]
# Offload last model to CPU
if hasattr(self , """final_offload_hook""") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=__lowerCAmelCase)
| 370
| 0
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowercase__ (lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = AltDiffusionPipeline
__UpperCamelCase : Dict = TEXT_TO_IMAGE_PARAMS
__UpperCamelCase : Union[str, Any] = TEXT_TO_IMAGE_BATCH_PARAMS
__UpperCamelCase : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
__UpperCamelCase : Any = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowercase ( self : Any ):
torch.manual_seed(0 )
snake_case__ : int = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=3_2 , )
snake_case__ : List[str] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=__lowerCamelCase , set_alpha_to_one=__lowerCamelCase , )
torch.manual_seed(0 )
snake_case__ : Optional[Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
snake_case__ : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5_0_0_2 , )
snake_case__ : int = CLIPTextModel(__lowerCamelCase )
snake_case__ : str = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
snake_case__ : Union[str, Any] = 7_7
snake_case__ : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase ( self : int , __a : int , __a : str=0 ):
if str(__lowerCamelCase ).startswith("""mps""" ):
snake_case__ : Tuple = torch.manual_seed(__lowerCamelCase )
else:
snake_case__ : Any = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
snake_case__ : Optional[Any] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase ( self : Union[str, Any] ):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def lowercase ( self : Optional[int] ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase ( self : Tuple ):
snake_case__ : List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case__ : int = self.get_dummy_components()
torch.manual_seed(0 )
snake_case__ : str = RobertaSeriesConfig(
hidden_size=3_2 , project_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5_0_0_2 , )
# TODO: remove after fixing the non-deterministic text encoder
snake_case__ : Any = RobertaSeriesModelWithTransformation(__lowerCamelCase )
snake_case__ : List[str] = text_encoder
snake_case__ : List[Any] = AltDiffusionPipeline(**__lowerCamelCase )
snake_case__ : Optional[Any] = alt_pipe.to(__lowerCamelCase )
alt_pipe.set_progress_bar_config(disable=__lowerCamelCase )
snake_case__ : List[str] = self.get_dummy_inputs(__lowerCamelCase )
snake_case__ : str = '''A photo of an astronaut'''
snake_case__ : Any = alt_pipe(**__lowerCamelCase )
snake_case__ : int = output.images
snake_case__ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : Optional[Any] = np.array(
[0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : Tuple ):
snake_case__ : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case__ : str = self.get_dummy_components()
snake_case__ : Optional[Any] = PNDMScheduler(skip_prk_steps=__lowerCamelCase )
torch.manual_seed(0 )
snake_case__ : int = RobertaSeriesConfig(
hidden_size=3_2 , project_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5_0_0_2 , )
# TODO: remove after fixing the non-deterministic text encoder
snake_case__ : Tuple = RobertaSeriesModelWithTransformation(__lowerCamelCase )
snake_case__ : List[str] = text_encoder
snake_case__ : List[Any] = AltDiffusionPipeline(**__lowerCamelCase )
snake_case__ : str = alt_pipe.to(__lowerCamelCase )
alt_pipe.set_progress_bar_config(disable=__lowerCamelCase )
snake_case__ : Union[str, Any] = self.get_dummy_inputs(__lowerCamelCase )
snake_case__ : Optional[int] = alt_pipe(**__lowerCamelCase )
snake_case__ : Optional[int] = output.images
snake_case__ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : Union[str, Any] = np.array(
[0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class lowercase__ (unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Union[str, Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self : int ):
snake_case__ : Union[str, Any] = AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" , safety_checker=__lowerCamelCase )
snake_case__ : List[Any] = alt_pipe.to(__lowerCamelCase )
alt_pipe.set_progress_bar_config(disable=__lowerCamelCase )
snake_case__ : Any = '''A painting of a squirrel eating a burger'''
snake_case__ : List[Any] = torch.manual_seed(0 )
snake_case__ : str = alt_pipe([prompt] , generator=__lowerCamelCase , guidance_scale=6.0 , num_inference_steps=2_0 , output_type="""np""" )
snake_case__ : str = output.images
snake_case__ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
snake_case__ : Dict = np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : str ):
snake_case__ : List[Any] = DDIMScheduler.from_pretrained("""BAAI/AltDiffusion""" , subfolder="""scheduler""" )
snake_case__ : List[str] = AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" , scheduler=__lowerCamelCase , safety_checker=__lowerCamelCase )
snake_case__ : Union[str, Any] = alt_pipe.to(__lowerCamelCase )
alt_pipe.set_progress_bar_config(disable=__lowerCamelCase )
snake_case__ : List[Any] = '''A painting of a squirrel eating a burger'''
snake_case__ : Optional[Any] = torch.manual_seed(0 )
snake_case__ : List[str] = alt_pipe([prompt] , generator=__lowerCamelCase , num_inference_steps=2 , output_type="""numpy""" )
snake_case__ : List[str] = output.images
snake_case__ : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
snake_case__ : List[Any] = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 702
|
from __future__ import annotations
from typing import Any
class lowercase__ :
"""simple docstring"""
def __init__( self : str , __a : int ):
snake_case__ : Any = num_of_nodes
snake_case__ : list[list[int]] = []
snake_case__ : dict[int, int] = {}
def lowercase ( self : Any , __a : int , __a : int , __a : int ):
self.m_edges.append([u_node, v_node, weight] )
def lowercase ( self : int , __a : int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def lowercase ( self : Dict , __a : int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
snake_case__ : Optional[Any] = self.find_component(__a )
def lowercase ( self : Union[str, Any] , __a : list[int] , __a : int , __a : int ):
if component_size[u_node] <= component_size[v_node]:
snake_case__ : int = v_node
component_size[v_node] += component_size[u_node]
self.set_component(__a )
elif component_size[u_node] >= component_size[v_node]:
snake_case__ : Any = self.find_component(__a )
component_size[u_node] += component_size[v_node]
self.set_component(__a )
def lowercase ( self : int ):
snake_case__ : Tuple = []
snake_case__ : Optional[Any] = 0
snake_case__ : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
snake_case__ : int = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
snake_case__ , snake_case__ , snake_case__ : List[Any] = edge
snake_case__ : int = self.m_component[u]
snake_case__ : Tuple = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
snake_case__ : int = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(__a , __a ):
snake_case__ , snake_case__ , snake_case__ : Union[str, Any] = edge
snake_case__ : Optional[int] = self.m_component[u]
snake_case__ : Tuple = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(__a , __a , __a )
print(f'Added edge [{u} - {v}]\nAdded weight: {w}\n' )
num_of_components -= 1
snake_case__ : Union[str, Any] = [-1] * self.m_num_of_nodes
print(f'The total weight of the minimal spanning tree is: {mst_weight}' )
def _lowercase ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 127
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class lowercase ( lowerCAmelCase__ ):
_a = "mgp-str"
def __init__( self , _a=[32, 128] , _a=4 , _a=3 , _a=27 , _a=38 , _a=5_0257 , _a=3_0522 , _a=768 , _a=12 , _a=12 , _a=4.0 , _a=True , _a=False , _a=1e-5 , _a=0.0 , _a=0.0 , _a=0.0 , _a=False , _a=0.02 , **_a , ) -> List[str]:
super().__init__(**_A )
_A : List[Any] = image_size
_A : str = patch_size
_A : List[Any] = num_channels
_A : Tuple = max_token_length
_A : Dict = num_character_labels
_A : Any = num_bpe_labels
_A : Any = num_wordpiece_labels
_A : Optional[int] = hidden_size
_A : List[Any] = num_hidden_layers
_A : List[Any] = num_attention_heads
_A : List[Any] = mlp_ratio
_A : Union[str, Any] = distilled
_A : Tuple = layer_norm_eps
_A : List[Any] = drop_rate
_A : Dict = qkv_bias
_A : List[Any] = attn_drop_rate
_A : Tuple = drop_path_rate
_A : Any = output_aa_attentions
_A : Tuple = initializer_range
| 307
|
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowercase_ = logging.get_logger(__name__)
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = set()
__SCREAMING_SNAKE_CASE : str = []
def parse_line(snake_case ):
for line in fp:
if isinstance(snake_case , snake_case ):
__SCREAMING_SNAKE_CASE : List[Any] = line.decode('''UTF-8''' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(''' ''' ):
# process a single warning and move it to `selected_warnings`.
if len(snake_case ) > 0:
__SCREAMING_SNAKE_CASE : List[Any] = '''\n'''.join(snake_case )
# Only keep the warnings specified in `targets`
if any(F''': {x}: ''' in warning for x in targets ):
selected_warnings.add(snake_case )
buffer.clear()
continue
else:
__SCREAMING_SNAKE_CASE : int = line.strip()
buffer.append(snake_case )
if from_gh:
for filename in os.listdir(snake_case ):
__SCREAMING_SNAKE_CASE : Any = os.path.join(snake_case , snake_case )
if not os.path.isdir(snake_case ):
# read the file
if filename != "warnings.txt":
continue
with open(snake_case ) as fp:
parse_line(snake_case )
else:
try:
with zipfile.ZipFile(snake_case ) as z:
for filename in z.namelist():
if not os.path.isdir(snake_case ):
# read the file
if filename != "warnings.txt":
continue
with z.open(snake_case ) as fp:
parse_line(snake_case )
except Exception:
logger.warning(
F'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.''' )
return selected_warnings
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = set()
__SCREAMING_SNAKE_CASE : List[Any] = [os.path.join(snake_case , snake_case ) for p in os.listdir(snake_case ) if (p.endswith('''.zip''' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(snake_case , snake_case ) )
return selected_warnings
if __name__ == "__main__":
def a__ ( snake_case ):
"""simple docstring"""
return values.split(''',''' )
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
lowercase_ = parser.parse_args()
lowercase_ = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
lowercase_ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
lowercase_ = extract_warnings(args.output_dir, args.targets)
lowercase_ = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 74
| 0
|
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class a ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : Tuple , snake_case : List[Any] , snake_case : str , snake_case : Union[str, Any] ) -> List[Any]:
self.assertEqual(len(snake_case ) , len(snake_case ) )
for a, b in zip(snake_case , snake_case ):
self.assertAlmostEqual(snake_case , snake_case , delta=snake_case )
def lowerCamelCase__ ( self : Optional[int] ) -> Union[str, Any]:
__UpperCAmelCase : Union[str, Any] = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(snake_case ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1E-2 )
def lowerCamelCase__ ( self : int ) -> Union[str, Any]:
__UpperCAmelCase : Dict = None
ops.enable_eager_execution_internal()
__UpperCAmelCase : Any = tf.config.list_physical_devices('''CPU''' )
if len(snake_case ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
__UpperCAmelCase : Optional[Any] = tf.config.list_logical_devices(device_type='''CPU''' )
__UpperCAmelCase : Dict = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
__UpperCAmelCase : List[str] = GradientAccumulator()
__UpperCAmelCase : Optional[Any] = tf.Variable([4.0, 3.0] )
__UpperCAmelCase , __UpperCAmelCase : Tuple = create_optimizer(5E-5 , 10 , 5 )
__UpperCAmelCase : int = tf.Variable([0.0, 0.0] , trainable=snake_case )
def accumulate_on_replica(snake_case : List[str] ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(snake_case : Tuple , snake_case : Optional[Any] ):
with strategy.scope():
__UpperCAmelCase : Optional[Any] = strategy.experimental_local_results(snake_case )
local_variables[0].assign(snake_case )
local_variables[1].assign(snake_case )
strategy.run(snake_case , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(snake_case )
def _check_local_values(snake_case : List[str] , snake_case : Tuple ):
__UpperCAmelCase : List[str] = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , snake_case , tol=1E-2 )
self.assertListAlmostEqual(values[1].value() , snake_case , tol=1E-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 266
|
'''simple docstring'''
def _a ( _lowercase : list[list[int]] , _lowercase : int , _lowercase : int , _lowercase : set ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Any = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
__UpperCAmelCase : Optional[Any] = 0
count += depth_first_search(_lowercase , row + 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , row - 1 , _lowercase , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col + 1 , _lowercase )
count += depth_first_search(_lowercase , _lowercase , col - 1 , _lowercase )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 266
| 1
|
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
UpperCAmelCase = logging.getLogger(__name__)
class lowerCAmelCase ( snake_case_ ):
lowerCAmelCase_ = '''sequence-classification'''
def __init__( self : Tuple , __lowercase : Optional[Any] ):
"""simple docstring"""
if type(UpperCamelCase__ ) == dict:
__lowercase =Namespace(**UpperCamelCase__ )
__lowercase =glue_output_modes[hparams.task]
__lowercase =glue_tasks_num_labels[hparams.task]
super().__init__(UpperCamelCase__ , UpperCamelCase__ , self.mode )
def snake_case ( self : int , **__lowercase : Tuple ):
"""simple docstring"""
return self.model(**UpperCamelCase__ )
def snake_case ( self : List[str] , __lowercase : Any , __lowercase : Dict ):
"""simple docstring"""
__lowercase ={"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
__lowercase =batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
__lowercase =self(**UpperCamelCase__ )
__lowercase =outputs[0]
__lowercase =self.trainer.lr_schedulers[0]["scheduler"]
__lowercase ={"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def snake_case ( self : List[str] ):
"""simple docstring"""
__lowercase =self.hparams
__lowercase =processors[args.task]()
__lowercase =processor.get_labels()
for mode in ["train", "dev"]:
__lowercase =self._feature_file(UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , UpperCamelCase__ )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
__lowercase =(
processor.get_dev_examples(args.data_dir )
if mode == "dev"
else processor.get_train_examples(args.data_dir )
)
__lowercase =convert_examples_to_features(
UpperCamelCase__ , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , UpperCamelCase__ )
torch.save(UpperCamelCase__ , UpperCamelCase__ )
def snake_case ( self : str , __lowercase : Tuple , __lowercase : Any , __lowercase : str = False ):
"""simple docstring"""
__lowercase ="dev" if mode == "test" else mode
__lowercase =self._feature_file(UpperCamelCase__ )
logger.info('Loading features from cached file %s' , UpperCamelCase__ )
__lowercase =torch.load(UpperCamelCase__ )
__lowercase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
__lowercase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
__lowercase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
__lowercase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
__lowercase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) , batch_size=UpperCamelCase__ , shuffle=UpperCamelCase__ , )
def snake_case ( self : List[str] , __lowercase : Optional[int] , __lowercase : str ):
"""simple docstring"""
__lowercase ={"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
__lowercase =batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
__lowercase =self(**UpperCamelCase__ )
__lowercase =outputs[:2]
__lowercase =logits.detach().cpu().numpy()
__lowercase =inputs["labels"].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def snake_case ( self : Optional[Any] , __lowercase : Union[str, Any] ):
"""simple docstring"""
__lowercase =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
__lowercase =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
__lowercase =np.argmax(UpperCamelCase__ , axis=1 )
elif self.hparams.glue_output_mode == "regression":
__lowercase =np.squeeze(UpperCamelCase__ )
__lowercase =np.concatenate([x['target'] for x in outputs] , axis=0 )
__lowercase =[[] for _ in range(out_label_ids.shape[0] )]
__lowercase =[[] for _ in range(out_label_ids.shape[0] )]
__lowercase ={**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task , UpperCamelCase__ , UpperCamelCase__ )}
__lowercase =dict(results.items() )
__lowercase =results
return ret, preds_list, out_label_list
def snake_case ( self : int , __lowercase : List[str] ):
"""simple docstring"""
__lowercase =self._eval_end(UpperCamelCase__ )
__lowercase =ret["log"]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def snake_case ( self : List[Any] , __lowercase : Optional[int] ):
"""simple docstring"""
__lowercase =self._eval_end(UpperCamelCase__ )
__lowercase =ret["log"]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def snake_case ( __lowercase : Optional[Any] , __lowercase : List[str] ):
"""simple docstring"""
BaseTransformer.add_model_specific_args(UpperCamelCase__ , UpperCamelCase__ )
parser.add_argument(
'--max_seq_length' , default=128 , type=UpperCamelCase__ , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=UpperCamelCase__ , required=UpperCamelCase__ , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=UpperCamelCase__ , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def __UpperCamelCase ( ):
'''simple docstring'''
__lowercase =argparse.ArgumentParser()
add_generic_args(lowercase__, os.getcwd() )
__lowercase =GLUETransformer.add_model_specific_args(lowercase__, os.getcwd() )
__lowercase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
__lowercase =os.path.join(
'./results', F'''{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}''', )
os.makedirs(args.output_dir )
__lowercase =GLUETransformer(lowercase__ )
__lowercase =generic_train(lowercase__, lowercase__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
__lowercase =sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt' ), recursive=lowercase__ ) )
__lowercase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(lowercase__ )
if __name__ == "__main__":
main()
| 119
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig""",
"""PoolFormerOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ["""PoolFormerFeatureExtractor"""]
__snake_case = ["""PoolFormerImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
"""POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PoolFormerForImageClassification""",
"""PoolFormerModel""",
"""PoolFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 178
| 0
|
def __lowerCAmelCase ( A_ : int , A_ : int ) -> int:
__UpperCAmelCase = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelCase = n - k
# Calculate C(n,k)
for i in range(A_ ):
result *= n - i
result //= i + 1
return result
def __lowerCAmelCase ( A_ : int ) -> int:
return binomial_coefficient(2 * node_count , A_ ) // (node_count + 1)
def __lowerCAmelCase ( A_ : int ) -> int:
if n < 0:
raise ValueError("factorial() not defined for negative values" )
__UpperCAmelCase = 1
for i in range(1 , n + 1 ):
result *= i
return result
def __lowerCAmelCase ( A_ : int ) -> int:
return catalan_number(A_ ) * factorial(A_ )
if __name__ == "__main__":
a_ = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
F"Given {node_count} nodes, there are {binary_tree_count(node_count)} "
F"binary trees and {catalan_number(node_count)} binary search trees."
)
| 286
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class UpperCAmelCase__ ( snake_case ):
"""simple docstring"""
lowerCAmelCase__ : Dict = 'vit_mae'
def __init__( self: List[Any] , __lowerCAmelCase: Any=768 , __lowerCAmelCase: List[str]=12 , __lowerCAmelCase: Optional[int]=12 , __lowerCAmelCase: Tuple=3_072 , __lowerCAmelCase: List[Any]="gelu" , __lowerCAmelCase: Dict=0.0 , __lowerCAmelCase: Tuple=0.0 , __lowerCAmelCase: Any=0.02 , __lowerCAmelCase: List[Any]=1E-12 , __lowerCAmelCase: List[str]=224 , __lowerCAmelCase: Optional[Any]=16 , __lowerCAmelCase: Union[str, Any]=3 , __lowerCAmelCase: Tuple=True , __lowerCAmelCase: Union[str, Any]=16 , __lowerCAmelCase: Optional[int]=512 , __lowerCAmelCase: int=8 , __lowerCAmelCase: int=2_048 , __lowerCAmelCase: str=0.75 , __lowerCAmelCase: Union[str, Any]=False , **__lowerCAmelCase: List[Any] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__lowerCAmelCase )
__UpperCAmelCase = hidden_size
__UpperCAmelCase = num_hidden_layers
__UpperCAmelCase = num_attention_heads
__UpperCAmelCase = intermediate_size
__UpperCAmelCase = hidden_act
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = initializer_range
__UpperCAmelCase = layer_norm_eps
__UpperCAmelCase = image_size
__UpperCAmelCase = patch_size
__UpperCAmelCase = num_channels
__UpperCAmelCase = qkv_bias
__UpperCAmelCase = decoder_num_attention_heads
__UpperCAmelCase = decoder_hidden_size
__UpperCAmelCase = decoder_num_hidden_layers
__UpperCAmelCase = decoder_intermediate_size
__UpperCAmelCase = mask_ratio
__UpperCAmelCase = norm_pix_loss
| 286
| 1
|
'''simple docstring'''
import sys
lowercase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = N ) -> int:
'''simple docstring'''
snake_case : List[str] = -sys.maxsize - 1
for i in range(len(a_ ) - 12 ):
snake_case : Optional[Any] = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
snake_case : str = product
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| 638
|
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase = TypeVar("T")
class UpperCamelCase_ ( Generic[T] ):
'''simple docstring'''
lowerCAmelCase = 42 # Cache store of keys
lowerCAmelCase = 42 # References of the keys in cache
lowerCAmelCase = 1_0 # Maximum capacity of cache
def __init__( self , a ) -> None:
snake_case_ = deque()
snake_case_ = set()
if not n:
snake_case_ = sys.maxsize
elif n < 0:
raise ValueError('n should be an integer greater than 0.' )
else:
snake_case_ = n
def _UpperCamelCase ( self , a ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
snake_case_ = self.dq_store.pop()
self.key_reference.remove(a )
else:
self.dq_store.remove(a )
self.dq_store.appendleft(a )
self.key_reference.add(a )
def _UpperCamelCase ( self ) -> None:
for k in self.dq_store:
print(a )
def __repr__( self ) -> str:
return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}'''
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase = LRUCache(4)
lru_cache.refer("A")
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer("A")
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 198
| 0
|
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Tuple ,_a : Tuple ,_a : Optional[Any]=13 ,_a : Optional[int]=7 ,_a : Optional[Any]=True ,_a : Optional[Any]=True ,_a : List[str]=True ,_a : str=True ,_a : Dict=99 ,_a : Optional[int]=32 ,_a : Optional[int]=5 ,_a : int=4 ,_a : Tuple=37 ,_a : int="gelu" ,_a : Tuple=0.1 ,_a : Union[str, Any]=0.1 ,_a : Optional[Any]=512 ,_a : Dict=16 ,_a : List[Any]=2 ,_a : Optional[Any]=0.02 ,_a : Union[str, Any]=3 ,_a : List[str]=4 ,_a : List[Any]=None ,):
'''simple docstring'''
A_ : Optional[int] = parent
A_ : int = batch_size
A_ : List[Any] = seq_length
A_ : Tuple = is_training
A_ : str = use_input_mask
A_ : int = use_token_type_ids
A_ : Dict = use_labels
A_ : List[str] = vocab_size
A_ : List[str] = hidden_size
A_ : Dict = num_hidden_layers
A_ : str = num_attention_heads
A_ : int = intermediate_size
A_ : Optional[Any] = hidden_act
A_ : int = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : str = max_position_embeddings
A_ : List[Any] = type_vocab_size
A_ : List[str] = type_sequence_label_size
A_ : List[Any] = initializer_range
A_ : int = num_labels
A_ : int = num_choices
A_ : int = scope
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : int = None
if self.use_input_mask:
A_ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
A_ : List[Any] = None
if self.use_token_type_ids:
A_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A_ : Union[str, Any] = None
A_ : Dict = None
A_ : Tuple = None
if self.use_labels:
A_ : Optional[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Union[str, Any] = ids_tensor([self.batch_size] ,self.num_choices )
A_ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : List[str] ):
'''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=_a ,initializer_range=self.initializer_range ,)
def _a ( self : str ,_a : List[str] ,_a : List[str] ,_a : Optional[Any] ,_a : Optional[Any] ,_a : List[str] ,_a : List[str] ,_a : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = NystromformerModel(config=_a )
model.to(_a )
model.eval()
A_ : Optional[Any] = model(_a ,attention_mask=_a ,token_type_ids=_a )
A_ : Any = model(_a ,token_type_ids=_a )
A_ : Tuple = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Tuple ,_a : List[Any] ,_a : Dict ,_a : Tuple ,_a : Optional[Any] ,_a : Any ,_a : List[Any] ,_a : int ):
'''simple docstring'''
A_ : int = NystromformerForMaskedLM(config=_a )
model.to(_a )
model.eval()
A_ : Tuple = model(_a ,attention_mask=_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Any ,_a : Optional[Any] ,_a : Dict ,_a : Any ,_a : Optional[int] ,_a : str ,_a : Dict ,_a : List[str] ):
'''simple docstring'''
A_ : List[str] = NystromformerForQuestionAnswering(config=_a )
model.to(_a )
model.eval()
A_ : Dict = model(
_a ,attention_mask=_a ,token_type_ids=_a ,start_positions=_a ,end_positions=_a ,)
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 : str ,_a : Optional[int] ,_a : List[str] ,_a : List[str] ,_a : Any ,_a : Tuple ,_a : Union[str, Any] ,_a : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[Any] = self.num_labels
A_ : Optional[int] = NystromformerForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : List[Any] = model(_a ,attention_mask=_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _a ( self : Optional[int] ,_a : List[Any] ,_a : int ,_a : Tuple ,_a : Optional[Any] ,_a : Any ,_a : Union[str, Any] ,_a : Tuple ):
'''simple docstring'''
A_ : List[str] = self.num_labels
A_ : List[Any] = NystromformerForTokenClassification(config=_a )
model.to(_a )
model.eval()
A_ : Dict = model(_a ,attention_mask=_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _a ( self : int ,_a : Tuple ,_a : List[Any] ,_a : Optional[Any] ,_a : Any ,_a : Optional[int] ,_a : Tuple ,_a : Any ):
'''simple docstring'''
A_ : Optional[int] = self.num_choices
A_ : Tuple = NystromformerForMultipleChoice(config=_a )
model.to(_a )
model.eval()
A_ : int = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
A_ : List[str] = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
A_ : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
A_ : Optional[Any] = model(
_a ,attention_mask=_a ,token_type_ids=_a ,labels=_a ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Dict = self.prepare_config_and_inputs()
(
A_
) : str = config_and_inputs
A_ : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
a_ = (
{
"""feature-extraction""": NystromformerModel,
"""fill-mask""": NystromformerForMaskedLM,
"""question-answering""": NystromformerForQuestionAnswering,
"""text-classification""": NystromformerForSequenceClassification,
"""token-classification""": NystromformerForTokenClassification,
"""zero-shot""": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
a_ = False
a_ = False
def _a ( self : int ):
'''simple docstring'''
A_ : Any = NystromformerModelTester(self )
A_ : Any = ConfigTester(self ,config_class=_a ,hidden_size=37 )
def _a ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A_ : List[str] = type
self.model_tester.create_and_check_model(*_a )
def _a ( self : str ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_a )
def _a ( self : Dict ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_a )
def _a ( self : Any ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_a )
def _a ( self : int ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_a )
@slow
def _a ( self : int ):
'''simple docstring'''
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Optional[int] = NystromformerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : int ):
'''simple docstring'''
A_ : Optional[int] = NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""" )
A_ : int = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
A_ : int = model(_a )[0]
A_ : Union[str, Any] = torch.Size((1, 6, 768) )
self.assertEqual(output.shape ,_a )
A_ : Tuple = 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] ,_a ,atol=1e-4 ) )
@slow
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Tuple = """the [MASK] of Belgium is Brussels"""
A_ : List[Any] = AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""" )
A_ : List[Any] = NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""" )
A_ : Tuple = tokenizer(_a ,return_tensors="""pt""" )
with torch.no_grad():
A_ : Union[str, Any] = model(encoding.input_ids ).logits
A_ : str = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(_a ) ,"""capital""" )
| 704
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27
| 0
|
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def _lowercase ( __A ):
'''simple docstring'''
__UpperCamelCase = checkpoints.load_tax_checkpoint(__A )
__UpperCamelCase = flatten_dict(__A )
return flax_params
def _lowercase ( __A ):
'''simple docstring'''
__UpperCamelCase = {}
__UpperCamelCase = {
"""token_embedder""": """embeddings""",
"""encoder_norm""": """layernorm""",
"""kernel""": """weight""",
""".out""": """.output""",
"""scale""": """weight""",
"""embedders_0.pos_embedding""": """row_embedder.weight""",
"""embedders_1.pos_embedding""": """column_embedder.weight""",
}
__UpperCamelCase = {
"""query""": """attention.query""",
"""key""": """attention.key""",
"""value""": """attention.value""",
"""output.dense""": """output""",
"""encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
"""pre_self_attention_layer_norm""": """self_attention.layer_norm""",
"""pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
"""mlp.""": """mlp.DenseReluDense.""",
"""pre_mlp_layer_norm""": """mlp.layer_norm""",
"""self_attention.o""": """self_attention.attention.o""",
"""decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
"""decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
"""decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.logits_dense.weight""": """decoder.lm_head.weight""",
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
__UpperCamelCase = """.""".join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
__UpperCamelCase = new_key.replace(__A ,__A )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
__UpperCamelCase = new_key.replace(__A ,__A )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
__UpperCamelCase = re.sub(R"""layers_(\d+)""" ,R"""layer.\1""" ,__A )
__UpperCamelCase = new_key.replace("""encoder""" ,"""encoder.encoder""" )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
__UpperCamelCase = re.sub(R"""layers_(\d+)""" ,R"""layer.\1""" ,__A )
__UpperCamelCase = flax_dict[key]
__UpperCamelCase = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
__UpperCamelCase = torch.from_numpy(converted_dict[key].T )
else:
__UpperCamelCase = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def _lowercase ( __A ,__A ,__A=False ,__A=False ):
'''simple docstring'''
__UpperCamelCase = get_flax_param(__A )
if not use_large:
__UpperCamelCase = PixaStructVisionConfig()
__UpperCamelCase = PixaStructTextConfig()
else:
__UpperCamelCase = PixaStructVisionConfig(
hidden_size=1_536 ,d_ff=3_968 ,num_attention_heads=24 ,num_hidden_layers=18 )
__UpperCamelCase = PixaStructTextConfig(hidden_size=1_536 ,d_ff=3_968 ,num_heads=24 ,num_layers=18 )
__UpperCamelCase = PixaStructConfig(
vision_config=encoder_config.to_dict() ,text_config=decoder_config.to_dict() ,is_vqa=__A )
__UpperCamelCase = PixaStructForConditionalGeneration(__A )
__UpperCamelCase = rename_and_convert_flax_params(__A )
model.load_state_dict(__A )
__UpperCamelCase = AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
__UpperCamelCase = PixaStructImageProcessor()
__UpperCamelCase = PixaStructProcessor(image_processor=__A ,tokenizer=__A )
if use_large:
__UpperCamelCase = 4_096
__UpperCamelCase = True
# mkdir if needed
os.makedirs(__A ,exist_ok=__A )
model.save_pretrained(__A )
processor.save_pretrained(__A )
print("""Model saved in {}""".format(__A ) )
if __name__ == "__main__":
a__ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--use_large', action='store_true', help='Use large model.')
parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
a__ : Any = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 601
|
'''simple docstring'''
# using dfs for finding eulerian path traversal
def _lowercase ( __A ,__A ,__A ,__A=None ):
'''simple docstring'''
__UpperCamelCase = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
__UpperCamelCase , __UpperCamelCase = True, True
__UpperCamelCase = dfs(__A ,__A ,__A ,__A )
return path
def _lowercase ( __A ,__A ):
'''simple docstring'''
__UpperCamelCase = 0
__UpperCamelCase = -1
for i in range(__A ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
__UpperCamelCase = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def _lowercase ( __A ,__A ):
'''simple docstring'''
__UpperCamelCase = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
__UpperCamelCase , __UpperCamelCase = check_circuit_or_path(__A ,__A )
if check == 3:
print("""graph is not Eulerian""" )
print("""no path""" )
return
__UpperCamelCase = 1
if check == 2:
__UpperCamelCase = odd_node
print("""graph has a Euler path""" )
if check == 1:
print("""graph has a Euler cycle""" )
__UpperCamelCase = dfs(__A ,__A ,__A )
print(__A )
def _lowercase ( ):
'''simple docstring'''
__UpperCamelCase = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
__UpperCamelCase = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
__UpperCamelCase = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
__UpperCamelCase = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
__UpperCamelCase = {
1: [],
2: []
# all degree is zero
}
__UpperCamelCase = 10
check_euler(__A ,__A )
check_euler(__A ,__A )
check_euler(__A ,__A )
check_euler(__A ,__A )
check_euler(__A ,__A )
if __name__ == "__main__":
main()
| 601
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : List[Any] = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
_lowerCamelCase : List[str] = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
_lowerCamelCase : Any = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class lowerCamelCase__ ( __snake_case ):
__UpperCAmelCase = """whisper"""
__UpperCAmelCase = ["""past_key_values"""]
__UpperCAmelCase = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , lowerCAmelCase__=51_865 , lowerCAmelCase__=80 , lowerCAmelCase__=6 , lowerCAmelCase__=4 , lowerCAmelCase__=6 , lowerCAmelCase__=4 , lowerCAmelCase__=1_536 , lowerCAmelCase__=1_536 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=50_257 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__="gelu" , lowerCAmelCase__=256 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=False , lowerCAmelCase__=1_500 , lowerCAmelCase__=448 , lowerCAmelCase__=50_256 , lowerCAmelCase__=50_256 , lowerCAmelCase__=50_256 , lowerCAmelCase__=None , lowerCAmelCase__=[220, 50_256] , lowerCAmelCase__=False , lowerCAmelCase__=256 , lowerCAmelCase__=False , lowerCAmelCase__=0.05 , lowerCAmelCase__=10 , lowerCAmelCase__=2 , lowerCAmelCase__=0.0 , lowerCAmelCase__=10 , lowerCAmelCase__=0 , lowerCAmelCase__=7 , **lowerCAmelCase__ , ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :List[str] =vocab_size
_UpperCamelCase :Dict =num_mel_bins
_UpperCamelCase :Union[str, Any] =d_model
_UpperCamelCase :Dict =encoder_layers
_UpperCamelCase :Optional[Any] =encoder_attention_heads
_UpperCamelCase :Union[str, Any] =decoder_layers
_UpperCamelCase :Any =decoder_attention_heads
_UpperCamelCase :int =decoder_ffn_dim
_UpperCamelCase :List[Any] =encoder_ffn_dim
_UpperCamelCase :Any =dropout
_UpperCamelCase :Tuple =attention_dropout
_UpperCamelCase :Dict =activation_dropout
_UpperCamelCase :Any =activation_function
_UpperCamelCase :List[Any] =init_std
_UpperCamelCase :Tuple =encoder_layerdrop
_UpperCamelCase :Optional[Any] =decoder_layerdrop
_UpperCamelCase :int =use_cache
_UpperCamelCase :Tuple =encoder_layers
_UpperCamelCase :List[Any] =scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCamelCase :List[Any] =max_source_positions
_UpperCamelCase :List[str] =max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
_UpperCamelCase :Optional[int] =classifier_proj_size
_UpperCamelCase :Optional[int] =use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase :List[str] =apply_spec_augment
_UpperCamelCase :int =mask_time_prob
_UpperCamelCase :Union[str, Any] =mask_time_length
_UpperCamelCase :str =mask_time_min_masks
_UpperCamelCase :int =mask_feature_prob
_UpperCamelCase :Optional[Any] =mask_feature_length
_UpperCamelCase :Optional[int] =mask_feature_min_masks
_UpperCamelCase :Optional[Any] =median_filter_width
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , is_encoder_decoder=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , suppress_tokens=lowerCAmelCase__ , begin_suppress_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
class lowerCamelCase__ ( __snake_case ):
@property
def _UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
_UpperCamelCase :List[Any] =OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
_UpperCamelCase :Tuple ={0: """batch"""}
else:
_UpperCamelCase :Optional[Any] ={0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase__ , direction="""inputs""" )
return common_inputs
def _UpperCamelCase ( self , lowerCAmelCase__ , lowerCAmelCase__ = -1 , lowerCAmelCase__ = -1 , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = 22_050 , lowerCAmelCase__ = 5.0 , lowerCAmelCase__ = 220 , ) -> Mapping[str, Any]:
"""simple docstring"""
_UpperCamelCase :Tuple =OrderedDict()
_UpperCamelCase :List[Any] =OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=lowerCAmelCase__ , framework=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , time_duration=lowerCAmelCase__ , frequency=lowerCAmelCase__ , )
_UpperCamelCase :List[str] =encoder_inputs["""input_features"""].shape[2]
_UpperCamelCase :Tuple =encoder_sequence_length // 2 if self.use_past else seq_length
_UpperCamelCase :int =super().generate_dummy_inputs(
preprocessor.tokenizer , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase :Dict =encoder_inputs.pop("""input_features""" )
_UpperCamelCase :Any =decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
_UpperCamelCase :Tuple =decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _UpperCamelCase ( self ) -> float:
"""simple docstring"""
return 1e-3
| 512
|
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
class lowerCamelCase__ ( __snake_case ):
__UpperCAmelCase = """mvp"""
__UpperCAmelCase = ["""past_key_values"""]
__UpperCAmelCase = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , lowerCAmelCase__=50_267 , lowerCAmelCase__=1_024 , lowerCAmelCase__=12 , lowerCAmelCase__=4_096 , lowerCAmelCase__=16 , lowerCAmelCase__=12 , lowerCAmelCase__=4_096 , lowerCAmelCase__=16 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__="gelu" , lowerCAmelCase__=1_024 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=0.0 , lowerCAmelCase__=False , lowerCAmelCase__=True , lowerCAmelCase__=1 , lowerCAmelCase__=0 , lowerCAmelCase__=2 , lowerCAmelCase__=True , lowerCAmelCase__=2 , lowerCAmelCase__=2 , lowerCAmelCase__=False , lowerCAmelCase__=100 , lowerCAmelCase__=800 , **lowerCAmelCase__ , ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :Dict =vocab_size
_UpperCamelCase :List[Any] =max_position_embeddings
_UpperCamelCase :Tuple =d_model
_UpperCamelCase :List[Any] =encoder_ffn_dim
_UpperCamelCase :Optional[int] =encoder_layers
_UpperCamelCase :List[str] =encoder_attention_heads
_UpperCamelCase :List[Any] =decoder_ffn_dim
_UpperCamelCase :Union[str, Any] =decoder_layers
_UpperCamelCase :int =decoder_attention_heads
_UpperCamelCase :Union[str, Any] =dropout
_UpperCamelCase :Tuple =attention_dropout
_UpperCamelCase :Union[str, Any] =activation_dropout
_UpperCamelCase :Optional[Any] =activation_function
_UpperCamelCase :Dict =init_std
_UpperCamelCase :Optional[Any] =encoder_layerdrop
_UpperCamelCase :List[Any] =decoder_layerdrop
_UpperCamelCase :Optional[int] =classifier_dropout
_UpperCamelCase :Optional[Any] =use_cache
_UpperCamelCase :List[Any] =encoder_layers
_UpperCamelCase :List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCamelCase :Dict =use_prompt
_UpperCamelCase :Optional[Any] =prompt_length
_UpperCamelCase :Tuple =prompt_mid_dim
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , is_encoder_decoder=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , forced_eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , lowerCAmelCase__ ):
_UpperCamelCase :Dict =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.""" )
| 512
| 1
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
def __magic_name__ ( __snake_case : np.ndarray ) -> Optional[int]:
lowercase , lowercase : int = np.shape(UpperCamelCase__ )
if rows != columns:
lowercase : List[Any] = (
"\'table\' has to be of square shaped array but got a "
f"""{rows}x{columns} array:\n{table}"""
)
raise ValueError(UpperCamelCase__ )
lowercase : Optional[Any] = np.zeros((rows, columns) )
lowercase : Dict = np.zeros((rows, columns) )
for i in range(UpperCamelCase__ ):
for j in range(UpperCamelCase__ ):
lowercase : Optional[Any] = sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__ ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
lowercase : List[Any] = (table[i][j] - total) / upper[j][j]
lowercase : List[Any] = 1
for j in range(UpperCamelCase__ , UpperCamelCase__ ):
lowercase : List[Any] = sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__ ) )
lowercase : Any = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 361
|
'''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
__lowerCAmelCase : int = [
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)
__lowerCAmelCase : List[Any] = logging.getLogger()
def lowerCAmelCase ( ):
"""simple docstring"""
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
__UpperCAmelCase = parser.parse_args()
return args.f
def lowerCAmelCase ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Any="eval" ):
"""simple docstring"""
__UpperCAmelCase = os.path.join(UpperCamelCase__ , f"""{split}_results.json""" )
if os.path.exists(UpperCamelCase__ ):
with open(UpperCamelCase__ , '''r''' ) as f:
return json.load(UpperCamelCase__ )
raise ValueError(f"""can't find {path}""" )
__lowerCAmelCase : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class A ( UpperCAmelCase ):
def snake_case__ ( self : List[Any] ) -> Union[str, Any]:
__UpperCAmelCase = self.get_auto_remove_tmp_dir()
__UpperCAmelCase = f"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(__a , '''argv''' , __a ):
run_flax_glue.main()
__UpperCAmelCase = get_results(__a )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
@slow
def snake_case__ ( self : Optional[Any] ) -> Optional[int]:
__UpperCAmelCase = self.get_auto_remove_tmp_dir()
__UpperCAmelCase = f"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(__a , '''argv''' , __a ):
run_clm_flax.main()
__UpperCAmelCase = get_results(__a )
self.assertLess(result['''eval_perplexity'''] , 1_0_0 )
@slow
def snake_case__ ( self : Dict ) -> List[str]:
__UpperCAmelCase = self.get_auto_remove_tmp_dir()
__UpperCAmelCase = f"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(__a , '''argv''' , __a ):
run_summarization_flax.main()
__UpperCAmelCase = get_results(__a , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 1_0 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def snake_case__ ( self : Any ) -> List[Any]:
__UpperCAmelCase = self.get_auto_remove_tmp_dir()
__UpperCAmelCase = f"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(__a , '''argv''' , __a ):
run_mlm_flax.main()
__UpperCAmelCase = get_results(__a )
self.assertLess(result['''eval_perplexity'''] , 4_2 )
@slow
def snake_case__ ( self : Dict ) -> str:
__UpperCAmelCase = self.get_auto_remove_tmp_dir()
__UpperCAmelCase = f"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(__a , '''argv''' , __a ):
run_ta_mlm_flax.main()
__UpperCAmelCase = get_results(__a )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.4_2 )
@slow
def snake_case__ ( self : Dict ) -> Tuple:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__UpperCAmelCase = 7 if get_gpu_count() > 1 else 2
__UpperCAmelCase = self.get_auto_remove_tmp_dir()
__UpperCAmelCase = f"""
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(__a , '''argv''' , __a ):
run_flax_ner.main()
__UpperCAmelCase = get_results(__a )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def snake_case__ ( self : Optional[Any] ) -> List[Any]:
__UpperCAmelCase = self.get_auto_remove_tmp_dir()
__UpperCAmelCase = f"""
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(__a , '''argv''' , __a ):
run_qa.main()
__UpperCAmelCase = get_results(__a )
self.assertGreaterEqual(result['''eval_f1'''] , 3_0 )
self.assertGreaterEqual(result['''eval_exact'''] , 3_0 )
| 262
| 0
|
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = 1
lowercase__ = 2
while i * i <= n:
lowercase__ = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def a ( ):
'''simple docstring'''
lowercase__ = 1
lowercase__ = 1
while True:
i += 1
t_num += i
if count_divisors(lowerCamelCase_ ) > 500:
break
return t_num
if __name__ == "__main__":
print(solution())
| 715
|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : str, lowerCamelCase : Any, lowerCamelCase : Tuple=7, lowerCamelCase : str=3, lowerCamelCase : Tuple=18, lowerCamelCase : int=30, lowerCamelCase : Tuple=400, lowerCamelCase : Any=True, lowerCamelCase : Any=None, lowerCamelCase : List[str]=True, lowerCamelCase : Union[str, Any]=None, ):
'''simple docstring'''
lowercase__ = size if size is not None else {'''shortest_edge''': 20}
lowercase__ = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = num_channels
lowercase__ = image_size
lowercase__ = min_resolution
lowercase__ = max_resolution
lowercase__ = do_resize
lowercase__ = size
lowercase__ = do_center_crop
lowercase__ = crop_size
def lowercase__ ( self : Any ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _UpperCAmelCase ( A__ ,unittest.TestCase ):
"""simple docstring"""
lowercase__ = MobileNetVaImageProcessor if is_vision_available() else None
def lowercase__ ( self : Any ):
'''simple docstring'''
lowercase__ = MobileNetVaImageProcessingTester(self )
@property
def lowercase__ ( self : Union[str, Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase__ ( self : Dict ):
'''simple docstring'''
lowercase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase, '''do_resize''' ) )
self.assertTrue(hasattr(lowerCamelCase, '''size''' ) )
self.assertTrue(hasattr(lowerCamelCase, '''do_center_crop''' ) )
self.assertTrue(hasattr(lowerCamelCase, '''crop_size''' ) )
def lowercase__ ( self : Any ):
'''simple docstring'''
lowercase__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size, {'''shortest_edge''': 20} )
self.assertEqual(image_processor.crop_size, {'''height''': 18, '''width''': 18} )
lowercase__ = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84 )
self.assertEqual(image_processor.size, {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size, {'''height''': 84, '''width''': 84} )
def lowercase__ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowercase__ ( self : Any ):
'''simple docstring'''
# Initialize image_processing
lowercase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__ = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, Image.Image )
# Test not batched input
lowercase__ = image_processing(image_inputs[0], return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
# Test batched
lowercase__ = image_processing(lowerCamelCase, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
def lowercase__ ( self : str ):
'''simple docstring'''
# Initialize image_processing
lowercase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__ = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase, numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, np.ndarray )
# Test not batched input
lowercase__ = image_processing(image_inputs[0], return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
# Test batched
lowercase__ = image_processing(lowerCamelCase, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
def lowercase__ ( self : str ):
'''simple docstring'''
# Initialize image_processing
lowercase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__ = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase, torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase, torch.Tensor )
# Test not batched input
lowercase__ = image_processing(image_inputs[0], return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
# Test batched
lowercase__ = image_processing(lowerCamelCase, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
| 671
| 0
|
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
__lowercase : Union[str, Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
__lowercase : Any = "\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {'f1': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results['f1'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results['f1'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n >>> print(round(results['f1'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'f1': array([0.8, 0. , 0. ])}\n"
__lowercase : Dict = "\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('int32' ) ),
'references': datasets.Sequence(datasets.Value('int32' ) ),
}
if self.config_name == 'multilabel'
else {
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html'] , )
def __UpperCAmelCase ( self , __a , __a , __a=None , __a=1 , __a="binary" , __a=None ):
'''simple docstring'''
__a : Tuple = fa_score(
a__ , a__ , labels=a__ , pos_label=a__ , average=a__ , sample_weight=a__ )
return {"f1": float(a__ ) if score.size == 1 else score}
| 476
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
_lowercase : List[str] = None
_lowercase : List[str] = logging.get_logger(__name__)
_lowercase : List[Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
_lowercase : str = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
},
"tokenizer_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
},
}
_lowercase : List[str] = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
_lowercase : Dict = "▁"
class _UpperCamelCase ( __snake_case ):
"""simple docstring"""
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = AlbertTokenizer
def __init__( self , a__=None , a__=None , a__=True , a__=True , a__=False , a__="[CLS]" , a__="[SEP]" , a__="<unk>" , a__="[SEP]" , a__="<pad>" , a__="[CLS]" , a__="[MASK]" , **a__ , ) -> Dict:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
A = (
AddedToken(a__ , lstrip=a__ , rstrip=a__ , normalized=a__ )
if isinstance(a__ , a__ )
else mask_token
)
super().__init__(
a__ , tokenizer_file=a__ , do_lower_case=a__ , remove_space=a__ , keep_accents=a__ , bos_token=a__ , eos_token=a__ , unk_token=a__ , sep_token=a__ , pad_token=a__ , cls_token=a__ , mask_token=a__ , **a__ , )
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = False if not self.vocab_file else True
def _UpperCAmelCase ( self , a__ , a__ = None ) -> List[int]:
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _UpperCAmelCase ( self , a__ , a__ = None ) -> List[int]:
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _UpperCAmelCase ( self , a__ , a__ = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(a__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A = os.path.join(
a__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a__ ):
copyfile(self.vocab_file , a__ )
return (out_vocab_file,)
| 641
| 0
|
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase_ :
def __init__( self , lowercase_ , lowercase_=13 , lowercase_=30 , lowercase_=2 , lowercase_=3 , lowercase_=True , lowercase_=True , lowercase_=32 , lowercase_=2 , lowercase_=4 , lowercase_=37 , lowercase_="gelu" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=10 , lowercase_=0.02 , lowercase_=3 , lowercase_=None , ):
_snake_case : Tuple = parent
_snake_case : Union[str, Any] = batch_size
_snake_case : Optional[int] = image_size
_snake_case : Union[str, Any] = patch_size
_snake_case : List[str] = num_channels
_snake_case : Optional[Any] = is_training
_snake_case : str = use_labels
_snake_case : Dict = hidden_size
_snake_case : Union[str, Any] = num_hidden_layers
_snake_case : Tuple = num_attention_heads
_snake_case : Dict = intermediate_size
_snake_case : Dict = hidden_act
_snake_case : Union[str, Any] = hidden_dropout_prob
_snake_case : Union[str, Any] = attention_probs_dropout_prob
_snake_case : Tuple = type_sequence_label_size
_snake_case : int = initializer_range
_snake_case : Optional[int] = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_snake_case : Optional[int] = (image_size // patch_size) ** 2
_snake_case : int = num_patches + 1
def UpperCamelCase ( self ):
_snake_case : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case : List[Any] = None
if self.use_labels:
_snake_case : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : List[Any] = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ):
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase_ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ ):
_snake_case : List[Any] = TFViTModel(config=lowercase_ )
_snake_case : List[Any] = model(lowercase_ , training=lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
_snake_case : List[Any] = self.image_size // 2
_snake_case : List[str] = pixel_values[:, :, :image_size, :image_size]
_snake_case : List[str] = model(lowercase_ , interpolate_pos_encoding=lowercase_ , training=lowercase_ )
_snake_case : Tuple = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ ):
_snake_case : Tuple = self.type_sequence_label_size
_snake_case : Tuple = TFViTForImageClassification(lowercase_ )
_snake_case : List[str] = model(lowercase_ , labels=lowercase_ , training=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
_snake_case : int = self.image_size // 2
_snake_case : Any = pixel_values[:, :, :image_size, :image_size]
_snake_case : List[Any] = model(lowercase_ , interpolate_pos_encoding=lowercase_ , training=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_snake_case : List[str] = 1
_snake_case : Union[str, Any] = TFViTForImageClassification(lowercase_ )
_snake_case : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_snake_case : Any = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ):
_snake_case : Union[str, Any] = self.prepare_config_and_inputs()
_snake_case : str = config_and_inputs
_snake_case : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class lowercase_ ( __snake_case , __snake_case , unittest.TestCase ):
_lowerCamelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_lowerCamelCase = (
{'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
if is_tf_available()
else {}
)
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def UpperCamelCase ( self ):
_snake_case : str = TFViTModelTester(self )
_snake_case : List[str] = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37 )
def UpperCamelCase ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def UpperCamelCase ( self ):
pass
@unittest.skip(reason="ViT does not use inputs_embeds" )
def UpperCamelCase ( self ):
pass
def UpperCamelCase ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : Tuple = model_class(lowercase_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
_snake_case : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase_ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self ):
_snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : List[Any] = model_class(lowercase_ )
_snake_case : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case : str = [*signature.parameters.keys()]
_snake_case : List[str] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowercase_ )
def UpperCamelCase ( self ):
_snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def UpperCamelCase ( self ):
_snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def UpperCamelCase ( self ):
_snake_case : Any = TFViTModel.from_pretrained("google/vit-base-patch16-224" )
self.assertIsNotNone(lowercase_ )
def snake_case () -> str:
'''simple docstring'''
_snake_case : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class lowercase_ ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ):
return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224" ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ):
_snake_case : Union[str, Any] = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224" )
_snake_case : List[Any] = self.default_image_processor
_snake_case : Union[str, Any] = prepare_img()
_snake_case : List[Any] = image_processor(images=lowercase_ , return_tensors="tf" )
# forward pass
_snake_case : List[Any] = model(**lowercase_ )
# verify the logits
_snake_case : Union[str, Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowercase_ )
_snake_case : List[Any] = tf.constant([-0.2_744, 0.8_215, -0.0_836] )
tf.debugging.assert_near(outputs.logits[0, :3] , lowercase_ , atol=1e-4 )
| 717
|
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def snake_case (__lowercase , __lowercase=False ) -> List[Any]:
'''simple docstring'''
try:
_snake_case : str = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_snake_case : Union[str, Any] = default
else:
# KEY is set, convert it to True or False.
try:
_snake_case : Optional[Any] = strtobool(__lowercase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"""If set, {key} must be yes or no.""" )
return _value
__SCREAMING_SNAKE_CASE : List[str] = parse_flag_from_env('RUN_SLOW', default=False)
def snake_case (__lowercase ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skip("Test was skipped" )(__lowercase )
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , "test is slow" )(__lowercase )
def snake_case (__lowercase ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU" )(__lowercase )
def snake_case (__lowercase ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU" )(__lowercase )
def snake_case (__lowercase ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , "test requires a XPU" )(__lowercase )
def snake_case (__lowercase ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`" )(__lowercase )
def snake_case (__lowercase ) -> Optional[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite" )(__lowercase )
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library" )(__lowercase )
def snake_case (__lowercase ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , "test requires TPU" )(__lowercase )
def snake_case (__lowercase ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU" )(__lowercase )
def snake_case (__lowercase ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU" )(__lowercase )
def snake_case (__lowercase ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs" )(__lowercase )
def snake_case (__lowercase ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs" )(__lowercase )
def snake_case (__lowercase ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , "test requires safetensors" )(__lowercase )
def snake_case (__lowercase ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed" )(__lowercase )
def snake_case (__lowercase ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_torch_version(">=" , "1.12.0" ) , "test requires torch version >= 1.12.0" )(__lowercase )
def snake_case (__lowercase=None , __lowercase=None ) -> int:
'''simple docstring'''
if test_case is None:
return partial(__lowercase , version=__lowercase )
return unittest.skipUnless(is_torch_version(">=" , __lowercase ) , F"""test requires torch version >= {version}""" )(__lowercase )
def snake_case (__lowercase ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard" )(__lowercase )
def snake_case (__lowercase ) -> Any:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , "test requires wandb" )(__lowercase )
def snake_case (__lowercase ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml" )(__lowercase )
__SCREAMING_SNAKE_CASE : Any = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def snake_case (__lowercase ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(
_atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(__lowercase )
class lowercase_ ( unittest.TestCase ):
_lowerCamelCase = True
@classmethod
def UpperCamelCase ( cls ):
_snake_case : List[str] = tempfile.mkdtemp()
@classmethod
def UpperCamelCase ( cls ):
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def UpperCamelCase ( self ):
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("**/*" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(lowercase_ )
class lowercase_ ( unittest.TestCase ):
def UpperCamelCase ( self ):
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class lowercase_ ( unittest.TestCase ):
def UpperCamelCase ( self , lowercase_ ):
_snake_case : int = mocks if isinstance(lowercase_ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def snake_case (__lowercase ) -> List[Any]:
'''simple docstring'''
_snake_case : Tuple = AcceleratorState()
_snake_case : str = tensor[None].clone().to(state.device )
_snake_case : List[str] = gather(__lowercase ).cpu()
_snake_case : Union[str, Any] = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , __lowercase ):
return False
return True
class lowercase_ :
def __init__( self , lowercase_ , lowercase_ , lowercase_ ):
_snake_case : Any = returncode
_snake_case : List[str] = stdout
_snake_case : Tuple = stderr
async def snake_case (__lowercase , __lowercase ) -> Tuple:
'''simple docstring'''
while True:
_snake_case : Any = await stream.readline()
if line:
callback(__lowercase )
else:
break
async def snake_case (__lowercase , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=False , __lowercase=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print("\nRunning: " , " ".join(__lowercase ) )
_snake_case : List[str] = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=__lowercase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=__lowercase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_snake_case : int = []
_snake_case : List[str] = []
def tee(__lowercase , __lowercase , __lowercase , __lowercase="" ):
_snake_case : Union[str, Any] = line.decode("utf-8" ).rstrip()
sink.append(__lowercase )
if not quiet:
print(__lowercase , __lowercase , file=__lowercase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda __lowercase : tee(__lowercase , __lowercase , sys.stdout , label="stdout:" ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda __lowercase : tee(__lowercase , __lowercase , sys.stderr , label="stderr:" ) ) ),
] , timeout=__lowercase , )
return _RunOutput(await p.wait() , __lowercase , __lowercase )
def snake_case (__lowercase , __lowercase=None , __lowercase=None , __lowercase=180 , __lowercase=False , __lowercase=True ) -> _RunOutput:
'''simple docstring'''
_snake_case : str = asyncio.get_event_loop()
_snake_case : Any = loop.run_until_complete(
_stream_subprocess(__lowercase , env=__lowercase , stdin=__lowercase , timeout=__lowercase , quiet=__lowercase , echo=__lowercase ) )
_snake_case : Tuple = " ".join(__lowercase )
if result.returncode > 0:
_snake_case : List[Any] = "\n".join(result.stderr )
raise RuntimeError(
F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
F"""The combined stderr from workers follows:\n{stderr}""" )
return result
class lowercase_ ( __snake_case ):
pass
def snake_case (__lowercase , __lowercase=False ) -> int:
'''simple docstring'''
try:
_snake_case : int = subprocess.check_output(__lowercase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(__lowercase , "decode" ):
_snake_case : Dict = output.decode("utf-8" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"""Command `{' '.join(__lowercase )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 580
| 0
|
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = str(lowerCAmelCase_ )
while len(lowerCAmelCase_ ) != 1:
__SCREAMING_SNAKE_CASE = [int(lowerCAmelCase_ ) for i in num_string]
__SCREAMING_SNAKE_CASE = 1
for i in range(0 , len(lowerCAmelCase_ ) ):
total *= numbers[i]
__SCREAMING_SNAKE_CASE = str(lowerCAmelCase_ )
steps += 1
return steps
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = str(lowerCAmelCase_ )
while len(lowerCAmelCase_ ) != 1:
__SCREAMING_SNAKE_CASE = [int(lowerCAmelCase_ ) for i in num_string]
__SCREAMING_SNAKE_CASE = 0
for i in range(0 , len(lowerCAmelCase_ ) ):
total += numbers[i]
__SCREAMING_SNAKE_CASE = str(lowerCAmelCase_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 682
|
"""simple docstring"""
import os
def UpperCAmelCase__ ():
'''simple docstring'''
with open(os.path.dirname(lowerCAmelCase_ ) + "/p022_names.txt" ) as file:
__SCREAMING_SNAKE_CASE = str(file.readlines()[0] )
__SCREAMING_SNAKE_CASE = names.replace("\"" , "" ).split("," )
names.sort()
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i, name in enumerate(lowerCAmelCase_ ):
for letter in name:
name_score += ord(lowerCAmelCase_ ) - 64
total_score += (i + 1) * name_score
__SCREAMING_SNAKE_CASE = 0
return total_score
if __name__ == "__main__":
print(solution())
| 682
| 1
|
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def __UpperCamelCase ( __lowerCamelCase : np.ndarray , __lowerCamelCase : float , __lowerCamelCase : int = 1_6000 ) -> Optional[int]:
'''simple docstring'''
_a = int(round(sample_rate * max_length ) )
if len(__lowerCamelCase ) <= sample_length:
return wav
_a = randint(0 , len(__lowerCamelCase ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __SCREAMING_SNAKE_CASE :
UpperCAmelCase = field(default=lowerCamelCase__ , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''A file containing the training audio paths and labels.'''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''A file containing the validation audio paths and labels.'''} )
UpperCAmelCase = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
UpperCAmelCase = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
UpperCAmelCase = field(
default='''audio''' , metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''} , )
UpperCAmelCase = field(
default='''label''' , metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase = field(
default=20 , metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
UpperCAmelCase = field(
default='''facebook/wav2vec2-base''' , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} , )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''} )
UpperCAmelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
UpperCAmelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def a_ ( self ) -> List[Any]:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"The argument `--freeze_feature_extractor` is deprecated and "
"will be removed in a future version. Use `--freeze_feature_encoder`"
"instead. Setting `freeze_feature_encoder==True`." , __UpperCamelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"The argument `--freeze_feature_extractor` is deprecated and "
"should not be used in combination with `--freeze_feature_encoder`."
"Only make use of `--freeze_feature_encoder`." )
def __UpperCamelCase ( ) -> Dict:
'''simple docstring'''
_a = 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.
_a , _a , _a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_a , _a , _a = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_audio_classification" , __lowerCamelCase , __lowerCamelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_a = training_args.get_process_log_level()
logger.setLevel(__lowerCamelCase )
transformers.utils.logging.set_verbosity(__lowerCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} "
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_a = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_a = 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 train from scratch." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Initialize our dataset and prepare it for the audio classification task.
_a = DatasetDict()
_a = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_a = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--audio_column_name` to the correct audio column - one of "
F"{', '.join(raw_datasets['train'].column_names )}." )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--label_column_name` to the correct text column - one of "
F"{', '.join(raw_datasets['train'].column_names )}." )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_a = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_a = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_a = feature_extractor.model_input_names[0]
def train_transforms(__lowerCamelCase : Dict ):
_a = []
for audio in batch[data_args.audio_column_name]:
_a = random_subsample(
audio["array"] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(__lowerCamelCase )
_a = feature_extractor(__lowerCamelCase , sampling_rate=feature_extractor.sampling_rate )
_a = {model_input_name: inputs.get(__lowerCamelCase )}
_a = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(__lowerCamelCase : List[Any] ):
_a = [audio["array"] for audio in batch[data_args.audio_column_name]]
_a = feature_extractor(__lowerCamelCase , sampling_rate=feature_extractor.sampling_rate )
_a = {model_input_name: inputs.get(__lowerCamelCase )}
_a = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_a = raw_datasets["train"].features[data_args.label_column_name].names
_a , _a = {}, {}
for i, label in enumerate(__lowerCamelCase ):
_a = str(__lowerCamelCase )
_a = label
# Load the accuracy metric from the datasets package
_a = evaluate.load("accuracy" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(__lowerCamelCase : Dict ):
_a = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=__lowerCamelCase , references=eval_pred.label_ids )
_a = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__lowerCamelCase ) , labelaid=__lowerCamelCase , idalabel=__lowerCamelCase , finetuning_task="audio-classification" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_a = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=__lowerCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_a = (
raw_datasets["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(__lowerCamelCase , output_all_columns=__lowerCamelCase )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_a = (
raw_datasets["eval"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(__lowerCamelCase , output_all_columns=__lowerCamelCase )
# Initialize our trainer
_a = Trainer(
model=__lowerCamelCase , args=__lowerCamelCase , train_dataset=raw_datasets["train"] if training_args.do_train else None , eval_dataset=raw_datasets["eval"] if training_args.do_eval else None , compute_metrics=__lowerCamelCase , tokenizer=__lowerCamelCase , )
# Training
if training_args.do_train:
_a = None
if training_args.resume_from_checkpoint is not None:
_a = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_a = last_checkpoint
_a = trainer.train(resume_from_checkpoint=__lowerCamelCase )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_a = trainer.evaluate()
trainer.log_metrics("eval" , __lowerCamelCase )
trainer.save_metrics("eval" , __lowerCamelCase )
# Write model card and (optionally) push to hub
_a = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "audio-classification",
"dataset": data_args.dataset_name,
"tags": ["audio-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__lowerCamelCase )
else:
trainer.create_model_card(**__lowerCamelCase )
if __name__ == "__main__":
main()
| 715
|
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self , __UpperCamelCase ) -> Optional[Any]:
# we need a list not a string, so do something to change the type
_a = arr.split("," )
def a_ ( self ) -> List[Any]:
_a = [int(self.array[0] )] * len(self.array )
_a = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
_a = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
_a = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
lowercase__ = input("please input some numbers:")
lowercase__ = SubArray(whole_array)
lowercase__ = array.solve_sub_array()
print(("the results is:", re))
| 276
| 0
|
from math import factorial
_UpperCAmelCase = {str(digit): factorial(digit) for digit in range(10)}
def __UpperCamelCase (lowerCAmelCase : int ) -> int:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
raise TypeError('Parameter number must be int' )
if number < 0:
raise ValueError('Parameter number must be greater than or equal to 0' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(lowerCAmelCase ) )
def __UpperCamelCase (lowerCAmelCase : int = 60, lowerCAmelCase : int = 1_000_000 ) -> int:
if not isinstance(lowerCAmelCase, lowerCAmelCase ) or not isinstance(lowerCAmelCase, lowerCAmelCase ):
raise TypeError('Parameters chain_length and number_limit must be int' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'Parameters chain_length and number_limit must be greater than 0' )
# the counter for the chains with the exact desired length
A = 0
# the cached sizes of the previous chains
A = {}
for start_chain_element in range(1, lowerCAmelCase ):
# The temporary set will contain the elements of the chain
A = set()
A = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
A = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(lowerCAmelCase )
chain_set_length += 1
A = digit_factorial_sum(lowerCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
A = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{solution()}''')
| 699
|
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = ['''image_processor''', '''tokenizer''']
SCREAMING_SNAKE_CASE : List[str] = '''BridgeTowerImageProcessor'''
SCREAMING_SNAKE_CASE : Tuple = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int] ):
super().__init__(UpperCamelCase__ , UpperCamelCase__ )
def __call__( self : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCamelCase__ : bool = True , UpperCamelCase__ : Union[bool, str, PaddingStrategy] = False , UpperCamelCase__ : Union[bool, str, TruncationStrategy] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : int = 0 , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : bool = False , UpperCamelCase__ : bool = False , UpperCamelCase__ : bool = False , UpperCamelCase__ : bool = False , UpperCamelCase__ : bool = True , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , **UpperCamelCase__ : List[Any] , ):
A = self.tokenizer(
text=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , stride=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_overflowing_tokens=UpperCamelCase__ , return_special_tokens_mask=UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , return_length=UpperCamelCase__ , verbose=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
# add pixel_values + pixel_mask
A = self.image_processor(
UpperCamelCase__ , return_tensors=UpperCamelCase__ , do_normalize=UpperCamelCase__ , do_center_crop=UpperCamelCase__ , **UpperCamelCase__ )
encoding.update(UpperCamelCase__ )
return encoding
def UpperCamelCase ( self : Dict , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : Any ):
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ )
def UpperCamelCase ( self : int , *UpperCamelCase__ : int , **UpperCamelCase__ : List[str] ):
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ )
@property
def UpperCamelCase ( self : Any ):
A = self.tokenizer.model_input_names
A = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 699
| 1
|
"""simple docstring"""
SCREAMING_SNAKE_CASE = {str(digit): digit**5 for digit in range(10)}
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCAmelCase__ ) )
def __lowerCAmelCase( ):
"""simple docstring"""
return sum(
number
for number in range(1_000 ,1_000_000 )
if number == digits_fifth_powers_sum(lowerCAmelCase__ ) )
if __name__ == "__main__":
print(solution())
| 721
|
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
SCREAMING_SNAKE_CASE = pytest.mark.integration
SCREAMING_SNAKE_CASE = {'comet'}
SCREAMING_SNAKE_CASE = importlib.util.find_spec('fairseq') is not None
SCREAMING_SNAKE_CASE = {'code_eval'}
SCREAMING_SNAKE_CASE = os.name == 'nt'
SCREAMING_SNAKE_CASE = {'bertscore', 'frugalscore', 'perplexity'}
SCREAMING_SNAKE_CASE = importlib.util.find_spec('transformers') is not None
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
@wraps(__UpperCAmelCase )
def wrapper(self ,__UpperCAmelCase ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest('"test requires Fairseq"' )
else:
test_case(self ,__UpperCAmelCase )
return wrapper
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
@wraps(__UpperCAmelCase )
def wrapper(self ,__UpperCAmelCase ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest('"test requires transformers"' )
else:
test_case(self ,__UpperCAmelCase )
return wrapper
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
@wraps(__UpperCAmelCase )
def wrapper(self ,__UpperCAmelCase ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest('"test not supported on Windows"' )
else:
test_case(self ,__UpperCAmelCase )
return wrapper
def __lowerCAmelCase( ):
"""simple docstring"""
_lowercase : int = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('./metrics/*/' )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
@local
class _lowerCamelCase (parameterized.TestCase ):
_snake_case = {}
_snake_case = None
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning' )
def __UpperCAmelCase ( self : str , lowerCamelCase_ : List[str] ):
"""simple docstring"""
_lowercase : Optional[Any] = '[...]'
_lowercase : str = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , lowerCamelCase_ ) ).module_path )
_lowercase : Dict = datasets.load.import_main_class(metric_module.__name__ , dataset=lowerCamelCase_ )
# check parameters
_lowercase : Optional[int] = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(lowerCamelCase_ , metric_module.__name__ ):
with self.use_local_metrics():
try:
_lowercase : Optional[Any] = doctest.testmod(lowerCamelCase_ , verbose=lowerCamelCase_ , raise_on_error=lowerCamelCase_ )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def __UpperCAmelCase ( self : Any , lowerCamelCase_ : Dict ):
"""simple docstring"""
_lowercase : Optional[Any] = '[...]'
_lowercase : Dict = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , lowerCamelCase_ ) ).module_path )
# run doctest
with self.use_local_metrics():
_lowercase : str = doctest.testmod(lowerCamelCase_ , verbose=lowerCamelCase_ , raise_on_error=lowerCamelCase_ )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def __UpperCAmelCase ( self : Tuple , lowerCamelCase_ : int , lowerCamelCase_ : str ):
"""simple docstring"""
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCamelCase_ ):
yield
else:
yield
@contextmanager
def __UpperCAmelCase ( self : Dict ):
"""simple docstring"""
def load_local_metric(lowerCamelCase_ : Optional[Any] , *lowerCamelCase_ : Any , **lowerCamelCase_ : Optional[int] ):
return load_metric(os.path.join('metrics' , lowerCamelCase_ ) , *lowerCamelCase_ , **lowerCamelCase_ )
with patch('datasets.load_metric' ) as mock_load_metric:
_lowercase : str = load_local_metric
yield
@classmethod
def __UpperCAmelCase ( cls : Tuple , lowerCamelCase_ : Tuple ):
"""simple docstring"""
def wrapper(lowerCamelCase_ : int ):
_lowercase : Any = contextmanager(lowerCamelCase_ )
_lowercase : Any = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher('bleurt' )
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string('sv' ,'' ,'' ) # handle pytest cli flags
class _lowerCamelCase (__lowerCamelCase ):
def __UpperCAmelCase ( self : Optional[Any] , lowerCamelCase_ : str ):
"""simple docstring"""
assert len(input_dict['input_ids'] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch('bleurt.score._create_predictor' ) as mock_create_predictor:
_lowercase : Dict = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher('bertscore' )
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
import torch
def bert_cos_score_idf(__UpperCAmelCase ,__UpperCAmelCase ,*__UpperCAmelCase ,**__UpperCAmelCase ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(__UpperCAmelCase ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch('bert_score.scorer.get_model' ), patch(
'bert_score.scorer.bert_cos_score_idf' ) as mock_bert_cos_score_idf:
_lowercase : Tuple = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher('comet' )
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
def load_from_checkpoint(__UpperCAmelCase ):
class _lowerCamelCase :
def __UpperCAmelCase ( self : Tuple , lowerCamelCase_ : str , *lowerCamelCase_ : List[Any] , **lowerCamelCase_ : List[str] ):
"""simple docstring"""
assert len(lowerCamelCase_ ) == 2
_lowercase : Union[str, Any] = [0.19, 0.92]
return scores, sum(lowerCamelCase_ ) / len(lowerCamelCase_ )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch('comet.download_model' ) as mock_download_model:
_lowercase : Dict = None
with patch('comet.load_from_checkpoint' ) as mock_load_from_checkpoint:
_lowercase : str = load_from_checkpoint
yield
def __lowerCAmelCase( ):
"""simple docstring"""
_lowercase : Tuple = load_metric(os.path.join('metrics' ,'seqeval' ) )
_lowercase : int = 'ERROR'
_lowercase : Union[str, Any] = F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
with pytest.raises(__UpperCAmelCase ,match=re.escape(__UpperCAmelCase ) ):
metric.compute(predictions=[] ,references=[] ,scheme=__UpperCAmelCase )
| 283
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Any = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
'''tokenization_convbert''': ['''ConvBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Any = ['''ConvBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Dict = [
'''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvBertForMaskedLM''',
'''ConvBertForMultipleChoice''',
'''ConvBertForQuestionAnswering''',
'''ConvBertForSequenceClassification''',
'''ConvBertForTokenClassification''',
'''ConvBertLayer''',
'''ConvBertModel''',
'''ConvBertPreTrainedModel''',
'''load_tf_weights_in_convbert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = [
'''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFConvBertForMaskedLM''',
'''TFConvBertForMultipleChoice''',
'''TFConvBertForQuestionAnswering''',
'''TFConvBertForSequenceClassification''',
'''TFConvBertForTokenClassification''',
'''TFConvBertLayer''',
'''TFConvBertModel''',
'''TFConvBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 578
|
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: float | Decimal , _lowerCamelCase: float = 10**-10 ):
__SCREAMING_SNAKE_CASE : List[Any] = a
while True:
__SCREAMING_SNAKE_CASE : Optional[Any] = Decimal(_lowerCamelCase ) - (
Decimal(eval(_lowerCamelCase ) ) / Decimal(eval(str(diff(_lowerCamelCase ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(_lowerCamelCase ) ) < precision: # noqa: S307
return float(_lowerCamelCase )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")
# Find root of polynomial
print(f"The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}")
# Find Square Root of 5
print(f"The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}")
# Exponential Roots
print(f"The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}")
| 578
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : List[str] = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Optional[Any] = [
"MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegaForCausalLM",
"MegaForMaskedLM",
"MegaForMultipleChoice",
"MegaForQuestionAnswering",
"MegaForSequenceClassification",
"MegaForTokenClassification",
"MegaModel",
"MegaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
lowercase__ : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 704
|
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase__ : Tuple = logging.get_logger(__name__)
lowercase__ : Union[str, Any] = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
_snake_case = 'mctct'
def __init__( self , SCREAMING_SNAKE_CASE_=8065 , SCREAMING_SNAKE_CASE_=1536 , SCREAMING_SNAKE_CASE_=36 , SCREAMING_SNAKE_CASE_=6144 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=384 , SCREAMING_SNAKE_CASE_=920 , SCREAMING_SNAKE_CASE_=1E-5 , SCREAMING_SNAKE_CASE_=0.3 , SCREAMING_SNAKE_CASE_="relu" , SCREAMING_SNAKE_CASE_=0.0_2 , SCREAMING_SNAKE_CASE_=0.3 , SCREAMING_SNAKE_CASE_=0.3 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0.3 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=(7,) , SCREAMING_SNAKE_CASE_=(3,) , SCREAMING_SNAKE_CASE_=80 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="sum" , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , )-> Tuple:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = vocab_size
__UpperCamelCase = hidden_size
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = intermediate_size
__UpperCamelCase = num_attention_heads
__UpperCamelCase = attention_head_dim
__UpperCamelCase = max_position_embeddings
__UpperCamelCase = layer_norm_eps
__UpperCamelCase = layerdrop
__UpperCamelCase = hidden_act
__UpperCamelCase = initializer_range
__UpperCamelCase = hidden_dropout_prob
__UpperCamelCase = attention_probs_dropout_prob
__UpperCamelCase = pad_token_id
__UpperCamelCase = bos_token_id
__UpperCamelCase = eos_token_id
__UpperCamelCase = conv_glu_dim
__UpperCamelCase = conv_dropout
__UpperCamelCase = num_conv_layers
__UpperCamelCase = input_feat_per_channel
__UpperCamelCase = input_channels
__UpperCamelCase = conv_channels
__UpperCamelCase = ctc_loss_reduction
__UpperCamelCase = ctc_zero_infinity
# prevents config testing fail with exporting to json
__UpperCamelCase = list(SCREAMING_SNAKE_CASE_ )
__UpperCamelCase = list(SCREAMING_SNAKE_CASE_ )
if len(self.conv_kernel ) != self.num_conv_layers:
raise ValueError(
'''Configuration for convolutional module is incorrect. '''
'''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
F"but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, "
F"`config.num_conv_layers = {self.num_conv_layers}`." )
| 451
| 0
|
from manim import *
class lowercase ( A__ ):
'''simple docstring'''
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCAmelCase = Rectangle(height=0.25 , width=0.25 )
UpperCAmelCase = [mem.copy() for i in range(6 )]
UpperCAmelCase = [mem.copy() for i in range(6 )]
UpperCAmelCase = VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
UpperCAmelCase = VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
UpperCAmelCase = VGroup(_snake_case , _snake_case ).arrange(_snake_case , buff=0 )
UpperCAmelCase = Text('''CPU''' , font_size=24 )
UpperCAmelCase = Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_snake_case )
UpperCAmelCase = [mem.copy() for i in range(4 )]
UpperCAmelCase = VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
UpperCAmelCase = Text('''GPU''' , font_size=24 )
UpperCAmelCase = Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
gpu.move_to([-1, -1, 0] )
self.add(_snake_case )
UpperCAmelCase = [mem.copy() for i in range(6 )]
UpperCAmelCase = VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
UpperCAmelCase = Text('''Model''' , font_size=24 )
UpperCAmelCase = Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
model.move_to([3, -1.0, 0] )
self.add(_snake_case )
UpperCAmelCase = []
UpperCAmelCase = []
for i, rect in enumerate(_snake_case ):
UpperCAmelCase = fill.copy().set_fill(_snake_case , opacity=0.8 )
target.move_to(_snake_case )
model_arr.append(_snake_case )
UpperCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_snake_case , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(_snake_case )
self.add(*_snake_case , *_snake_case )
UpperCAmelCase = [meta_mem.copy() for i in range(6 )]
UpperCAmelCase = [meta_mem.copy() for i in range(6 )]
UpperCAmelCase = VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
UpperCAmelCase = VGroup(*_snake_case ).arrange(_snake_case , buff=0 )
UpperCAmelCase = VGroup(_snake_case , _snake_case ).arrange(_snake_case , buff=0 )
UpperCAmelCase = Text('''Disk''' , font_size=24 )
UpperCAmelCase = Group(_snake_case , _snake_case ).arrange(_snake_case , buff=0.5 , aligned_edge=_snake_case )
disk.move_to([-4, -1.25, 0] )
self.add(_snake_case , _snake_case )
UpperCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_snake_case , _snake_case )
UpperCAmelCase = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , )
blue_text.next_to(_snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_snake_case )
UpperCAmelCase = MarkupText(
f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_snake_case ) )
UpperCAmelCase = Square(0.3 )
input.set_fill(_snake_case , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , _snake_case , buff=0.5 )
self.play(Write(_snake_case ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=_snake_case , buff=0.02 )
self.play(MoveToTarget(_snake_case ) )
self.play(FadeOut(_snake_case ) )
UpperCAmelCase = Arrow(start=_snake_case , end=_snake_case , color=_snake_case , buff=0.5 )
a.next_to(model_arr[0].get_left() , _snake_case , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
UpperCAmelCase = MarkupText(
f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_snake_case , run_time=3 ) )
UpperCAmelCase = {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}
self.play(
Write(_snake_case ) , Circumscribe(model_arr[0] , color=_snake_case , **_snake_case ) , Circumscribe(model_cpu_arr[0] , color=_snake_case , **_snake_case ) , Circumscribe(gpu_rect[0] , color=_snake_case , **_snake_case ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
UpperCAmelCase = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , _snake_case , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
UpperCAmelCase = AnimationGroup(
FadeOut(_snake_case , run_time=0.5 ) , MoveToTarget(_snake_case , run_time=0.5 ) , FadeIn(_snake_case , run_time=0.5 ) , lag_ratio=0.2 )
self.play(_snake_case )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
UpperCAmelCase = 0.7
self.play(
Circumscribe(model_arr[i] , **_snake_case ) , Circumscribe(cpu_left_col_base[i] , **_snake_case ) , Circumscribe(cpu_left_col_base[i + 1] , color=_snake_case , **_snake_case ) , Circumscribe(gpu_rect[0] , color=_snake_case , **_snake_case ) , Circumscribe(model_arr[i + 1] , color=_snake_case , **_snake_case ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=_snake_case , **_snake_case ) , Circumscribe(cpu_left_col_base[-1] , color=_snake_case , **_snake_case ) , Circumscribe(gpu_rect[0] , color=_snake_case , **_snake_case ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
UpperCAmelCase = a_c
UpperCAmelCase = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(_snake_case ) , FadeOut(_snake_case , run_time=0.5 ) , )
UpperCAmelCase = MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed.""" , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_snake_case , run_time=3 ) , MoveToTarget(_snake_case ) )
self.wait()
| 254
|
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( A__ , A__ , A__ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]
@register_to_config
def __init__( self , _snake_case , _snake_case , _snake_case = None , _snake_case = 5_0257 , _snake_case = 1024 , _snake_case = 768 , _snake_case = 12 , _snake_case = 12 , _snake_case = None , _snake_case = "gelu_new" , _snake_case = 0.1 , _snake_case = 0.1 , _snake_case = 0.1 , _snake_case = 1e-5 , _snake_case = 0.02 , _snake_case = True , _snake_case = True , _snake_case = False , _snake_case = False , ) -> Any:
"""simple docstring"""
super().__init__()
UpperCAmelCase = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
f""" `n_embd`: {n_embd} are not equal.""" )
UpperCAmelCase = prefix_inner_dim
UpperCAmelCase = prefix_hidden_dim
UpperCAmelCase = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
UpperCAmelCase = (
nn.Linear(self.prefix_hidden_dim , _snake_case ) if self.prefix_hidden_dim is not None else nn.Identity()
)
UpperCAmelCase = GPTaConfig(
vocab_size=_snake_case , n_positions=_snake_case , n_embd=_snake_case , n_layer=_snake_case , n_head=_snake_case , n_inner=_snake_case , activation_function=_snake_case , resid_pdrop=_snake_case , embd_pdrop=_snake_case , attn_pdrop=_snake_case , layer_norm_epsilon=_snake_case , initializer_range=_snake_case , scale_attn_weights=_snake_case , use_cache=_snake_case , scale_attn_by_inverse_layer_idx=_snake_case , reorder_and_upcast_attn=_snake_case , )
UpperCAmelCase = GPTaLMHeadModel(_snake_case )
def snake_case_ ( self , _snake_case , _snake_case , _snake_case = None , _snake_case = None , ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = self.transformer.transformer.wte(_snake_case )
UpperCAmelCase = self.encode_prefix(_snake_case )
UpperCAmelCase = self.decode_prefix(_snake_case )
UpperCAmelCase = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
UpperCAmelCase = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
UpperCAmelCase = torch.cat((dummy_token, input_ids) , dim=1 )
UpperCAmelCase = self.transformer(inputs_embeds=_snake_case , labels=_snake_case , attention_mask=_snake_case )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case_ ( self , _snake_case , _snake_case ) -> torch.Tensor:
"""simple docstring"""
return torch.zeros(_snake_case , self.prefix_length , dtype=torch.intaa , device=_snake_case )
def snake_case_ ( self , _snake_case ) -> Union[str, Any]:
"""simple docstring"""
return self.encode_prefix(_snake_case )
@torch.no_grad()
def snake_case_ ( self , _snake_case , _snake_case , _snake_case ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = torch.split(_snake_case , 1 , dim=0 )
UpperCAmelCase = []
UpperCAmelCase = []
for feature in features:
UpperCAmelCase = self.decode_prefix(feature.to(_snake_case ) ) # back to the clip feature
# Only support beam search for now
UpperCAmelCase , UpperCAmelCase = self.generate_beam(
input_embeds=_snake_case , device=_snake_case , eos_token_id=_snake_case )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
UpperCAmelCase = torch.stack(_snake_case )
UpperCAmelCase = torch.stack(_snake_case )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case = 5 , _snake_case = 67 , _snake_case = 1.0 , _snake_case = None , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = eos_token_id
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = torch.ones(_snake_case , device=_snake_case , dtype=torch.int )
UpperCAmelCase = torch.zeros(_snake_case , device=_snake_case , dtype=torch.bool )
if input_embeds is not None:
UpperCAmelCase = input_embeds
else:
UpperCAmelCase = self.transformer.transformer.wte(_snake_case )
for i in range(_snake_case ):
UpperCAmelCase = self.transformer(inputs_embeds=_snake_case )
UpperCAmelCase = outputs.logits
UpperCAmelCase = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
UpperCAmelCase = logits.softmax(-1 ).log()
if scores is None:
UpperCAmelCase , UpperCAmelCase = logits.topk(_snake_case , -1 )
UpperCAmelCase = generated.expand(_snake_case , *generated.shape[1:] )
UpperCAmelCase , UpperCAmelCase = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
UpperCAmelCase = next_tokens
else:
UpperCAmelCase = tokens.expand(_snake_case , *tokens.shape[1:] )
UpperCAmelCase = torch.cat((tokens, next_tokens) , dim=1 )
else:
UpperCAmelCase = -float(np.inf )
UpperCAmelCase = 0
UpperCAmelCase = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
UpperCAmelCase = scores_sum / seq_lengths[:, None]
UpperCAmelCase , UpperCAmelCase = scores_sum_average.view(-1 ).topk(_snake_case , -1 )
UpperCAmelCase = next_tokens // scores_sum.shape[1]
UpperCAmelCase = seq_lengths[next_tokens_source]
UpperCAmelCase = next_tokens % scores_sum.shape[1]
UpperCAmelCase = next_tokens.unsqueeze(1 )
UpperCAmelCase = tokens[next_tokens_source]
UpperCAmelCase = torch.cat((tokens, next_tokens) , dim=1 )
UpperCAmelCase = generated[next_tokens_source]
UpperCAmelCase = scores_sum_average * seq_lengths
UpperCAmelCase = is_stopped[next_tokens_source]
UpperCAmelCase = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
UpperCAmelCase = torch.cat((generated, next_token_embed) , dim=1 )
UpperCAmelCase = is_stopped + next_tokens.eq(_snake_case ).squeeze()
if is_stopped.all():
break
UpperCAmelCase = scores / seq_lengths
UpperCAmelCase = scores.argsort(descending=_snake_case )
# tokens tensors are already padded to max_seq_length
UpperCAmelCase = [tokens[i] for i in order]
UpperCAmelCase = torch.stack(_snake_case , dim=0 )
UpperCAmelCase = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 254
| 1
|
def _SCREAMING_SNAKE_CASE ( lowercase : int = 50 ):
'''simple docstring'''
lowerCamelCase_ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 706
|
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class A( unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCamelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def a__ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = pipeline(task='text-generation' , model='sshleifer/tiny-ctrl' , framework='pt' )
# Using `do_sample=False` to force deterministic output
lowerCamelCase_ = text_generator('This is a test' , do_sample=A_ )
self.assertEqual(
A_ , [
{
'generated_text': (
'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
' oscope. FiliFili@@'
)
}
] , )
lowerCamelCase_ = text_generator(['This is a test', 'This is a second test'] )
self.assertEqual(
A_ , [
[
{
'generated_text': (
'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
' oscope. FiliFili@@'
)
}
],
[
{
'generated_text': (
'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
' oscope. oscope. FiliFili@@'
)
}
],
] , )
lowerCamelCase_ = text_generator('This is a test' , do_sample=A_ , num_return_sequences=2 , return_tensors=A_ )
self.assertEqual(
A_ , [
{'generated_token_ids': ANY(A_ )},
{'generated_token_ids': ANY(A_ )},
] , )
lowerCamelCase_ = text_generator.model.config.eos_token_id
lowerCamelCase_ = '<pad>'
lowerCamelCase_ = text_generator(
['This is a test', 'This is a second test'] , do_sample=A_ , num_return_sequences=2 , batch_size=2 , return_tensors=A_ , )
self.assertEqual(
A_ , [
[
{'generated_token_ids': ANY(A_ )},
{'generated_token_ids': ANY(A_ )},
],
[
{'generated_token_ids': ANY(A_ )},
{'generated_token_ids': ANY(A_ )},
],
] , )
@require_tf
def a__ ( self : Optional[int] ) -> str:
"""simple docstring"""
lowerCamelCase_ = pipeline(task='text-generation' , model='sshleifer/tiny-ctrl' , framework='tf' )
# Using `do_sample=False` to force deterministic output
lowerCamelCase_ = text_generator('This is a test' , do_sample=A_ )
self.assertEqual(
A_ , [
{
'generated_text': (
'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
' please,'
)
}
] , )
lowerCamelCase_ = text_generator(['This is a test', 'This is a second test'] , do_sample=A_ )
self.assertEqual(
A_ , [
[
{
'generated_text': (
'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
' please,'
)
}
],
[
{
'generated_text': (
'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
)
}
],
] , )
def a__ ( self : Optional[int] , A_ : Dict , A_ : int , A_ : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ = TextGenerationPipeline(model=A_ , tokenizer=A_ )
return text_generator, ["This is a test", "Another test"]
def a__ ( self : Dict ) -> str:
"""simple docstring"""
lowerCamelCase_ = 'Hello I believe in'
lowerCamelCase_ = pipeline('text-generation' , model='hf-internal-testing/tiny-random-gpt2' )
lowerCamelCase_ = text_generator(A_ )
self.assertEqual(
A_ , [{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}] , )
lowerCamelCase_ = text_generator(A_ , stop_sequence=' fe' )
self.assertEqual(A_ , [{'generated_text': 'Hello I believe in fe'}] )
def a__ ( self : Any , A_ : Optional[Any] , A_ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = text_generator.model
lowerCamelCase_ = text_generator.tokenizer
lowerCamelCase_ = text_generator('This is a test' )
self.assertEqual(A_ , [{'generated_text': ANY(A_ )}] )
self.assertTrue(outputs[0]['generated_text'].startswith('This is a test' ) )
lowerCamelCase_ = text_generator('This is a test' , return_full_text=A_ )
self.assertEqual(A_ , [{'generated_text': ANY(A_ )}] )
self.assertNotIn('This is a test' , outputs[0]['generated_text'] )
lowerCamelCase_ = pipeline(task='text-generation' , model=A_ , tokenizer=A_ , return_full_text=A_ )
lowerCamelCase_ = text_generator('This is a test' )
self.assertEqual(A_ , [{'generated_text': ANY(A_ )}] )
self.assertNotIn('This is a test' , outputs[0]['generated_text'] )
lowerCamelCase_ = text_generator('This is a test' , return_full_text=A_ )
self.assertEqual(A_ , [{'generated_text': ANY(A_ )}] )
self.assertTrue(outputs[0]['generated_text'].startswith('This is a test' ) )
lowerCamelCase_ = text_generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=A_ )
self.assertEqual(
A_ , [
[{'generated_text': ANY(A_ )}, {'generated_text': ANY(A_ )}],
[{'generated_text': ANY(A_ )}, {'generated_text': ANY(A_ )}],
] , )
if text_generator.tokenizer.pad_token is not None:
lowerCamelCase_ = text_generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=A_ )
self.assertEqual(
A_ , [
[{'generated_text': ANY(A_ )}, {'generated_text': ANY(A_ )}],
[{'generated_text': ANY(A_ )}, {'generated_text': ANY(A_ )}],
] , )
with self.assertRaises(A_ ):
lowerCamelCase_ = text_generator('test' , return_full_text=A_ , return_text=A_ )
with self.assertRaises(A_ ):
lowerCamelCase_ = text_generator('test' , return_full_text=A_ , return_tensors=A_ )
with self.assertRaises(A_ ):
lowerCamelCase_ = text_generator('test' , return_text=A_ , return_tensors=A_ )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
lowerCamelCase_ = text_generator('' )
self.assertEqual(A_ , [{'generated_text': ANY(A_ )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
lowerCamelCase_ = text_generator('' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
lowerCamelCase_ = ['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
if (
tokenizer.model_max_length < 10000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('This is a test' * 500 , max_new_tokens=20 )
lowerCamelCase_ = text_generator('This is a test' * 500 , handle_long_generation='hole' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(A_ ):
text_generator(
'This is a test' * 500 , handle_long_generation='hole' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def a__ ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
import torch
# Classic `model_kwargs`
lowerCamelCase_ = pipeline(
model='hf-internal-testing/tiny-random-bloom' , model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
lowerCamelCase_ = pipe('This is a test' )
self.assertEqual(
A_ , [
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
lowerCamelCase_ = pipeline(model='hf-internal-testing/tiny-random-bloom' , device_map='auto' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
lowerCamelCase_ = pipe('This is a test' )
self.assertEqual(
A_ , [
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
lowerCamelCase_ = pipeline(model='hf-internal-testing/tiny-random-bloom' , device_map='auto' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
lowerCamelCase_ = pipe('This is a test' )
self.assertEqual(
A_ , [
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
] , )
@require_torch
@require_torch_gpu
def a__ ( self : int ) -> str:
"""simple docstring"""
import torch
lowerCamelCase_ = pipeline(model='hf-internal-testing/tiny-random-bloom' , device=0 , torch_dtype=torch.floataa )
pipe('This is a test' )
@require_torch
@require_accelerate
@require_torch_gpu
def a__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
import torch
lowerCamelCase_ = pipeline(model='hf-internal-testing/tiny-random-bloom' , device_map='auto' , torch_dtype=torch.floataa )
pipe('This is a test' , do_sample=A_ , top_p=0.5 )
def a__ ( self : Tuple ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = 'Hello world'
lowerCamelCase_ = pipeline('text-generation' , model='hf-internal-testing/tiny-random-gpt2' )
if text_generator.model.framework == "tf":
lowerCamelCase_ = logging.get_logger('transformers.generation.tf_utils' )
else:
lowerCamelCase_ = logging.get_logger('transformers.generation.utils' )
lowerCamelCase_ = 'Both `max_new_tokens`' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(A_ ) as cl:
lowerCamelCase_ = text_generator(A_ , max_length=10 , max_new_tokens=1 )
self.assertIn(A_ , cl.out )
# The user only sets one -> no warning
with CaptureLogger(A_ ) as cl:
lowerCamelCase_ = text_generator(A_ , max_new_tokens=1 )
self.assertNotIn(A_ , cl.out )
with CaptureLogger(A_ ) as cl:
lowerCamelCase_ = text_generator(A_ , max_length=10 )
self.assertNotIn(A_ , cl.out )
| 651
| 0
|
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : int = logging.get_logger(__name__)
snake_case : Optional[Any] = {
"""kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = "align_text_model"
def __init__( self , A_=30522 , A_=768 , A_=12 , A_=12 , A_=3072 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=2 , A_=0.02 , A_=1e-1_2 , A_=0 , A_="absolute" , A_=True , **A_ , )-> List[str]:
super().__init__(**UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = position_embedding_type
_SCREAMING_SNAKE_CASE = use_cache
_SCREAMING_SNAKE_CASE = pad_token_id
@classmethod
def __magic_name__ ( cls , A_ , **A_ )-> "PretrainedConfig":
cls._set_token_in_kwargs(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = cls.get_config_dict(UpperCamelCase__ , **UpperCamelCase__ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get('model_type' ) == "align":
_SCREAMING_SNAKE_CASE = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = "align_vision_model"
def __init__( self , A_ = 3 , A_ = 600 , A_ = 2.0 , A_ = 3.1 , A_ = 8 , A_ = [3, 3, 5, 3, 5, 5, 3] , A_ = [32, 16, 24, 40, 80, 112, 192] , A_ = [16, 24, 40, 80, 112, 192, 320] , A_ = [] , A_ = [1, 2, 2, 2, 1, 2, 1] , A_ = [1, 2, 2, 3, 3, 4, 1] , A_ = [1, 6, 6, 6, 6, 6, 6] , A_ = 0.25 , A_ = "swish" , A_ = 2560 , A_ = "mean" , A_ = 0.02 , A_ = 0.001 , A_ = 0.99 , A_ = 0.2 , **A_ , )-> Union[str, Any]:
super().__init__(**UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = width_coefficient
_SCREAMING_SNAKE_CASE = depth_coefficient
_SCREAMING_SNAKE_CASE = depth_divisor
_SCREAMING_SNAKE_CASE = kernel_sizes
_SCREAMING_SNAKE_CASE = in_channels
_SCREAMING_SNAKE_CASE = out_channels
_SCREAMING_SNAKE_CASE = depthwise_padding
_SCREAMING_SNAKE_CASE = strides
_SCREAMING_SNAKE_CASE = num_block_repeats
_SCREAMING_SNAKE_CASE = expand_ratios
_SCREAMING_SNAKE_CASE = squeeze_expansion_ratio
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dim
_SCREAMING_SNAKE_CASE = pooling_type
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = batch_norm_eps
_SCREAMING_SNAKE_CASE = batch_norm_momentum
_SCREAMING_SNAKE_CASE = drop_connect_rate
_SCREAMING_SNAKE_CASE = sum(UpperCamelCase__ ) * 4
@classmethod
def __magic_name__ ( cls , A_ , **A_ )-> "PretrainedConfig":
cls._set_token_in_kwargs(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = cls.get_config_dict(UpperCamelCase__ , **UpperCamelCase__ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get('model_type' ) == "align":
_SCREAMING_SNAKE_CASE = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "align"
SCREAMING_SNAKE_CASE : Union[str, Any] = True
def __init__( self , A_=None , A_=None , A_=640 , A_=1.0 , A_=0.02 , **A_ , )-> Union[str, Any]:
super().__init__(**UpperCamelCase__ )
if text_config is None:
_SCREAMING_SNAKE_CASE = {}
logger.info('text_config is None. Initializing the AlignTextConfig with default values.' )
if vision_config is None:
_SCREAMING_SNAKE_CASE = {}
logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.' )
_SCREAMING_SNAKE_CASE = AlignTextConfig(**UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = AlignVisionConfig(**UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = projection_dim
_SCREAMING_SNAKE_CASE = temperature_init_value
_SCREAMING_SNAKE_CASE = initializer_range
@classmethod
def __magic_name__ ( cls , A_ , A_ , **A_ )-> List[Any]:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **UpperCamelCase__ )
def __magic_name__ ( self )-> Optional[int]:
_SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
_SCREAMING_SNAKE_CASE = self.text_config.to_dict()
_SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
_SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 605
|
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class lowerCAmelCase ( a ):
"""simple docstring"""
__lowercase :torch.FloatTensor
class lowerCAmelCase ( a , a ):
"""simple docstring"""
@register_to_config
def __init__( self , UpperCamelCase__ = 3 , UpperCamelCase__ = 3 , UpperCamelCase__ = ("DownEncoderBlock2D",) , UpperCamelCase__ = ("UpDecoderBlock2D",) , UpperCamelCase__ = (64,) , UpperCamelCase__ = 1 , UpperCamelCase__ = "silu" , UpperCamelCase__ = 3 , UpperCamelCase__ = 32 , UpperCamelCase__ = 256 , UpperCamelCase__ = 32 , UpperCamelCase__ = None , UpperCamelCase__ = 0.18_215 , UpperCamelCase__ = "group" , ) -> Any:
'''simple docstring'''
super().__init__()
# pass init params to Encoder
lowerCamelCase_ = Encoder(
in_channels=UpperCamelCase__ , out_channels=UpperCamelCase__ , down_block_types=UpperCamelCase__ , block_out_channels=UpperCamelCase__ , layers_per_block=UpperCamelCase__ , act_fn=UpperCamelCase__ , norm_num_groups=UpperCamelCase__ , double_z=UpperCamelCase__ , )
lowerCamelCase_ = vq_embed_dim if vq_embed_dim is not None else latent_channels
lowerCamelCase_ = nn.Convad(UpperCamelCase__ , UpperCamelCase__ , 1 )
lowerCamelCase_ = VectorQuantizer(UpperCamelCase__ , UpperCamelCase__ , beta=0.25 , remap=UpperCamelCase__ , sane_index_shape=UpperCamelCase__ )
lowerCamelCase_ = nn.Convad(UpperCamelCase__ , UpperCamelCase__ , 1 )
# pass init params to Decoder
lowerCamelCase_ = Decoder(
in_channels=UpperCamelCase__ , out_channels=UpperCamelCase__ , up_block_types=UpperCamelCase__ , block_out_channels=UpperCamelCase__ , layers_per_block=UpperCamelCase__ , act_fn=UpperCamelCase__ , norm_num_groups=UpperCamelCase__ , norm_type=UpperCamelCase__ , )
@apply_forward_hook
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ = True ) -> VQEncoderOutput:
'''simple docstring'''
lowerCamelCase_ = self.encoder(UpperCamelCase__ )
lowerCamelCase_ = self.quant_conv(UpperCamelCase__ )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=UpperCamelCase__ )
@apply_forward_hook
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ = False , UpperCamelCase__ = True ) -> Union[DecoderOutput, torch.FloatTensor]:
'''simple docstring'''
if not force_not_quantize:
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = self.quantize(UpperCamelCase__ )
else:
lowerCamelCase_ = h
lowerCamelCase_ = self.post_quant_conv(UpperCamelCase__ )
lowerCamelCase_ = self.decoder(UpperCamelCase__ , quant if self.config.norm_type == '''spatial''' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCamelCase__ )
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ = True ) -> Union[DecoderOutput, torch.FloatTensor]:
'''simple docstring'''
lowerCamelCase_ = sample
lowerCamelCase_ = self.encode(UpperCamelCase__ ).latents
lowerCamelCase_ = self.decode(UpperCamelCase__ ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCamelCase__ )
| 142
| 0
|
"""simple docstring"""
import re
def __UpperCAmelCase ( _snake_case : str ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]", str_ )]
def __UpperCAmelCase ( _snake_case : str ):
_lowercase = split_input(str_ )
return "".join(
["".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def __UpperCAmelCase ( _snake_case : str, _snake_case : bool, _snake_case : str ):
try:
_lowercase = split_input(_snake_case )
if upper:
_lowercase = "".join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
_lowercase = "".join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def __UpperCAmelCase ( _snake_case : str ):
return to_simple_case(_snake_case )
def __UpperCAmelCase ( _snake_case : str ):
try:
_lowercase = to_simple_case(_snake_case )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def __UpperCAmelCase ( _snake_case : str, _snake_case : bool ):
return to_complex_case(_snake_case, _snake_case, "_" )
def __UpperCAmelCase ( _snake_case : str, _snake_case : bool ):
return to_complex_case(_snake_case, _snake_case, "-" )
if __name__ == "__main__":
__import__("doctest").testmod()
| 227
|
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
class UpperCAmelCase_ :
def __init__( self : int , _lowercase : Dict , _lowercase : List[Any] ) -> Optional[int]:
_lowercase = question_encoder
_lowercase = generator
_lowercase = self.question_encoder
def _lowerCamelCase ( self : int , _lowercase : Any ) -> str:
if os.path.isfile(_lowercase ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(_lowercase , exist_ok=_lowercase )
_lowercase = os.path.join(_lowercase , "question_encoder_tokenizer" )
_lowercase = os.path.join(_lowercase , "generator_tokenizer" )
self.question_encoder.save_pretrained(_lowercase )
self.generator.save_pretrained(_lowercase )
@classmethod
def _lowerCamelCase ( cls : Union[str, Any] , _lowercase : Optional[Any] , **_lowercase : Dict ) -> List[str]:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_lowercase = kwargs.pop("config" , _lowercase )
if config is None:
_lowercase = RagConfig.from_pretrained(_lowercase )
_lowercase = AutoTokenizer.from_pretrained(
_lowercase , config=config.question_encoder , subfolder="question_encoder_tokenizer" )
_lowercase = AutoTokenizer.from_pretrained(
_lowercase , config=config.generator , subfolder="generator_tokenizer" )
return cls(question_encoder=_lowercase , generator=_lowercase )
def __call__( self : str , *_lowercase : Tuple , **_lowercase : Optional[Any] ) -> str:
return self.current_tokenizer(*_lowercase , **_lowercase )
def _lowerCamelCase ( self : str , *_lowercase : Any , **_lowercase : Optional[int] ) -> List[str]:
return self.generator.batch_decode(*_lowercase , **_lowercase )
def _lowerCamelCase ( self : List[Any] , *_lowercase : Dict , **_lowercase : str ) -> Optional[Any]:
return self.generator.decode(*_lowercase , **_lowercase )
def _lowerCamelCase ( self : List[Any] ) -> Optional[Any]:
_lowercase = self.question_encoder
def _lowerCamelCase ( self : Dict ) -> int:
_lowercase = self.generator
def _lowerCamelCase ( self : int , _lowercase : List[str] , _lowercase : Optional[List[str]] = None , _lowercase : Optional[int] = None , _lowercase : Optional[int] = None , _lowercase : str = "longest" , _lowercase : str = None , _lowercase : bool = True , **_lowercase : List[Any] , ) -> BatchEncoding:
warnings.warn(
"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
"regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
"details" , _lowercase , )
if max_length is None:
_lowercase = self.current_tokenizer.model_max_length
_lowercase = self(
_lowercase , add_special_tokens=_lowercase , return_tensors=_lowercase , max_length=_lowercase , padding=_lowercase , truncation=_lowercase , **_lowercase , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_lowercase = self.current_tokenizer.model_max_length
_lowercase = self(
text_target=_lowercase , add_special_tokens=_lowercase , return_tensors=_lowercase , padding=_lowercase , max_length=_lowercase , truncation=_lowercase , **_lowercase , )
_lowercase = labels["input_ids"]
return model_inputs
| 227
| 1
|
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : List[Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ (lowerCAmelCase__ , unittest.TestCase ):
lowercase_ : Any = XLMRobertaTokenizer
lowercase_ : Dict = XLMRobertaTokenizerFast
lowercase_ : Union[str, Any] = True
lowercase_ : Optional[Any] = True
def A__ ( self : Union[str, Any] ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = XLMRobertaTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def A__ ( self : Union[str, Any] ):
"""simple docstring"""
lowerCAmelCase__ = "<pad>"
lowerCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def A__ ( self : Dict ):
"""simple docstring"""
lowerCAmelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(__UpperCAmelCase ) , 10_02 )
def A__ ( self : List[Any] ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def A__ ( self : List[str] ):
"""simple docstring"""
lowerCAmelCase__ = XLMRobertaTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
lowerCAmelCase__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
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''',
'''.''',
] , )
lowerCAmelCase__ = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def A__ ( self : Optional[Any] ):
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCAmelCase__ = (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
lowerCAmelCase__ = tempfile.mkdtemp()
lowerCAmelCase__ = tokenizer_r.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_p.save_pretrained(__UpperCAmelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
lowerCAmelCase__ = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__UpperCAmelCase , __UpperCAmelCase )
# Checks everything loads correctly in the same way
lowerCAmelCase__ = tokenizer_r.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_p.from_pretrained(__UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCAmelCase , __UpperCAmelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__UpperCAmelCase )
# Save tokenizer rust, legacy_format=True
lowerCAmelCase__ = tempfile.mkdtemp()
lowerCAmelCase__ = tokenizer_r.save_pretrained(__UpperCAmelCase , legacy_format=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_p.save_pretrained(__UpperCAmelCase )
# Checks it save with the same files
self.assertSequenceEqual(__UpperCAmelCase , __UpperCAmelCase )
# Checks everything loads correctly in the same way
lowerCAmelCase__ = tokenizer_r.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_p.from_pretrained(__UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCAmelCase , __UpperCAmelCase ) )
shutil.rmtree(__UpperCAmelCase )
# Save tokenizer rust, legacy_format=False
lowerCAmelCase__ = tempfile.mkdtemp()
lowerCAmelCase__ = tokenizer_r.save_pretrained(__UpperCAmelCase , legacy_format=__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_p.save_pretrained(__UpperCAmelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCAmelCase__ = tokenizer_r.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ = tokenizer_p.from_pretrained(__UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCAmelCase , __UpperCAmelCase ) )
shutil.rmtree(__UpperCAmelCase )
@cached_property
def A__ ( self : int ):
"""simple docstring"""
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def A__ ( self : List[str] ):
"""simple docstring"""
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__UpperCAmelCase , f.name )
lowerCAmelCase__ = XLMRobertaTokenizer(f.name , keep_accents=__UpperCAmelCase )
lowerCAmelCase__ = pickle.dumps(__UpperCAmelCase )
pickle.loads(__UpperCAmelCase )
def A__ ( self : Any ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = "I was born in 92000, and this is falsé."
lowerCAmelCase__ = tokenizer.tokenize(__UpperCAmelCase )
lowerCAmelCase__ = rust_tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
lowerCAmelCase__ = rust_tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = tokenizer.encode(__UpperCAmelCase )
lowerCAmelCase__ = rust_tokenizer.encode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
@slow
def A__ ( self : int ):
"""simple docstring"""
lowerCAmelCase__ = "Hello World!"
lowerCAmelCase__ = [0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def A__ ( self : Tuple ):
"""simple docstring"""
lowerCAmelCase__ = (
"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"
)
lowerCAmelCase__ = [
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def A__ ( self : Optional[int] ):
"""simple docstring"""
# fmt: off
lowerCAmelCase__ = {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 615
|
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class _a ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : Dict = ReformerTokenizer
lowerCamelCase_ : int = ReformerTokenizerFast
lowerCamelCase_ : Optional[Any] = True
lowerCamelCase_ : List[str] = False
lowerCamelCase_ : Optional[int] = True
def __UpperCAmelCase( self ):
super().setUp()
__A : List[Any] = ReformerTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase( self ):
__A : Any = "<s>"
__A : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def __UpperCAmelCase( self ):
__A : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(__UpperCAmelCase ) , 1_000 )
def __UpperCAmelCase( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def __UpperCAmelCase( self ):
if not self.test_rust_tokenizer:
return
__A : Union[str, Any] = self.get_tokenizer()
__A : Optional[int] = self.get_rust_tokenizer()
__A : Union[str, Any] = "I was born in 92000, and this is falsé."
__A : List[str] = tokenizer.tokenize(__UpperCAmelCase )
__A : List[Any] = rust_tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
__A : List[Any] = tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
__A : Optional[Any] = rust_tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
__A : List[Any] = self.get_rust_tokenizer()
__A : Union[str, Any] = tokenizer.encode(__UpperCAmelCase )
__A : Union[str, Any] = rust_tokenizer.encode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __UpperCAmelCase( self , __UpperCAmelCase=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
__A : Tuple = self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
# Simple input
__A : Optional[int] = "This is a simple input"
__A : Tuple = ["This is a simple input 1", "This is a simple input 2"]
__A : Any = ("This is a simple input", "This is a pair")
__A : Any = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__UpperCAmelCase , tokenizer_r.encode , __UpperCAmelCase , max_length=__UpperCAmelCase , padding="max_length" )
# Simple input
self.assertRaises(__UpperCAmelCase , tokenizer_r.encode_plus , __UpperCAmelCase , max_length=__UpperCAmelCase , padding="max_length" )
# Simple input
self.assertRaises(
__UpperCAmelCase , tokenizer_r.batch_encode_plus , __UpperCAmelCase , max_length=__UpperCAmelCase , padding="max_length" , )
# Pair input
self.assertRaises(__UpperCAmelCase , tokenizer_r.encode , __UpperCAmelCase , max_length=__UpperCAmelCase , padding="max_length" )
# Pair input
self.assertRaises(__UpperCAmelCase , tokenizer_r.encode_plus , __UpperCAmelCase , max_length=__UpperCAmelCase , padding="max_length" )
# Pair input
self.assertRaises(
__UpperCAmelCase , tokenizer_r.batch_encode_plus , __UpperCAmelCase , max_length=__UpperCAmelCase , padding="max_length" , )
def __UpperCAmelCase( self ):
pass
def __UpperCAmelCase( self ):
__A : Tuple = ReformerTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
__A : List[str] = tokenizer.tokenize("This is a test" )
self.assertListEqual(__UpperCAmelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [285, 46, 10, 170, 382] , )
__A : Optional[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
__UpperCAmelCase , [
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",
".",
] , )
__A : Tuple = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__A : int = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
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 __UpperCAmelCase( self ):
return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment" )
@slow
def __UpperCAmelCase( self ):
__A : Any = "Hello World!"
__A : Optional[int] = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def __UpperCAmelCase( self ):
__A : Optional[int] = (
"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"
)
__A : Union[str, Any] = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@require_torch
@slow
def __UpperCAmelCase( self ):
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
__A : Tuple = list(self.big_tokenizer.get_vocab().keys() )[:10]
__A : Optional[Any] = " ".join(__UpperCAmelCase )
__A : Tuple = self.big_tokenizer.encode_plus(__UpperCAmelCase , return_tensors="pt" )
__A : Dict = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="pt" )
__A : Union[str, Any] = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
__A : Dict = encoded_sequence["input_ids"].shape
__A : int = ReformerModel(__UpperCAmelCase )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__UpperCAmelCase )
model(**__UpperCAmelCase )
@slow
def __UpperCAmelCase( self ):
# fmt: off
__A : Dict = {"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "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]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
__A : int = [
"This is a very simple sentence.",
"The quick brown fox jumps over the lazy dog.",
]
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name="google/reformer-crime-and-punishment" , revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a" , padding=__UpperCAmelCase , sequences=__UpperCAmelCase , )
| 520
| 0
|
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( _A , _A , _A , _A="attention" ):
"""simple docstring"""
a_ = params[f"{prefix}/layers_{i}/{layer_name}/key/kernel"]
a_ = params[f"{prefix}/layers_{i}/{layer_name}/out/kernel"]
a_ = params[f"{prefix}/layers_{i}/{layer_name}/query/kernel"]
a_ = params[f"{prefix}/layers_{i}/{layer_name}/value/kernel"]
return k, o, q, v
def UpperCAmelCase__ ( _A , _A , _A , _A=False ):
"""simple docstring"""
if split_mlp_wi:
a_ = params[f"{prefix}/layers_{i}/mlp/wi_0/kernel"]
a_ = params[f"{prefix}/layers_{i}/mlp/wi_1/kernel"]
a_ = (wi_a, wi_a)
else:
a_ = params[f"{prefix}/layers_{i}/mlp/wi/kernel"]
a_ = params[f"{prefix}/layers_{i}/mlp/wo/kernel"]
return wi, wo
def UpperCAmelCase__ ( _A , _A , _A , _A ):
"""simple docstring"""
return params[f"{prefix}/layers_{i}/{layer_name}/scale"]
def UpperCAmelCase__ ( _A , *, _A , _A ):
"""simple docstring"""
a_ = traverse_util.flatten_dict(variables['''target'''] )
a_ = {'''/'''.join(_A ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
a_ = '''encoder/layers_0/mlp/wi_0/kernel''' in old
print('''Split MLP:''' , _A )
a_ = collections.OrderedDict()
# Shared embeddings.
a_ = old['''token_embedder/embedding''']
# Encoder.
for i in range(_A ):
# Block i, layer 0 (Self Attention).
a_ = tax_layer_norm_lookup(_A , _A , '''encoder''' , '''pre_attention_layer_norm''' )
a_ , a_ , a_ , a_ = tax_attention_lookup(_A , _A , '''encoder''' , '''attention''' )
a_ = layer_norm
a_ = k.T
a_ = o.T
a_ = q.T
a_ = v.T
# Block i, layer 1 (MLP).
a_ = tax_layer_norm_lookup(_A , _A , '''encoder''' , '''pre_mlp_layer_norm''' )
a_ , a_ = tax_mlp_lookup(_A , _A , '''encoder''' , _A )
a_ = layer_norm
if split_mlp_wi:
a_ = wi[0].T
a_ = wi[1].T
else:
a_ = wi.T
a_ = wo.T
a_ = old[
'''encoder/relpos_bias/rel_embedding'''
].T
a_ = old['''encoder/encoder_norm/scale''']
if not is_encoder_only:
# Decoder.
for i in range(_A ):
# Block i, layer 0 (Self Attention).
a_ = tax_layer_norm_lookup(_A , _A , '''decoder''' , '''pre_self_attention_layer_norm''' )
a_ , a_ , a_ , a_ = tax_attention_lookup(_A , _A , '''decoder''' , '''self_attention''' )
a_ = layer_norm
a_ = k.T
a_ = o.T
a_ = q.T
a_ = v.T
# Block i, layer 1 (Cross Attention).
a_ = tax_layer_norm_lookup(_A , _A , '''decoder''' , '''pre_cross_attention_layer_norm''' )
a_ , a_ , a_ , a_ = tax_attention_lookup(_A , _A , '''decoder''' , '''encoder_decoder_attention''' )
a_ = layer_norm
a_ = k.T
a_ = o.T
a_ = q.T
a_ = v.T
# Block i, layer 2 (MLP).
a_ = tax_layer_norm_lookup(_A , _A , '''decoder''' , '''pre_mlp_layer_norm''' )
a_ , a_ = tax_mlp_lookup(_A , _A , '''decoder''' , _A )
a_ = layer_norm
if split_mlp_wi:
a_ = wi[0].T
a_ = wi[1].T
else:
a_ = wi.T
a_ = wo.T
a_ = old['''decoder/decoder_norm/scale''']
a_ = old[
'''decoder/relpos_bias/rel_embedding'''
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
a_ = old['''decoder/logits_dense/kernel'''].T
return new
def UpperCAmelCase__ ( _A , _A ):
"""simple docstring"""
a_ = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
a_ = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
a_ = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
a_ = state_dict['''shared.weight''']
return state_dict
def UpperCAmelCase__ ( _A , _A , _A , _A ):
"""simple docstring"""
a_ = checkpoints.load_tax_checkpoint(_A )
a_ = convert_tax_to_pytorch(_A , num_layers=config.num_layers , is_encoder_only=_A )
a_ = make_state_dict(_A , _A )
model.load_state_dict(_A , strict=_A )
def UpperCAmelCase__ ( _A , _A , _A , _A = False ):
"""simple docstring"""
a_ = TaConfig.from_json_file(_A )
print(f"Building PyTorch model from configuration: {config}" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
a_ = TaEncoderModel(_A )
else:
a_ = TaForConditionalGeneration(_A )
# Load weights from tf checkpoint
load_tax_weights_in_ta(_A , _A , _A , _A )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(_A )
# Verify that we can load the checkpoint.
model.from_pretrained(_A )
print('''Done''' )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
UpperCamelCase__ = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 143
|
from __future__ import annotations
def UpperCAmelCase__ ( _A ):
"""simple docstring"""
a_ = [True] * limit
a_ = False
a_ = False
a_ = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
a_ = i * 2
while index < limit:
a_ = False
a_ = index + i
a_ = [2]
for i in range(3 , _A , 2 ):
if is_prime[i]:
primes.append(_A )
return primes
def UpperCAmelCase__ ( _A = 1_000_000 ):
"""simple docstring"""
a_ = prime_sieve(_A )
a_ = 0
a_ = 0
for i in range(len(_A ) ):
for j in range(i + length , len(_A ) ):
a_ = sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
a_ = j - i
a_ = sol
return largest
if __name__ == "__main__":
print(F"""{solution() = }""")
| 143
| 1
|
import random
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = a[left_index]
_UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , snake_case ):
if a[j] < pivot:
_UpperCAmelCase , _UpperCAmelCase = a[i], a[j]
i += 1
_UpperCAmelCase , _UpperCAmelCase = a[i - 1], a[left_index]
return i - 1
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> List[Any]:
if left < right:
_UpperCAmelCase = random.randint(snake_case , right - 1 )
_UpperCAmelCase , _UpperCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase = partition(snake_case , snake_case , snake_case )
quick_sort_random(
snake_case , snake_case , snake_case ) # recursive quicksort to the left of the pivot point
quick_sort_random(
snake_case , pivot_index + 1 , snake_case ) # recursive quicksort to the right of the pivot point
def _SCREAMING_SNAKE_CASE ( ) -> List[str]:
_UpperCAmelCase = input("""Enter numbers separated by a comma:\n""" ).strip()
_UpperCAmelCase = [int(snake_case ) for item in user_input.split(""",""" )]
quick_sort_random(snake_case , 0 , len(snake_case ) )
print(snake_case )
if __name__ == "__main__":
main()
| 518
|
def _SCREAMING_SNAKE_CASE ( snake_case = 1_0_0_0 ) -> int:
_UpperCAmelCase , _UpperCAmelCase = 1, 1
_UpperCAmelCase = []
for i in range(1 , n + 1 ):
_UpperCAmelCase = prev_numerator + 2 * prev_denominator
_UpperCAmelCase = prev_numerator + prev_denominator
if len(str(snake_case ) ) > len(str(snake_case ) ):
result.append(snake_case )
_UpperCAmelCase = numerator
_UpperCAmelCase = denominator
return len(snake_case )
if __name__ == "__main__":
print(F'{solution() = }')
| 518
| 1
|
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class __SCREAMING_SNAKE_CASE :
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] ):
return None
class __SCREAMING_SNAKE_CASE :
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str] ):
return None
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
__SCREAMING_SNAKE_CASE : Optional[int] = [
# (model_name, model_kwargs)
("""bert-base-cased""", {}),
("""gpt2""", {"""use_cache""": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def UpperCAmelCase__ ( self : Optional[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__UpperCamelCase , "tf" , 12 , **__UpperCamelCase )
@require_torch
@slow
def UpperCAmelCase__ ( self : List[Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__UpperCamelCase , "pt" , 12 , **__UpperCamelCase )
@require_torch
@slow
def UpperCAmelCase__ ( self : Any ):
from transformers import BertModel
_UpperCAmelCase = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t" ) as vocab_file:
vocab_file.write("\n".join(__UpperCamelCase ) )
vocab_file.flush()
_UpperCAmelCase = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
_UpperCAmelCase = BertModel(BertConfig(vocab_size=len(__UpperCamelCase ) ) )
model.save_pretrained(__UpperCamelCase )
self._test_export(__UpperCamelCase , "pt" , 12 , __UpperCamelCase )
@require_tf
@slow
def UpperCAmelCase__ ( self : Optional[int] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_UpperCAmelCase = self._test_export(__UpperCamelCase , "tf" , 12 , **__UpperCamelCase )
_UpperCAmelCase = quantize(Path(__UpperCamelCase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__UpperCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
@require_torch
@slow
def UpperCAmelCase__ ( self : Union[str, Any] ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_UpperCAmelCase = self._test_export(__UpperCamelCase , "pt" , 12 , **__UpperCamelCase )
_UpperCAmelCase = quantize(__UpperCamelCase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__UpperCamelCase ).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model" )
def UpperCAmelCase__ ( self : int , __UpperCamelCase : Tuple , __UpperCamelCase : int , __UpperCamelCase : Dict , __UpperCamelCase : Any=None , **__UpperCamelCase : Optional[Any] ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
_UpperCAmelCase = Path(__UpperCamelCase ).joinpath("model.onnx" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
return path
except Exception as e:
self.fail(__UpperCamelCase )
@require_torch
@require_tokenizers
@slow
def UpperCAmelCase__ ( self : List[Any] ):
from transformers import BertModel
_UpperCAmelCase = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
_UpperCAmelCase = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__UpperCamelCase , __UpperCamelCase , "pt" )
@require_tf
@require_tokenizers
@slow
def UpperCAmelCase__ ( self : List[str] ):
from transformers import TFBertModel
_UpperCAmelCase = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
_UpperCAmelCase = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
self._test_infer_dynamic_axis(__UpperCamelCase , __UpperCamelCase , "tf" )
def UpperCAmelCase__ ( self : Optional[int] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : int ):
_UpperCAmelCase = FeatureExtractionPipeline(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = infer_shapes(__UpperCamelCase , __UpperCamelCase )
# Assert all variables are present
self.assertEqual(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , __UpperCamelCase )
self.assertSequenceEqual(variable_names[3:] , __UpperCamelCase )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: "batch", 1: "sequence"} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["output_0"] , {0: "batch", 1: "sequence"} )
self.assertDictEqual(shapes["output_1"] , {0: "batch"} )
def UpperCAmelCase__ ( self : Tuple ):
_UpperCAmelCase = ["input_ids", "attention_mask", "token_type_ids"]
_UpperCAmelCase = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
_UpperCAmelCase , _UpperCAmelCase = ensure_valid_input(FuncContiguousArgs() , __UpperCamelCase , __UpperCamelCase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__UpperCamelCase ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(__UpperCamelCase ) , set(__UpperCamelCase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__UpperCamelCase , (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
_UpperCAmelCase , _UpperCAmelCase = ensure_valid_input(FuncNonContiguousArgs() , __UpperCamelCase , __UpperCamelCase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__UpperCamelCase ) , 1 )
self.assertEqual(len(__UpperCamelCase ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["input_ids"] )
self.assertEqual(ordered_input_names[0] , "input_ids" )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = generate_identified_filename(Path("/home/something/my_fake_model.onnx" ) , "-test" )
self.assertEqual("/home/something/my_fake_model-test.onnx" , generated.as_posix() )
| 705
|
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str]=13 , __UpperCamelCase : str=[30, 30] , __UpperCamelCase : Optional[int]=2 , __UpperCamelCase : int=3 , __UpperCamelCase : int=True , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : Any=32 , __UpperCamelCase : List[str]=5 , __UpperCamelCase : Optional[int]=4 , __UpperCamelCase : Tuple=37 , __UpperCamelCase : Optional[int]="gelu" , __UpperCamelCase : List[str]=0.1 , __UpperCamelCase : Dict=0.1 , __UpperCamelCase : List[str]=10 , __UpperCamelCase : Any=0.02 , __UpperCamelCase : str=3 , __UpperCamelCase : List[Any]=None , __UpperCamelCase : Optional[int]=8 , __UpperCamelCase : str=10 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
_UpperCAmelCase = n_targets
_UpperCAmelCase = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
_UpperCAmelCase = (image_size[1] // patch_size) * (image_size[0] // patch_size)
_UpperCAmelCase = num_patches + 1 + self.num_detection_tokens
def UpperCAmelCase__ ( self : int ):
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
_UpperCAmelCase = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
_UpperCAmelCase = []
for i in range(self.batch_size ):
_UpperCAmelCase = {}
_UpperCAmelCase = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=__UpperCamelCase )
_UpperCAmelCase = torch.rand(self.n_targets , 4 , device=__UpperCamelCase )
labels.append(__UpperCamelCase )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase__ ( self : Tuple ):
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def UpperCAmelCase__ ( self : int , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : str ):
_UpperCAmelCase = YolosModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : int ):
_UpperCAmelCase = YolosForObjectDetection(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
_UpperCAmelCase = model(pixel_values=__UpperCamelCase )
_UpperCAmelCase = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
_UpperCAmelCase = model(pixel_values=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowercase , lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : int = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : List[str] = (
{"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
)
__SCREAMING_SNAKE_CASE : Any = False
__SCREAMING_SNAKE_CASE : str = False
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : Dict = False
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : int , __UpperCamelCase : str=False ):
_UpperCAmelCase = super()._prepare_for_class(__UpperCamelCase , __UpperCamelCase , return_labels=__UpperCamelCase )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
_UpperCAmelCase = []
for i in range(self.model_tester.batch_size ):
_UpperCAmelCase = {}
_UpperCAmelCase = torch.ones(
size=(self.model_tester.n_targets,) , device=__UpperCamelCase , dtype=torch.long )
_UpperCAmelCase = torch.ones(
self.model_tester.n_targets , 4 , device=__UpperCamelCase , dtype=torch.float )
labels.append(__UpperCamelCase )
_UpperCAmelCase = labels
return inputs_dict
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = YolosModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 )
def UpperCAmelCase__ ( self : Dict ):
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : List[Any] ):
# YOLOS does not use inputs_embeds
pass
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(__UpperCamelCase )
_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] , __UpperCamelCase )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
# in YOLOS, the seq_len is different
_UpperCAmelCase = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase = True
_UpperCAmelCase = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
_UpperCAmelCase = len(__UpperCamelCase )
# Check attention is always last and order is fine
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
_UpperCAmelCase = 1
self.assertEqual(out_len + added_hidden_states , len(__UpperCamelCase ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def UpperCAmelCase__ ( self : Tuple ):
def check_hidden_states_output(__UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : List[str] ):
_UpperCAmelCase = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# YOLOS has a different seq_length
_UpperCAmelCase = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
_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(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*__UpperCamelCase )
@slow
def UpperCAmelCase__ ( self : Optional[int] ):
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = YolosModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def __lowerCamelCase ( ) -> Any:
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
@cached_property
def UpperCAmelCase__ ( self : Union[str, Any] ):
return AutoImageProcessor.from_pretrained("hustvl/yolos-small" ) if is_vision_available() else None
@slow
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = YolosForObjectDetection.from_pretrained("hustvl/yolos-small" ).to(__UpperCamelCase )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=__UpperCamelCase , return_tensors="pt" ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(inputs.pixel_values )
# verify outputs
_UpperCAmelCase = torch.Size((1, 100, 92) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
_UpperCAmelCase = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] , device=__UpperCamelCase , )
_UpperCAmelCase = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] , device=__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , __UpperCamelCase , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , __UpperCamelCase , atol=1e-4 ) )
# verify postprocessing
_UpperCAmelCase = image_processor.post_process_object_detection(
__UpperCamelCase , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
_UpperCAmelCase = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861] ).to(__UpperCamelCase )
_UpperCAmelCase = [75, 75, 17, 63, 17]
_UpperCAmelCase = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495] ).to(__UpperCamelCase )
self.assertEqual(len(results["scores"] ) , 5 )
self.assertTrue(torch.allclose(results["scores"] , __UpperCamelCase , atol=1e-4 ) )
self.assertSequenceEqual(results["labels"].tolist() , __UpperCamelCase )
self.assertTrue(torch.allclose(results["boxes"][0, :] , __UpperCamelCase ) )
| 129
| 0
|
'''simple docstring'''
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__lowerCAmelCase : Dict = logging.get_logger(__name__)
def lowerCAmelCase ( UpperCamelCase__ : Tuple , UpperCamelCase__ : Any ):
"""simple docstring"""
__UpperCAmelCase = set()
__UpperCAmelCase = []
def parse_line(UpperCamelCase__ : Tuple ):
for line in fp:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__UpperCAmelCase = line.decode('''UTF-8''' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(''' ''' ):
# process a single warning and move it to `selected_warnings`.
if len(UpperCamelCase__ ) > 0:
__UpperCAmelCase = '''\n'''.join(UpperCamelCase__ )
# Only keep the warnings specified in `targets`
if any(f""": {x}: """ in warning for x in targets ):
selected_warnings.add(UpperCamelCase__ )
buffer.clear()
continue
else:
__UpperCAmelCase = line.strip()
buffer.append(UpperCamelCase__ )
if from_gh:
for filename in os.listdir(UpperCamelCase__ ):
__UpperCAmelCase = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
if not os.path.isdir(UpperCamelCase__ ):
# read the file
if filename != "warnings.txt":
continue
with open(UpperCamelCase__ ) as fp:
parse_line(UpperCamelCase__ )
else:
try:
with zipfile.ZipFile(UpperCamelCase__ ) as z:
for filename in z.namelist():
if not os.path.isdir(UpperCamelCase__ ):
# read the file
if filename != "warnings.txt":
continue
with z.open(UpperCamelCase__ ) as fp:
parse_line(UpperCamelCase__ )
except Exception:
logger.warning(
f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.""" )
return selected_warnings
def lowerCAmelCase ( UpperCamelCase__ : str , UpperCamelCase__ : Tuple ):
"""simple docstring"""
__UpperCAmelCase = set()
__UpperCAmelCase = [os.path.join(UpperCamelCase__ , UpperCamelCase__ ) for p in os.listdir(UpperCamelCase__ ) if (p.endswith('''.zip''' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(UpperCamelCase__ , UpperCamelCase__ ) )
return selected_warnings
if __name__ == "__main__":
def lowerCAmelCase ( UpperCamelCase__ : int ):
"""simple docstring"""
return values.split(''',''' )
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Where to store the downloaded artifacts and other result files.",
)
parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
# optional parameters
parser.add_argument(
"--targets",
default="DeprecationWarning,UserWarning,FutureWarning",
type=list_str,
help="Comma-separated list of target warning(s) which we want to extract.",
)
parser.add_argument(
"--from_gh",
action="store_true",
help="If running from a GitHub action workflow and collecting warnings from its artifacts.",
)
__lowerCAmelCase : Tuple = parser.parse_args()
__lowerCAmelCase : str = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__lowerCAmelCase : str = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("=" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__lowerCAmelCase : str = extract_warnings(args.output_dir, args.targets)
__lowerCAmelCase : Optional[Any] = sorted(selected_warnings)
with open(os.path.join(args.output_dir, "selected_warnings.json"), "w", encoding="UTF-8") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 262
|
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class A :
def __init__( self : List[str] , __a : Any , __a : int=9_9 , __a : Any=1_3 , __a : Tuple=7 , __a : Tuple=9 , __a : Tuple=True , __a : Union[str, Any]=True , __a : List[Any]=False , __a : Optional[Any]=3_2 , __a : str=5 , __a : Optional[int]=4 , __a : Union[str, Any]=3_7 , __a : List[str]=8 , __a : Optional[int]=0.1 , __a : List[str]=0.0_0_2 , __a : List[Any]=1 , __a : str=0 , __a : Dict=0 , __a : int=None , __a : List[Any]=None , ) -> Tuple:
__UpperCAmelCase = parent
__UpperCAmelCase = batch_size
__UpperCAmelCase = encoder_seq_length
__UpperCAmelCase = decoder_seq_length
# For common tests
__UpperCAmelCase = self.decoder_seq_length
__UpperCAmelCase = is_training
__UpperCAmelCase = use_attention_mask
__UpperCAmelCase = use_labels
__UpperCAmelCase = vocab_size
__UpperCAmelCase = hidden_size
__UpperCAmelCase = num_hidden_layers
__UpperCAmelCase = num_attention_heads
__UpperCAmelCase = d_ff
__UpperCAmelCase = relative_attention_num_buckets
__UpperCAmelCase = dropout_rate
__UpperCAmelCase = initializer_factor
__UpperCAmelCase = eos_token_id
__UpperCAmelCase = pad_token_id
__UpperCAmelCase = decoder_start_token_id
__UpperCAmelCase = None
__UpperCAmelCase = decoder_layers
def snake_case__ ( self : Union[str, Any] ) -> int:
return TaConfig.from_pretrained('''google/umt5-base''' )
def snake_case__ ( self : List[Any] , __a : List[str] , __a : str , __a : Optional[int] , __a : List[Any]=None , __a : List[Any]=None , __a : Any=None , __a : str=None , __a : Any=None , ) -> List[Any]:
if attention_mask is None:
__UpperCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
__UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
__UpperCAmelCase = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=__a )
if decoder_head_mask is None:
__UpperCAmelCase = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=__a )
if cross_attn_head_mask is None:
__UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=__a )
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,
}
def snake_case__ ( self : List[str] ) -> Dict:
__UpperCAmelCase = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
__UpperCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
__UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
__UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
__UpperCAmelCase = self.get_config()
__UpperCAmelCase = config.num_attention_heads
__UpperCAmelCase = self.prepare_inputs_dict(__a , __a , __a )
return config, input_dict
def snake_case__ ( self : Union[str, Any] ) -> Optional[Any]:
__UpperCAmelCase , __UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def snake_case__ ( self : int ) -> Optional[int]:
return TaConfig(
vocab_size=1_6_6 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def snake_case__ ( self : Optional[int] ) -> Any:
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def snake_case__ ( self : int , __a : Any , __a : Union[str, Any] , __a : List[Any] , __a : Dict , __a : Optional[Any] , __a : int , ) -> List[Any]:
__UpperCAmelCase = UMTaModel(config=__a )
model.to(__a )
model.eval()
__UpperCAmelCase = model(
input_ids=__a , decoder_input_ids=__a , attention_mask=__a , decoder_attention_mask=__a , )
__UpperCAmelCase = model(input_ids=__a , decoder_input_ids=__a )
__UpperCAmelCase = result.last_hidden_state
__UpperCAmelCase = result.past_key_values
__UpperCAmelCase = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(__a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def snake_case__ ( self : List[str] , __a : Any , __a : Tuple , __a : List[str] , __a : Optional[Any] , __a : Dict , __a : Any , ) -> Optional[Any]:
__UpperCAmelCase = UMTaModel(config=__a ).get_decoder().to(__a ).eval()
# first forward pass
__UpperCAmelCase = model(__a , use_cache=__a )
__UpperCAmelCase = model(__a )
__UpperCAmelCase = model(__a , use_cache=__a )
self.parent.assertTrue(len(__a ) == len(__a ) )
self.parent.assertTrue(len(__a ) == len(__a ) + 1 )
__UpperCAmelCase , __UpperCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__UpperCAmelCase = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
__UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
__UpperCAmelCase = model(__a )['''last_hidden_state''']
__UpperCAmelCase = model(__a , past_key_values=__a )['''last_hidden_state''']
# select random slice
__UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__UpperCAmelCase = output_from_no_past[:, -1, random_slice_idx].detach()
__UpperCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__a , __a , atol=1e-3 ) )
def snake_case__ ( self : List[Any] , __a : Union[str, Any] , __a : Dict , ) -> Optional[int]:
__UpperCAmelCase = UMTaModel(config=__a ).to(__a ).half().eval()
__UpperCAmelCase = model(**__a )['''last_hidden_state''']
self.parent.assertFalse(torch.isnan(__a ).any().item() )
@require_torch
class A ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
a_ = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
a_ = (UMTaForConditionalGeneration,) if is_torch_available() else ()
a_ = (
{
'''conversational''': UMTaForConditionalGeneration,
'''feature-extraction''': UMTaModel,
'''summarization''': UMTaForConditionalGeneration,
'''text2text-generation''': UMTaForConditionalGeneration,
'''translation''': UMTaForConditionalGeneration,
'''question-answering''': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
a_ = True
a_ = False
a_ = False
a_ = True
a_ = True
# The small UMT5 model needs higher percentages for CPU/MP tests
a_ = [0.8, 0.9]
def snake_case__ ( self : Tuple ) -> Optional[int]:
__UpperCAmelCase = UMTaModelTester(self )
@unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
def snake_case__ ( self : str ) -> Optional[int]:
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
__UpperCAmelCase = UMTaModel(config_and_inputs[0] ).to(__a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
__a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f"""{tmpdirname}/t5_test.onnx""" , export_params=__a , opset_version=9 , input_names=['''input_ids''', '''decoder_input_ids'''] , )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def snake_case__ ( self : Union[str, Any] ) -> List[str]:
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*__a )
def snake_case__ ( self : List[Any] ) -> str:
__UpperCAmelCase = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
__UpperCAmelCase = config_and_inputs[0]
__UpperCAmelCase = UMTaForConditionalGeneration(__a ).eval()
model.to(__a )
__UpperCAmelCase = {
'''head_mask''': torch.zeros(config.num_layers , config.num_heads , device=__a ),
'''decoder_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__a ),
'''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__a ),
}
for attn_name, (name, mask) in zip(__a , head_masking.items() ):
__UpperCAmelCase = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
__UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_heads , device=__a )
__UpperCAmelCase = model.generate(
config_and_inputs[1]['''input_ids'''] , num_beams=1 , max_length=3 , output_attentions=__a , return_dict_in_generate=__a , **__a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
__UpperCAmelCase = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
def snake_case__ ( self : Optional[int] ) -> int:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class A ( unittest.TestCase ):
@slow
@unittest.skip(
'''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
def snake_case__ ( self : Any ) -> int:
__UpperCAmelCase = UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' , return_dict=__a ).to(__a )
__UpperCAmelCase = AutoTokenizer.from_pretrained('''google/umt5-small''' , use_fast=__a , legacy=__a )
__UpperCAmelCase = [
'''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
'''No se como puedo <extra_id_0>.''',
'''This is the reason why we <extra_id_0> them.''',
'''The <extra_id_0> walks in <extra_id_1>, seats''',
'''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
]
__UpperCAmelCase = tokenizer(__a , return_tensors='''pt''' , padding=__a ).input_ids
# fmt: off
__UpperCAmelCase = torch.tensor(
[
[ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
] )
# fmt: on
torch.testing.assert_allclose(__a , __a )
__UpperCAmelCase = model.generate(input_ids.to(__a ) )
__UpperCAmelCase = [
'''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
'''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
]
__UpperCAmelCase = tokenizer.batch_decode(__a )
self.assertEqual(__a , __a )
| 262
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''XLNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''XLNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLNetForMultipleChoice''',
'''XLNetForQuestionAnswering''',
'''XLNetForQuestionAnsweringSimple''',
'''XLNetForSequenceClassification''',
'''XLNetForTokenClassification''',
'''XLNetLMHeadModel''',
'''XLNetModel''',
'''XLNetPreTrainedModel''',
'''load_tf_weights_in_xlnet''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLNetForMultipleChoice''',
'''TFXLNetForQuestionAnsweringSimple''',
'''TFXLNetForSequenceClassification''',
'''TFXLNetForTokenClassification''',
'''TFXLNetLMHeadModel''',
'''TFXLNetMainLayer''',
'''TFXLNetModel''',
'''TFXLNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 719
|
from __future__ import annotations
from typing import TypedDict
class __A( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
def __magic_name__ ( __a : str ):
'''simple docstring'''
if not isinstance(__a , __a ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(__a ) )]
def __magic_name__ ( __a : str ):
'''simple docstring'''
if not isinstance(__a , __a ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
UpperCamelCase__ = all_rotations(__a )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
UpperCamelCase__ = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(__a ),
}
return response
def __magic_name__ ( __a : str , __a : int ):
'''simple docstring'''
if not isinstance(__a , __a ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
UpperCamelCase__ = int(__a )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(__a ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
UpperCamelCase__ = [""""""] * len(__a )
for _ in range(len(__a ) ):
for i in range(len(__a ) ):
UpperCamelCase__ = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
lowerCamelCase_ = '''Provide a string that I will generate its BWT transform: '''
lowerCamelCase_ = input(entry_msg).strip()
lowerCamelCase_ = bwt_transform(s)
print(
f'Burrows Wheeler transform for string \'{s}\' results '
f'in \'{result["bwt_string"]}\''
)
lowerCamelCase_ = reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
print(
f'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
f'we get original string \'{original_string}\''
)
| 86
| 0
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _lowerCAmelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
a : Union[str, Any] =StableDiffusionInstructPixaPixPipeline
a : List[str] =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
a : Any =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
a : List[str] =IMAGE_TO_IMAGE_IMAGE_PARAMS
a : Union[str, Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
def _a ( self ):
torch.manual_seed(0 )
UpperCamelCase_: Tuple = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=8 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=3_2 , )
UpperCamelCase_: List[Any] = PNDMScheduler(skip_prk_steps=_lowerCamelCase )
torch.manual_seed(0 )
UpperCamelCase_: Optional[int] = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase_: Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
UpperCamelCase_: int = CLIPTextModel(_lowerCamelCase )
UpperCamelCase_: Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
UpperCamelCase_: Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def _a ( self , _lowerCamelCase , _lowerCamelCase=0 ):
UpperCamelCase_: str = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
UpperCamelCase_: Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase_: str = Image.fromarray(np.uinta(_lowerCamelCase ) ).convert('RGB' )
if str(_lowerCamelCase ).startswith('mps' ):
UpperCamelCase_: List[Any] = torch.manual_seed(_lowerCamelCase )
else:
UpperCamelCase_: Tuple = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
UpperCamelCase_: Optional[Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'image_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def _a ( self ):
UpperCamelCase_: List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCamelCase_: str = self.get_dummy_components()
UpperCamelCase_: Dict = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
UpperCamelCase_: int = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
UpperCamelCase_: int = self.get_dummy_inputs(_lowerCamelCase )
UpperCamelCase_: Union[str, Any] = sd_pipe(**_lowerCamelCase ).images
UpperCamelCase_: Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
UpperCamelCase_: Optional[int] = np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self ):
UpperCamelCase_: str = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCamelCase_: int = self.get_dummy_components()
UpperCamelCase_: Dict = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
UpperCamelCase_: List[Any] = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
UpperCamelCase_: Any = self.get_dummy_inputs(_lowerCamelCase )
UpperCamelCase_: Dict = 'french fries'
UpperCamelCase_: Optional[int] = sd_pipe(**_lowerCamelCase , negative_prompt=_lowerCamelCase )
UpperCamelCase_: List[str] = output.images
UpperCamelCase_: List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
UpperCamelCase_: Dict = np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self ):
UpperCamelCase_: int = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCamelCase_: Tuple = self.get_dummy_components()
UpperCamelCase_: Any = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
UpperCamelCase_: List[Any] = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
UpperCamelCase_: Dict = self.get_dummy_inputs(_lowerCamelCase )
UpperCamelCase_: List[Any] = [inputs['prompt']] * 2
UpperCamelCase_: Optional[Any] = np.array(inputs['image'] ).astype(np.floataa ) / 2_5_5.0
UpperCamelCase_: Any = torch.from_numpy(_lowerCamelCase ).unsqueeze(0 ).to(_lowerCamelCase )
UpperCamelCase_: Tuple = image / 2 + 0.5
UpperCamelCase_: int = image.permute(0 , 3 , 1 , 2 )
UpperCamelCase_: int = image.repeat(2 , 1 , 1 , 1 )
UpperCamelCase_: List[Any] = sd_pipe(**_lowerCamelCase ).images
UpperCamelCase_: List[str] = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
UpperCamelCase_: Optional[int] = np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self ):
UpperCamelCase_: int = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCamelCase_: Any = self.get_dummy_components()
UpperCamelCase_: str = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' )
UpperCamelCase_: Dict = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
UpperCamelCase_: str = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
UpperCamelCase_: Union[str, Any] = self.get_dummy_inputs(_lowerCamelCase )
UpperCamelCase_: Optional[int] = sd_pipe(**_lowerCamelCase ).images
UpperCamelCase_: Optional[Any] = image[0, -3:, -3:, -1]
UpperCamelCase_: Tuple = [round(_lowerCamelCase , 4 ) for x in image_slice.flatten().tolist()]
print(','.join([str(_lowerCamelCase ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
UpperCamelCase_: Tuple = np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def _a ( self ):
UpperCamelCase_: int = self.get_dummy_components()
UpperCamelCase_: Optional[Any] = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
UpperCamelCase_: str = VaeImageProcessor(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase )
UpperCamelCase_: Optional[Any] = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
UpperCamelCase_: Dict = pipe(**self.get_dummy_inputs_by_type(_lowerCamelCase , input_image_type='pt' ) )[0]
UpperCamelCase_: Union[str, Any] = components['vae']
UpperCamelCase_: Union[str, Any] = self.get_dummy_inputs_by_type(_lowerCamelCase , input_image_type='pt' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
UpperCamelCase_: str = vae.encode(inputs[image_param] ).latent_dist.mode()
UpperCamelCase_: Dict = pipe(**_lowerCamelCase )[0]
UpperCamelCase_: str = np.abs(out - out_latents_inputs ).max()
self.assertLess(_lowerCamelCase , 1e-4 , 'passing latents as image input generate different result from passing image' )
@slow
@require_torch_gpu
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
def _a ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , _lowerCamelCase=0 ):
UpperCamelCase_: int = torch.manual_seed(_lowerCamelCase )
UpperCamelCase_: int = load_image(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
UpperCamelCase_: Union[str, Any] = {
'prompt': 'turn him into a cyborg',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'image_guidance_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def _a ( self ):
UpperCamelCase_: Dict = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_lowerCamelCase )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
UpperCamelCase_: List[str] = self.get_inputs()
UpperCamelCase_: Optional[int] = pipe(**_lowerCamelCase ).images
UpperCamelCase_: Dict = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
UpperCamelCase_: Optional[Any] = np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _a ( self ):
UpperCamelCase_: List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_lowerCamelCase )
UpperCamelCase_: List[str] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
UpperCamelCase_: Union[str, Any] = self.get_inputs()
UpperCamelCase_: Optional[Any] = pipe(**_lowerCamelCase ).images
UpperCamelCase_: Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
UpperCamelCase_: Any = np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _a ( self ):
UpperCamelCase_: Optional[int] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_lowerCamelCase )
UpperCamelCase_: Any = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
UpperCamelCase_: int = self.get_inputs()
UpperCamelCase_: Any = pipe(**_lowerCamelCase ).images
UpperCamelCase_: str = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
UpperCamelCase_: str = np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _a ( self ):
UpperCamelCase_: Optional[Any] = 0
def callback_fn(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> None:
UpperCamelCase_: List[Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
UpperCamelCase_: List[str] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
UpperCamelCase_: Optional[Any] = latents[0, -3:, -3:, -1]
UpperCamelCase_: Tuple = np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
UpperCamelCase_: str = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
UpperCamelCase_: Optional[int] = latents[0, -3:, -3:, -1]
UpperCamelCase_: Any = np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
UpperCamelCase_: Tuple = False
UpperCamelCase_: Optional[int] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_lowerCamelCase , torch_dtype=torch.floataa )
UpperCamelCase_: Tuple = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
UpperCamelCase_: Optional[Any] = self.get_inputs()
pipe(**_lowerCamelCase , callback=_lowerCamelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _a ( self ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCamelCase_: List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_lowerCamelCase , torch_dtype=torch.floataa )
UpperCamelCase_: Optional[int] = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
UpperCamelCase_: Union[str, Any] = self.get_inputs()
UpperCamelCase_: Optional[Any] = pipe(**_lowerCamelCase )
UpperCamelCase_: str = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def _a ( self ):
UpperCamelCase_: Tuple = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
UpperCamelCase_: Dict = inputs['image'].resize((5_0_4, 5_0_4) )
UpperCamelCase_: int = 'timbrooks/instruct-pix2pix'
UpperCamelCase_: Optional[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
_lowerCamelCase , safety_checker=_lowerCamelCase , )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
UpperCamelCase_: Tuple = pipe(**_lowerCamelCase )
UpperCamelCase_: Optional[int] = output.images[0]
UpperCamelCase_: List[Any] = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
UpperCamelCase_: Union[str, Any] = np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 57
|
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_ , lowerCamelCase_=1_3 , lowerCamelCase_=7 , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=False , lowerCamelCase_=True , lowerCamelCase_=9_9 , lowerCamelCase_=3_2 , lowerCamelCase_=5 , lowerCamelCase_=4 , lowerCamelCase_=3_7 , lowerCamelCase_="gelu" , lowerCamelCase_=0.1 , lowerCamelCase_=0.1 , lowerCamelCase_=5_1_2 , lowerCamelCase_=1_6 , lowerCamelCase_=2 , lowerCamelCase_=0.02 , lowerCamelCase_=3 , lowerCamelCase_=4 , lowerCamelCase_=None , ) -> str:
_a : int = parent
_a : str = batch_size
_a : Any = seq_length
_a : Tuple = is_training
_a : int = use_input_mask
_a : Any = use_token_type_ids
_a : List[Any] = use_labels
_a : Optional[int] = vocab_size
_a : str = hidden_size
_a : Any = num_hidden_layers
_a : List[Any] = num_attention_heads
_a : Tuple = intermediate_size
_a : Optional[int] = hidden_act
_a : str = hidden_dropout_prob
_a : Tuple = attention_probs_dropout_prob
_a : List[Any] = max_position_embeddings
_a : Union[str, Any] = type_vocab_size
_a : Dict = type_sequence_label_size
_a : List[Any] = initializer_range
_a : Optional[Any] = num_labels
_a : Dict = num_choices
_a : Dict = scope
def __UpperCamelCase ( self ) -> Dict:
_a : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a : str = None
if self.use_input_mask:
_a : str = random_attention_mask([self.batch_size, self.seq_length] )
_a : int = None
if self.use_token_type_ids:
_a : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a : Union[str, Any] = None
_a : Any = None
_a : Optional[Any] = None
if self.use_labels:
_a : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
_a : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self ) -> List[Any]:
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase_ , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
_a : List[Any] = LlamaModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : Any = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ )
_a : List[Any] = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ) -> Any:
_a : Dict = True
_a : Tuple = LlamaModel(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : List[Any] = model(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , encoder_attention_mask=lowerCamelCase_ , )
_a : Optional[Any] = model(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , )
_a : str = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ) -> Dict:
_a : Dict = LlamaForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : int = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ) -> str:
_a : Optional[Any] = True
_a : Tuple = True
_a : Optional[int] = LlamaForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
# first forward pass
_a : str = model(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , encoder_attention_mask=lowerCamelCase_ , use_cache=lowerCamelCase_ , )
_a : Union[str, Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_a : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
_a : str = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_a : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_a : List[Any] = torch.cat([input_mask, next_mask] , dim=-1 )
_a : Any = model(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , encoder_attention_mask=lowerCamelCase_ , output_hidden_states=lowerCamelCase_ , )['hidden_states'][0]
_a : List[Any] = model(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , encoder_attention_mask=lowerCamelCase_ , past_key_values=lowerCamelCase_ , output_hidden_states=lowerCamelCase_ , )['hidden_states'][0]
# select random slice
_a : List[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a : int = output_from_no_past[:, -3:, random_slice_idx].detach()
_a : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1e-3 ) )
def __UpperCamelCase ( self ) -> Tuple:
_a : Optional[int] = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : Dict = config_and_inputs
_a : Tuple = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
__lowerCAmelCase : int = (LlamaForCausalLM,) if is_torch_available() else ()
__lowerCAmelCase : int = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCAmelCase : str = False
__lowerCAmelCase : List[Any] = False
def __UpperCamelCase ( self ) -> str:
_a : Optional[int] = LlamaModelTester(self )
_a : Tuple = ConfigTester(self , config_class=lowerCamelCase_ , hidden_size=3_7 )
def __UpperCamelCase ( self ) -> Any:
self.config_tester.run_common_tests()
def __UpperCamelCase ( self ) -> Union[str, Any]:
_a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def __UpperCamelCase ( self ) -> Optional[Any]:
_a : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a : List[str] = type
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def __UpperCamelCase ( self ) -> List[Any]:
_a , _a : int = self.model_tester.prepare_config_and_inputs_for_common()
_a : Optional[Any] = 3
_a : Union[str, Any] = input_dict['input_ids']
_a : List[Any] = input_ids.ne(1 ).to(lowerCamelCase_ )
_a : int = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a : Any = LlamaForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : List[Any] = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , labels=lowerCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __UpperCamelCase ( self ) -> List[str]:
_a , _a : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_a : List[str] = 3
_a : List[str] = 'single_label_classification'
_a : Union[str, Any] = input_dict['input_ids']
_a : str = input_ids.ne(1 ).to(lowerCamelCase_ )
_a : Any = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a : str = LlamaForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : List[Any] = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , labels=lowerCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __UpperCamelCase ( self ) -> Tuple:
_a , _a : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
_a : Union[str, Any] = 3
_a : str = 'multi_label_classification'
_a : Union[str, Any] = input_dict['input_ids']
_a : Union[str, Any] = input_ids.ne(1 ).to(lowerCamelCase_ )
_a : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a : str = LlamaForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
_a : int = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , labels=lowerCamelCase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def __UpperCamelCase ( self ) -> Optional[int]:
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def __UpperCamelCase ( self , lowerCamelCase_ ) -> List[str]:
_a , _a : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_a : Union[str, Any] = ids_tensor([1, 1_0] , config.vocab_size )
_a : Optional[Any] = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
_a : int = LlamaModel(lowerCamelCase_ )
original_model.to(lowerCamelCase_ )
original_model.eval()
_a : str = original_model(lowerCamelCase_ ).last_hidden_state
_a : Union[str, Any] = original_model(lowerCamelCase_ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
_a : Optional[Any] = {'type': scaling_type, 'factor': 10.0}
_a : Any = LlamaModel(lowerCamelCase_ )
scaled_model.to(lowerCamelCase_ )
scaled_model.eval()
_a : Optional[int] = scaled_model(lowerCamelCase_ ).last_hidden_state
_a : int = scaled_model(lowerCamelCase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1e-5 ) )
@require_torch
class a ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> int:
_a : Optional[Any] = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
_a : Optional[int] = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
_a : Tuple = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
_a : List[Any] = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , lowerCamelCase_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
_a : str = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , lowerCamelCase_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> Optional[int]:
_a : Any = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
_a : Any = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
_a : List[Any] = model(torch.tensor(lowerCamelCase_ ) )
# Expected mean on dim = -1
_a : Optional[int] = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , lowerCamelCase_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
_a : List[Any] = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , lowerCamelCase_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> Any:
_a : Optional[int] = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
_a : Union[str, Any] = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
_a : str = model(torch.tensor(lowerCamelCase_ ) )
# Expected mean on dim = -1
_a : int = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , lowerCamelCase_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
_a : List[Any] = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , lowerCamelCase_ , atol=1e-2 , rtol=1e-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def __UpperCamelCase ( self ) -> Dict:
_a : Dict = [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
_a : str = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
_a : Dict = model(torch.tensor(lowerCamelCase_ ) )
_a : str = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , lowerCamelCase_ , atol=1e-2 , rtol=1e-2 )
# fmt: off
_a : Any = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :3_0] , lowerCamelCase_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Model is curently gated' )
@slow
def __UpperCamelCase ( self ) -> Optional[Any]:
_a : List[Any] = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
_a : List[Any] = 'Simply put, the theory of relativity states that '
_a : List[Any] = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
_a : Any = tokenizer.encode(lowerCamelCase_ , return_tensors='pt' )
_a : Union[str, Any] = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=lowerCamelCase_ )
# greedy generation outputs
_a : List[Any] = model.generate(lowerCamelCase_ , max_new_tokens=6_4 , top_p=lowerCamelCase_ , temperature=1 , do_sample=lowerCamelCase_ )
_a : Dict = tokenizer.decode(generated_ids[0] , skip_special_tokens=lowerCamelCase_ )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
| 120
| 0
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
A_ = 'blip_2_vision_model'
def __init__( self , A_=14_08 , A_=61_44 , A_=39 , A_=16 , A_=2_24 , A_=14 , A_="gelu" , A_=0.00001 , A_=0.0 , A_=1E-1_0 , A_=True , **A_ , ) -> int:
"""simple docstring"""
super().__init__(**A_ )
_lowerCamelCase = hidden_size
_lowerCamelCase = intermediate_size
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = num_attention_heads
_lowerCamelCase = patch_size
_lowerCamelCase = image_size
_lowerCamelCase = initializer_range
_lowerCamelCase = attention_dropout
_lowerCamelCase = layer_norm_eps
_lowerCamelCase = hidden_act
_lowerCamelCase = qkv_bias
@classmethod
def UpperCamelCase_ ( cls , A_ , **A_ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(A_ )
_lowerCamelCase , _lowerCamelCase = cls.get_config_dict(A_ , **A_ )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('''model_type''' ) == "blip-2":
_lowerCamelCase = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(A_ , **A_ )
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
A_ = 'blip_2_qformer'
def __init__( self , A_=3_05_22 , A_=7_68 , A_=12 , A_=12 , A_=30_72 , A_="gelu" , A_=0.1 , A_=0.1 , A_=5_12 , A_=0.02 , A_=1E-1_2 , A_=0 , A_="absolute" , A_=2 , A_=14_08 , **A_ , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(pad_token_id=A_ , **A_ )
_lowerCamelCase = vocab_size
_lowerCamelCase = hidden_size
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = num_attention_heads
_lowerCamelCase = hidden_act
_lowerCamelCase = intermediate_size
_lowerCamelCase = hidden_dropout_prob
_lowerCamelCase = attention_probs_dropout_prob
_lowerCamelCase = max_position_embeddings
_lowerCamelCase = initializer_range
_lowerCamelCase = layer_norm_eps
_lowerCamelCase = position_embedding_type
_lowerCamelCase = cross_attention_frequency
_lowerCamelCase = encoder_hidden_size
@classmethod
def UpperCamelCase_ ( cls , A_ , **A_ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(A_ )
_lowerCamelCase , _lowerCamelCase = cls.get_config_dict(A_ , **A_ )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('''model_type''' ) == "blip-2":
_lowerCamelCase = config_dict['''qformer_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(A_ , **A_ )
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
A_ = 'blip-2'
A_ = True
def __init__( self , A_=None , A_=None , A_=None , A_=32 , **A_ ) -> str:
"""simple docstring"""
super().__init__(**A_ )
if vision_config is None:
_lowerCamelCase = {}
logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''' )
if qformer_config is None:
_lowerCamelCase = {}
logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''' )
if text_config is None:
_lowerCamelCase = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )
_lowerCamelCase = BlipaVisionConfig(**A_ )
_lowerCamelCase = BlipaQFormerConfig(**A_ )
_lowerCamelCase = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
_lowerCamelCase = CONFIG_MAPPING[text_model_type](**A_ )
_lowerCamelCase = self.text_config.tie_word_embeddings
_lowerCamelCase = self.text_config.is_encoder_decoder
_lowerCamelCase = num_query_tokens
_lowerCamelCase = self.vision_config.hidden_size
_lowerCamelCase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowerCamelCase = 1.0
_lowerCamelCase = 0.02
@classmethod
def UpperCamelCase_ ( cls , A_ , A_ , A_ , **A_ , ) -> Tuple:
"""simple docstring"""
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **A_ , )
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
_lowerCamelCase = copy.deepcopy(self.__dict__ )
_lowerCamelCase = self.vision_config.to_dict()
_lowerCamelCase = self.qformer_config.to_dict()
_lowerCamelCase = self.text_config.to_dict()
_lowerCamelCase = self.__class__.model_type
return output
| 638
|
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
_lowerCamelCase = dict(zip(A_ , range(len(A_ ) ) ) )
_lowerCamelCase = {
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
_lowerCamelCase = {
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 1_60_00,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase = os.path.join(self.tmpdirname , A_ )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
with open(self.feature_extraction_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
# load decoder from hub
_lowerCamelCase = '''hf-internal-testing/ngram-beam-search-decoder'''
def UpperCamelCase_ ( self , **A_ ) -> str:
"""simple docstring"""
_lowerCamelCase = self.add_kwargs_tokens_map.copy()
kwargs.update(A_ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **A_ )
def UpperCamelCase_ ( self , **A_ ) -> Optional[Any]:
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **A_ )
def UpperCamelCase_ ( self , **A_ ) -> int:
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
processor.save_pretrained(self.tmpdirname )
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , A_ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , A_ )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , A_ )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(A_ , '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=A_ , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = floats_list((3, 10_00) )
_lowerCamelCase = feature_extractor(A_ , return_tensors='''np''' )
_lowerCamelCase = processor(A_ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = '''This is a test string'''
_lowerCamelCase = processor(text=A_ )
_lowerCamelCase = tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase_ ( self , A_=(2, 10, 16) , A_=77 ) -> Optional[Any]:
"""simple docstring"""
np.random.seed(A_ )
return np.random.rand(*A_ )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits(shape=(10, 16) , seed=13 )
_lowerCamelCase = processor.decode(A_ )
_lowerCamelCase = decoder.decode_beams(A_ )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('''</s> <s> </s>''' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def UpperCamelCase_ ( self , A_ ) -> int:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
_lowerCamelCase = processor.batch_decode(A_ )
else:
with get_context(A_ ).Pool() as pool:
_lowerCamelCase = processor.batch_decode(A_ , A_ )
_lowerCamelCase = list(A_ )
with get_context('''fork''' ).Pool() as p:
_lowerCamelCase = decoder.decode_beams_batch(A_ , A_ )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(A_ , decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] , decoded_processor.text )
self.assertListEqual(A_ , decoded_processor.logit_score )
self.assertListEqual(A_ , decoded_processor.lm_score )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = 15
_lowerCamelCase = -20.0
_lowerCamelCase = -4.0
_lowerCamelCase = processor.batch_decode(
A_ , beam_width=A_ , beam_prune_logp=A_ , token_min_logp=A_ , )
_lowerCamelCase = decoded_processor_out.text
_lowerCamelCase = list(A_ )
with get_context('''fork''' ).Pool() as pool:
_lowerCamelCase = decoder.decode_beams_batch(
A_ , A_ , beam_width=A_ , beam_prune_logp=A_ , token_min_logp=A_ , )
_lowerCamelCase = [d[0][0] for d in decoded_decoder_out]
_lowerCamelCase = [d[0][2] for d in decoded_decoder_out]
_lowerCamelCase = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(A_ , A_ )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] , A_ )
self.assertTrue(np.array_equal(A_ , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , A_ , atol=1E-3 ) )
self.assertTrue(np.array_equal(A_ , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , A_ , atol=1E-3 ) )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = 2.0
_lowerCamelCase = 5.0
_lowerCamelCase = -20.0
_lowerCamelCase = True
_lowerCamelCase = processor.batch_decode(
A_ , alpha=A_ , beta=A_ , unk_score_offset=A_ , lm_score_boundary=A_ , )
_lowerCamelCase = decoded_processor_out.text
_lowerCamelCase = list(A_ )
decoder.reset_params(
alpha=A_ , beta=A_ , unk_score_offset=A_ , lm_score_boundary=A_ , )
with get_context('''fork''' ).Pool() as pool:
_lowerCamelCase = decoder.decode_beams_batch(
A_ , A_ , )
_lowerCamelCase = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(A_ , A_ )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] , A_ )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
_lowerCamelCase = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_lowerCamelCase = os.listdir(A_ )
_lowerCamelCase = ['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(A_ , A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = snapshot_download('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(A_ )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
_lowerCamelCase = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_lowerCamelCase = os.listdir(A_ )
_lowerCamelCase = os.listdir(A_ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(A_ , A_ )
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = floats_list((3, 10_00) )
_lowerCamelCase = processor_wavaveca(A_ , return_tensors='''np''' )
_lowerCamelCase = processor_auto(A_ , return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = processor_wavaveca.batch_decode(A_ )
_lowerCamelCase = processor_auto.batch_decode(A_ )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
@staticmethod
def UpperCamelCase_ ( A_ , A_ ) -> str:
"""simple docstring"""
_lowerCamelCase = [d[key] for d in offsets]
return retrieved_list
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = self._get_dummy_logits()[0]
_lowerCamelCase = processor.decode(A_ , output_word_offsets=A_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(A_ , A_ ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''end_offset''' ) , [1, 3, 5] )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = processor.batch_decode(A_ , output_word_offsets=A_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(A_ , A_ ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) for o in outputs['''word_offsets''']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''end_offset''' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
import torch
_lowerCamelCase = load_dataset('''common_voice''' , '''en''' , split='''train''' , streaming=A_ )
_lowerCamelCase = ds.cast_column('''audio''' , datasets.Audio(sampling_rate=1_60_00 ) )
_lowerCamelCase = iter(A_ )
_lowerCamelCase = next(A_ )
_lowerCamelCase = AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
_lowerCamelCase = WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
_lowerCamelCase = processor(sample['''audio''']['''array'''] , return_tensors='''pt''' ).input_values
with torch.no_grad():
_lowerCamelCase = model(A_ ).logits.cpu().numpy()
_lowerCamelCase = processor.decode(logits[0] , output_word_offsets=A_ )
_lowerCamelCase = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
_lowerCamelCase = [
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
_lowerCamelCase = '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) , A_ )
self.assertEqual(''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) , output.text )
# output times
_lowerCamelCase = torch.tensor(self.get_from_offsets(A_ , '''start_time''' ) )
_lowerCamelCase = torch.tensor(self.get_from_offsets(A_ , '''end_time''' ) )
# fmt: off
_lowerCamelCase = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
_lowerCamelCase = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(A_ , A_ , atol=0.01 ) )
self.assertTrue(torch.allclose(A_ , A_ , atol=0.01 ) )
| 638
| 1
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
SCREAMING_SNAKE_CASE_ = field(
default=__lowercase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE_ = field(
default='NER' , metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
SCREAMING_SNAKE_CASE_ = field(
default=__lowercase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE_ = field(default=__lowercase , metadata={'help': 'Set this flag to use fast tokenization.'} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
SCREAMING_SNAKE_CASE_ = field(
default=__lowercase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = field(
metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
SCREAMING_SNAKE_CASE_ = field(
default=__lowercase , metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE_ = field(
default=__lowercase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _UpperCamelCase ( ) -> int:
# 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.
lowerCamelCase_ = 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.
lowerCamelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCamelCase_ = 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.' )
lowerCamelCase_ = import_module('tasks' )
try:
lowerCamelCase_ = getattr(__A ,model_args.task_type )
lowerCamelCase_ = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' )
# 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 )
# Prepare CONLL-2003 task
lowerCamelCase_ = token_classification_task.get_labels(data_args.labels )
lowerCamelCase_ = dict(enumerate(__A ) )
lowerCamelCase_ = len(__A )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path ,num_labels=__A ,idalabel=__A ,labelaid={label: i for i, label in enumerate(__A )} ,cache_dir=model_args.cache_dir ,)
lowerCamelCase_ = 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 ,use_fast=model_args.use_fast ,)
lowerCamelCase_ = AutoModelForTokenClassification.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
lowerCamelCase_ = (
TokenClassificationDataset(
token_classification_task=__A ,data_dir=data_args.data_dir ,tokenizer=__A ,labels=__A ,model_type=config.model_type ,max_seq_length=data_args.max_seq_length ,overwrite_cache=data_args.overwrite_cache ,mode=Split.train ,)
if training_args.do_train
else None
)
lowerCamelCase_ = (
TokenClassificationDataset(
token_classification_task=__A ,data_dir=data_args.data_dir ,tokenizer=__A ,labels=__A ,model_type=config.model_type ,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 align_predictions(__UpperCamelCase ,__UpperCamelCase ) -> Tuple[List[int], List[int]]:
lowerCamelCase_ = np.argmax(__A ,axis=2 )
lowerCamelCase_ = preds.shape
lowerCamelCase_ = [[] for _ in range(__A )]
lowerCamelCase_ = [[] for _ in range(__A )]
for i in range(__A ):
for j in range(__A ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(__UpperCamelCase ) -> Dict:
lowerCamelCase_ = align_predictions(p.predictions ,p.label_ids )
return {
"accuracy_score": accuracy_score(__A ,__A ),
"precision": precision_score(__A ,__A ),
"recall": recall_score(__A ,__A ),
"f1": fa_score(__A ,__A ),
}
# Data collator
lowerCamelCase_ = DataCollatorWithPadding(__A ,pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowerCamelCase_ = 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_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowerCamelCase_ = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
lowerCamelCase_ = trainer.evaluate()
lowerCamelCase_ = os.path.join(training_args.output_dir ,'eval_results.txt' )
if trainer.is_world_process_zero():
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 )
# Predict
if training_args.do_predict:
lowerCamelCase_ = TokenClassificationDataset(
token_classification_task=__A ,data_dir=data_args.data_dir ,tokenizer=__A ,labels=__A ,model_type=config.model_type ,max_seq_length=data_args.max_seq_length ,overwrite_cache=data_args.overwrite_cache ,mode=Split.test ,)
lowerCamelCase_ = trainer.predict(__A )
lowerCamelCase_ = align_predictions(__A ,__A )
lowerCamelCase_ = os.path.join(training_args.output_dir ,'test_results.txt' )
if trainer.is_world_process_zero():
with open(__A ,'w' ) as writer:
for key, value in metrics.items():
logger.info(' %s = %s' ,__A ,__A )
writer.write('%s = %s\n' % (key, value) )
# Save predictions
lowerCamelCase_ = os.path.join(training_args.output_dir ,'test_predictions.txt' )
if trainer.is_world_process_zero():
with open(__A ,'w' ) as writer:
with open(os.path.join(data_args.data_dir ,'test.txt' ) ,'r' ) as f:
token_classification_task.write_predictions_to_file(__A ,__A ,__A )
return results
def _UpperCamelCase ( __UpperCamelCase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 42
|
import tensorflow as tf
from ...tf_utils import shape_list
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , _A , _A , _A , _A , _A=1 , _A=False , **_A) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**_A)
_UpperCAmelCase : Dict = vocab_size
_UpperCAmelCase : Any = d_embed
_UpperCAmelCase : List[Any] = d_proj
_UpperCAmelCase : List[Any] = cutoffs + [vocab_size]
_UpperCAmelCase : str = [0] + self.cutoffs
_UpperCAmelCase : Union[str, Any] = div_val
_UpperCAmelCase : Any = self.cutoffs[0]
_UpperCAmelCase : Optional[Any] = len(self.cutoffs) - 1
_UpperCAmelCase : Tuple = self.shortlist_size + self.n_clusters
_UpperCAmelCase : List[Any] = keep_order
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : List[Any] = []
def snake_case__ ( self , _A) -> List[str]:
"""simple docstring"""
if self.n_clusters > 0:
_UpperCAmelCase : Union[str, Any] = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=_A , name='''cluster_weight''')
_UpperCAmelCase : Tuple = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=_A , name='''cluster_bias''')
if self.div_val == 1:
for i in range(len(self.cutoffs)):
if self.d_proj != self.d_embed:
_UpperCAmelCase : Optional[int] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=_A , name=f'''out_projs_._{i}''' , )
self.out_projs.append(_A)
else:
self.out_projs.append(_A)
_UpperCAmelCase : str = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=_A , name=f'''out_layers_._{i}_._weight''' , )
_UpperCAmelCase : Optional[Any] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=_A , name=f'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias))
else:
for i in range(len(self.cutoffs)):
_UpperCAmelCase , _UpperCAmelCase : str = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCAmelCase : Optional[Any] = self.d_embed // (self.div_val**i)
_UpperCAmelCase : Optional[int] = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=_A , name=f'''out_projs_._{i}''')
self.out_projs.append(_A)
_UpperCAmelCase : Dict = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=_A , name=f'''out_layers_._{i}_._weight''' , )
_UpperCAmelCase : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=_A , name=f'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias))
super().build(_A)
@staticmethod
def snake_case__ ( _A , _A , _A , _A=None) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Tuple = x
if proj is not None:
_UpperCAmelCase : Optional[int] = tf.einsum('''ibd,ed->ibe''' , _A , _A)
return tf.einsum('''ibd,nd->ibn''' , _A , _A) + b
@staticmethod
def snake_case__ ( _A , _A) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = shape_list(_A)
_UpperCAmelCase : int = tf.range(lp_size[0] , dtype=target.dtype)
_UpperCAmelCase : Optional[Any] = tf.stack([r, target] , 1)
return tf.gather_nd(_A , _A)
def snake_case__ ( self , _A , _A , _A=True , _A=False) -> int:
"""simple docstring"""
_UpperCAmelCase : Tuple = 0
if self.n_clusters == 0:
_UpperCAmelCase : int = self._logit(_A , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0])
if target is not None:
_UpperCAmelCase : Any = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=_A , logits=_A)
_UpperCAmelCase : Union[str, Any] = tf.nn.log_softmax(_A , axis=-1)
else:
_UpperCAmelCase : Union[str, Any] = shape_list(_A)
_UpperCAmelCase : str = []
_UpperCAmelCase : Optional[Any] = tf.zeros(hidden_sizes[:2])
for i in range(len(self.cutoffs)):
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
_UpperCAmelCase : Any = (target >= l_idx) & (target < r_idx)
_UpperCAmelCase : str = tf.where(_A)
_UpperCAmelCase : Union[str, Any] = tf.boolean_mask(_A , _A) - l_idx
if self.div_val == 1:
_UpperCAmelCase : str = self.out_layers[0][0][l_idx:r_idx]
_UpperCAmelCase : Any = self.out_layers[0][1][l_idx:r_idx]
else:
_UpperCAmelCase : int = self.out_layers[i][0]
_UpperCAmelCase : int = self.out_layers[i][1]
if i == 0:
_UpperCAmelCase : Optional[Any] = tf.concat([cur_W, self.cluster_weight] , 0)
_UpperCAmelCase : Optional[int] = tf.concat([cur_b, self.cluster_bias] , 0)
_UpperCAmelCase : int = self._logit(_A , _A , _A , self.out_projs[0])
_UpperCAmelCase : int = tf.nn.log_softmax(_A)
out.append(head_logprob[..., : self.cutoffs[0]])
if target is not None:
_UpperCAmelCase : List[str] = tf.boolean_mask(_A , _A)
_UpperCAmelCase : Optional[Any] = self._gather_logprob(_A , _A)
else:
_UpperCAmelCase : List[str] = self._logit(_A , _A , _A , self.out_projs[i])
_UpperCAmelCase : Union[str, Any] = tf.nn.log_softmax(_A)
_UpperCAmelCase : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
_UpperCAmelCase : Optional[Any] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(_A)
if target is not None:
_UpperCAmelCase : Optional[Any] = tf.boolean_mask(_A , _A)
_UpperCAmelCase : str = tf.boolean_mask(_A , _A)
_UpperCAmelCase : Optional[Any] = self._gather_logprob(_A , _A)
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(_A , -cur_logprob , shape_list(_A))
_UpperCAmelCase : Optional[Any] = tf.concat(_A , axis=-1)
if target is not None:
if return_mean:
_UpperCAmelCase : Optional[int] = tf.reduce_mean(_A)
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(_A)
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(_A , name=self.name , aggregation='''mean''' if return_mean else '''''')
return out
| 485
| 0
|
from random import shuffle
import tensorflow as tf
from numpy import array
def _A ( lowerCamelCase , lowerCamelCase ):
a__ : int = int(lowerCamelCase )
assert noofclusters < len(lowerCamelCase )
# Find out the dimensionality
a__ : List[str] = len(vectors[0] )
# Will help select random centroids from among the available vectors
a__ : List[str] = list(range(len(lowerCamelCase ) ) )
shuffle(lowerCamelCase )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
a__ : Any = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
a__ : Optional[Any] = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
a__ : Union[str, Any] = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowerCamelCase )
]
##These nodes will assign the centroid Variables the appropriate
##values
a__ : str = tf.placeholder("float64" , [dim] )
a__ : Optional[int] = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowerCamelCase , lowerCamelCase ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
a__ : List[Any] = [tf.Variable(0 ) for i in range(len(lowerCamelCase ) )]
##These nodes will assign an assignment Variable the appropriate
##value
a__ : List[str] = tf.placeholder("int32" )
a__ : int = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowerCamelCase , lowerCamelCase ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
a__ : Tuple = tf.placeholder("float" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
a__ : List[str] = tf.reduce_mean(lowerCamelCase , 0 )
##Node for computing Euclidean distances
# Placeholders for input
a__ : Any = tf.placeholder("float" , [dim] )
a__ : List[Any] = tf.placeholder("float" , [dim] )
a__ : Dict = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCamelCase , lowerCamelCase ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
a__ : Optional[int] = tf.placeholder("float" , [noofclusters] )
a__ : int = tf.argmin(lowerCamelCase , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
a__ : Tuple = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowerCamelCase )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
a__ : Tuple = 100
for _ in range(lowerCamelCase ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowerCamelCase ) ):
a__ : Tuple = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
a__ : Tuple = [
sess.run(lowerCamelCase , feed_dict={va: vect, va: sess.run(lowerCamelCase )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
a__ : Any = sess.run(
lowerCamelCase , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowerCamelCase ):
# Collect all the vectors assigned to this cluster
a__ : List[Any] = [
vectors[i]
for i in range(len(lowerCamelCase ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
a__ : Optional[Any] = sess.run(
lowerCamelCase , feed_dict={mean_input: array(lowerCamelCase )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
a__ : Optional[int] = sess.run(lowerCamelCase )
a__ : Optional[int] = sess.run(lowerCamelCase )
return centroids, assignments
| 629
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"""configuration_distilbert""": [
"""DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""DistilBertConfig""",
"""DistilBertOnnxConfig""",
],
"""tokenization_distilbert""": ["""DistilBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""DistilBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = [
"""DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DistilBertForMaskedLM""",
"""DistilBertForMultipleChoice""",
"""DistilBertForQuestionAnswering""",
"""DistilBertForSequenceClassification""",
"""DistilBertForTokenClassification""",
"""DistilBertModel""",
"""DistilBertPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = [
"""TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDistilBertForMaskedLM""",
"""TFDistilBertForMultipleChoice""",
"""TFDistilBertForQuestionAnswering""",
"""TFDistilBertForSequenceClassification""",
"""TFDistilBertForTokenClassification""",
"""TFDistilBertMainLayer""",
"""TFDistilBertModel""",
"""TFDistilBertPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Any = [
"""FlaxDistilBertForMaskedLM""",
"""FlaxDistilBertForMultipleChoice""",
"""FlaxDistilBertForQuestionAnswering""",
"""FlaxDistilBertForSequenceClassification""",
"""FlaxDistilBertForTokenClassification""",
"""FlaxDistilBertModel""",
"""FlaxDistilBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 629
| 1
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : int ):
lowerCAmelCase = word.split()
def justify(_UpperCAmelCase : list , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
lowerCAmelCase = max_width - width
lowerCAmelCase = len(_UpperCAmelCase )
if len(_UpperCAmelCase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
lowerCAmelCase = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
lowerCAmelCase = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
lowerCAmelCase = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_UpperCAmelCase ):
num_spaces_between_words_list[i] += 1
lowerCAmelCase = []
for i in range(_UpperCAmelCase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_UpperCAmelCase )
lowerCAmelCase = []
lowerCAmelCase = []
lowerCAmelCase = 0
for word in words:
if width + len(_UpperCAmelCase ) + len(_UpperCAmelCase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_UpperCAmelCase )
width += len(_UpperCAmelCase )
else:
# justify the line and add it to result
answer.append(justify(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) )
# reset new line and new width
lowerCAmelCase ,lowerCAmelCase = [word], len(_UpperCAmelCase )
lowerCAmelCase = max_width - width - len(_UpperCAmelCase )
answer.append(' '.join(_UpperCAmelCase ) + (remaining_spaces + 1) * ' ' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 4
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ['''BartphoTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 186
| 0
|
import sys
def lowerCAmelCase_ ( A_):
UpperCamelCase__: Union[str, Any] = len(A_)
UpperCamelCase__: Tuple = [[0 for x in range(A_)] for x in range(A_)]
UpperCamelCase__: int = [[0 for x in range(A_)] for x in range(A_)]
for chain_length in range(2 ,A_):
for a in range(1 ,n - chain_length + 1):
UpperCamelCase__: int = a + chain_length - 1
UpperCamelCase__: Tuple = sys.maxsize
for c in range(A_ ,A_):
UpperCamelCase__: Tuple = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
UpperCamelCase__: List[Any] = cost
UpperCamelCase__: int = c
return matrix, sol
def lowerCAmelCase_ ( A_ ,A_ ,A_):
if i == j:
print("A" + str(A_) ,end=" ")
else:
print("(" ,end=" ")
print_optiomal_solution(A_ ,A_ ,optimal_solution[i][j])
print_optiomal_solution(A_ ,optimal_solution[i][j] + 1 ,A_)
print(")" ,end=" ")
def lowerCAmelCase_ ( ):
UpperCamelCase__: Any = [30, 35, 15, 5, 10, 20, 25]
UpperCamelCase__: Optional[Any] = len(A_)
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
UpperCamelCase__ , UpperCamelCase__: int = matrix_chain_order(A_)
print("No. of Operation required: " + str(matrix[1][n - 1]))
print_optiomal_solution(A_ ,1 ,n - 1)
if __name__ == "__main__":
main()
| 221
|
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
A__: str = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A__: int = 25_0004
A__: Optional[Any] = 25_0020
@require_sentencepiece
@require_tokenizers
class _a ( UpperCamelCase__ , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = MBartaaTokenizer
UpperCamelCase__ = MBartaaTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def UpperCAmelCase_ ( self: str ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase__: Tuple = MBartaaTokenizer(__lowerCamelCase , src_lang="en_XX" , tgt_lang="ro_RO" , keep_accents=__lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase_ ( self: Dict ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = "<s>"
UpperCamelCase__: Dict = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase ) , __lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase ) , __lowerCamelCase )
def UpperCAmelCase_ ( self: Dict ):
'''simple docstring'''
UpperCamelCase__: Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(__lowerCamelCase ) , 1054 )
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1054 )
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
UpperCamelCase__: Tuple = MBartaaTokenizer(__lowerCamelCase , src_lang="en_XX" , tgt_lang="ro_RO" , keep_accents=__lowerCamelCase )
UpperCamelCase__: Optional[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(__lowerCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
UpperCamelCase__: List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
__lowerCamelCase , [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__: Any = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase , [
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__: Tuple = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase , [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>", "."] , )
@slow
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
UpperCamelCase__: str = {"input_ids": [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCamelCase , model_name="facebook/mbart-large-50" , revision="d3913889c59cd5c9e456b269c376325eabad57e2" , )
def UpperCAmelCase_ ( self: Dict ):
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCamelCase__: Tuple = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCamelCase__: Optional[Any] = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
UpperCamelCase__: Optional[int] = self.tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
UpperCamelCase__: int = tempfile.mkdtemp()
UpperCamelCase__: List[Any] = tokenizer_r.save_pretrained(__lowerCamelCase )
UpperCamelCase__: Optional[int] = tokenizer_p.save_pretrained(__lowerCamelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
UpperCamelCase__: str = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(__lowerCamelCase , __lowerCamelCase )
# Checks everything loads correctly in the same way
UpperCamelCase__: Dict = tokenizer_r.from_pretrained(__lowerCamelCase )
UpperCamelCase__: List[str] = tokenizer_p.from_pretrained(__lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCamelCase , __lowerCamelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowerCamelCase )
# Save tokenizer rust, legacy_format=True
UpperCamelCase__: List[Any] = tempfile.mkdtemp()
UpperCamelCase__: Dict = tokenizer_r.save_pretrained(__lowerCamelCase , legacy_format=__lowerCamelCase )
UpperCamelCase__: Union[str, Any] = tokenizer_p.save_pretrained(__lowerCamelCase )
# Checks it save with the same files
self.assertSequenceEqual(__lowerCamelCase , __lowerCamelCase )
# Checks everything loads correctly in the same way
UpperCamelCase__: Optional[Any] = tokenizer_r.from_pretrained(__lowerCamelCase )
UpperCamelCase__: int = tokenizer_p.from_pretrained(__lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCamelCase , __lowerCamelCase ) )
shutil.rmtree(__lowerCamelCase )
# Save tokenizer rust, legacy_format=False
UpperCamelCase__: Any = tempfile.mkdtemp()
UpperCamelCase__: Any = tokenizer_r.save_pretrained(__lowerCamelCase , legacy_format=__lowerCamelCase )
UpperCamelCase__: Any = tokenizer_p.save_pretrained(__lowerCamelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCamelCase__: Tuple = tokenizer_r.from_pretrained(__lowerCamelCase )
UpperCamelCase__: List[str] = tokenizer_p.from_pretrained(__lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCamelCase , __lowerCamelCase ) )
shutil.rmtree(__lowerCamelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class _a ( unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = """facebook/mbart-large-50-one-to-many-mmt"""
UpperCamelCase__ = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
UpperCamelCase__ = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
""" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
""" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
UpperCamelCase__ = [EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]
@classmethod
def UpperCAmelCase_ ( cls: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: MBartaaTokenizer = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO" )
UpperCamelCase__: Union[str, Any] = 1
return cls
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 25_0001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 25_0004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 25_0020 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"] , 25_0038 )
def UpperCAmelCase_ ( self: Any ):
'''simple docstring'''
UpperCamelCase__: Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowerCamelCase )
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
self.assertIn(__lowerCamelCase , self.tokenizer.all_special_ids )
UpperCamelCase__: List[Any] = [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
UpperCamelCase__: List[Any] = self.tokenizer.decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
UpperCamelCase__: List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
self.assertNotIn(self.tokenizer.eos_token , __lowerCamelCase )
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
UpperCamelCase__: List[str] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , __lowerCamelCase )
UpperCamelCase__: List[str] = 10
UpperCamelCase__: List[Any] = self.tokenizer(__lowerCamelCase , max_length=__lowerCamelCase , truncation=__lowerCamelCase ).input_ids[0]
self.assertEqual(ids[0] , __lowerCamelCase )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(__lowerCamelCase ) , __lowerCamelCase )
def UpperCAmelCase_ ( self: List[Any] ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [25_0053, 25_0001] )
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
UpperCamelCase__: List[Any] = tempfile.mkdtemp()
UpperCamelCase__: List[Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__lowerCamelCase )
UpperCamelCase__: Tuple = MBartaaTokenizer.from_pretrained(__lowerCamelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __lowerCamelCase )
@require_torch
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
UpperCamelCase__: int = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowerCamelCase , return_tensors="pt" )
UpperCamelCase__: Optional[Any] = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
UpperCamelCase__: str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
UpperCamelCase__: List[str] = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(__lowerCamelCase , __lowerCamelCase )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
UpperCamelCase__: List[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __lowerCamelCase )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def UpperCAmelCase_ ( self: int ):
'''simple docstring'''
UpperCamelCase__: Optional[int] = self.tokenizer(self.src_text , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=3 , return_tensors="pt" )
UpperCamelCase__: List[str] = self.tokenizer(
text_target=self.tgt_text , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=10 , return_tensors="pt" )
UpperCamelCase__: Tuple = targets["input_ids"]
UpperCamelCase__: int = shift_tokens_right(__lowerCamelCase , 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 UpperCAmelCase_ ( self: List[str] ):
'''simple docstring'''
UpperCamelCase__: Dict = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR" )
self.assertEqual(
nested_simplify(__lowerCamelCase ) , {
# en_XX, A, test, EOS
"input_ids": [[25_0004, 62, 3034, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 25_0001,
} , )
| 221
| 1
|
'''simple docstring'''
def A_ ( _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : int = len(_lowerCAmelCase )
_lowerCamelCase : Dict = sum(_lowerCAmelCase )
_lowerCamelCase : List[Any] = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
_lowerCamelCase : Optional[Any] = True
for i in range(1 , s + 1 ):
_lowerCamelCase : Optional[int] = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
_lowerCamelCase : Tuple = dp[i][j - 1]
if arr[i - 1] <= j:
_lowerCamelCase : Optional[Any] = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
_lowerCamelCase : int = s - 2 * j
break
return diff
| 44
|
"""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 UpperCamelCase_ :
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=5 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=5_12 , snake_case__=16 , snake_case__=2 , snake_case__=0.02 , snake_case__=3 , snake_case__=4 , snake_case__=None , ) -> int:
"""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 UpperCamelCase_ ( self ) -> List[str]:
"""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 UpperCamelCase_ ( self ) -> Dict:
"""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=snake_case__ , initializer_range=self.initializer_range , )
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = NystromformerModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase = model(snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ )
UpperCAmelCase = model(snake_case__ , token_type_ids=snake_case__ )
UpperCAmelCase = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> int:
"""simple docstring"""
UpperCAmelCase = NystromformerForMaskedLM(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase = model(snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase = NystromformerForQuestionAnswering(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase = model(
snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , start_positions=snake_case__ , end_positions=snake_case__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = self.num_labels
UpperCAmelCase = NystromformerForSequenceClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase = model(snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> int:
"""simple docstring"""
UpperCAmelCase = self.num_labels
UpperCAmelCase = NystromformerForTokenClassification(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase = model(snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase = self.num_choices
UpperCAmelCase = NystromformerForMultipleChoice(config=snake_case__ )
model.to(snake_case__ )
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(
snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self ) -> List[str]:
"""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 UpperCamelCase_ ( a_ , a_ , unittest.TestCase ):
_A : Optional[Any] = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
_A : Optional[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 : int = False
_A : Dict = False
def UpperCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = NystromformerModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCamelCase_ ( self ) -> Dict:
"""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(*snake_case__ )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case__ )
def UpperCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case__ )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case__ )
def UpperCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case__ )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case__ )
@slow
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = NystromformerModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self ) -> List[str]:
"""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(snake_case__ )[0]
UpperCAmelCase = torch.Size((1, 6, 7_68) )
self.assertEqual(output.shape , snake_case__ )
UpperCAmelCase = torch.tensor(
[[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case__ , atol=1e-4 ) )
@slow
def UpperCamelCase_ ( self ) -> 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(snake_case__ , return_tensors="""pt""" )
with torch.no_grad():
UpperCAmelCase = model(encoding.input_ids ).logits
UpperCAmelCase = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(snake_case__ ) , """capital""" )
| 673
| 0
|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_lowercase : List[str] =['''small''', '''medium''', '''large''']
_lowercase : Optional[int] ='''lm_head.decoder.weight'''
_lowercase : Optional[Any] ='''lm_head.weight'''
def A__ ( lowercase: int, lowercase: Dict ) -> int:
A : int =torch.load(__snake_case )
A : Optional[Any] =d.pop(__snake_case )
os.makedirs(__snake_case, exist_ok=__snake_case )
torch.save(__snake_case, os.path.join(__snake_case, __snake_case ) )
if __name__ == "__main__":
_lowercase : Dict =argparse.ArgumentParser()
parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
_lowercase : Optional[Any] =parser.parse_args()
for MODEL in DIALOGPT_MODELS:
_lowercase : Tuple =os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
_lowercase : Any =f'''./DialoGPT-{MODEL}'''
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 712
|
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()
_lowercase : int =2
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self : List[Any] , *, # begin keyword-only arguments
SCREAMING_SNAKE_CASE__ : List[Any]="<s>" , SCREAMING_SNAKE_CASE__ : Optional[int]="<pad>" , SCREAMING_SNAKE_CASE__ : List[str]="</s>" , SCREAMING_SNAKE_CASE__ : Optional[Any]="<unk>" , SCREAMING_SNAKE_CASE__ : int=None , ) -> List[Any]:
A , A , A , A : Optional[Any] =bos, unk, pad, eos
A : Dict =[]
A : Union[str, Any] =[]
A : Any ={}
A : int =self.add_symbol(SCREAMING_SNAKE_CASE__ )
A : Any =self.add_symbol(SCREAMING_SNAKE_CASE__ )
A : List[Any] =self.add_symbol(SCREAMING_SNAKE_CASE__ )
A : List[str] =self.add_symbol(SCREAMING_SNAKE_CASE__ )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(SCREAMING_SNAKE_CASE__ )
A : List[str] =len(self.symbols )
def __eq__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] ) -> str:
return self.indices == other.indices
def __getitem__( self : int , SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[Any]:
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : List[Any] ) -> Union[str, Any]:
return len(self.symbols )
def __contains__( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] ) -> Tuple:
return sym in self.indices
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , SCREAMING_SNAKE_CASE__ : int ) -> Any:
A : Union[str, Any] =cls()
d.add_from_file(SCREAMING_SNAKE_CASE__ )
return d
def SCREAMING_SNAKE_CASE_ ( self : Tuple , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any=1 , SCREAMING_SNAKE_CASE__ : Optional[Any]=False ) -> Any:
if word in self.indices and not overwrite:
A : int =self.indices[word]
A : Union[str, Any] =self.count[idx] + n
return idx
else:
A : Tuple =len(self.symbols )
A : str =idx
self.symbols.append(SCREAMING_SNAKE_CASE__ )
self.count.append(SCREAMING_SNAKE_CASE__ )
return idx
def SCREAMING_SNAKE_CASE_ ( self : Any , SCREAMING_SNAKE_CASE__ : List[Any] ) -> Optional[Any]:
return 0
def SCREAMING_SNAKE_CASE_ ( self : Any , SCREAMING_SNAKE_CASE__ : List[str] ) -> Optional[Any]:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
try:
with open(SCREAMING_SNAKE_CASE__ , 'r' , encoding='utf-8' ) as fd:
self.add_from_file(SCREAMING_SNAKE_CASE__ )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(SCREAMING_SNAKE_CASE__ ) )
return
A : str =f.readlines()
A : int =self._load_meta(SCREAMING_SNAKE_CASE__ )
for line in lines[indices_start_line:]:
try:
A , A : Optional[int] =line.rstrip().rsplit(' ' , 1 )
if field == "#fairseq:overwrite":
A : int =True
A , A : Optional[Any] =line.rsplit(' ' , 1 )
else:
A : Any =False
A : Tuple =int(SCREAMING_SNAKE_CASE__ )
A : Optional[int] =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(SCREAMING_SNAKE_CASE__ ) )
self.add_symbol(SCREAMING_SNAKE_CASE__ , n=SCREAMING_SNAKE_CASE__ , overwrite=SCREAMING_SNAKE_CASE__ )
except ValueError:
raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'' )
def A__ ( lowercase: Union[str, Any] ) -> str:
# (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}
A : int =dict((re.sub(r'@@$', '', lowercase ), v) if k.endswith('@@' ) else (re.sub(r'$', '</w>', lowercase ), v) for k, v in d.items() )
A : int ='<s> <pad> </s> <unk>'.split()
# restore the special tokens
for k in keep_keys:
del da[F'{k}</w>']
A : List[Any] =d[k] # restore
return da
def A__ ( lowercase: Optional[int], lowercase: Optional[Any] ) -> str:
# prep
if not os.path.exists(lowercase ):
raise ValueError(F'path {biogpt_checkpoint_path} does not exist!' )
os.makedirs(lowercase, exist_ok=lowercase )
print(F'Writing results to {pytorch_dump_folder_path}' )
# handle various types of models
A : List[str] =os.path.join(lowercase, 'checkpoint.pt' )
if not os.path.isfile(lowercase ):
raise ValueError(F'path to the file {checkpoint_file} does not exist!' )
A : Optional[Any] =torch.load(lowercase, map_location='cpu' )
A : Any =chkpt['cfg']['model']
# dicts
A : Any =os.path.join(lowercase, 'dict.txt' )
if not os.path.isfile(lowercase ):
raise ValueError(F'path to the file {dict_file} does not exist!' )
A : Dict =Dictionary.load(lowercase )
A : Optional[Any] =rewrite_dict_keys(src_dict.indices )
A : Tuple =len(lowercase )
A : Any =os.path.join(lowercase, VOCAB_FILES_NAMES['vocab_file'] )
print(F'Generating {src_vocab_file} of {src_vocab_size} records' )
with open(lowercase, 'w', encoding='utf-8' ) as f:
f.write(json.dumps(lowercase, ensure_ascii=lowercase, indent=lowercase ) )
# merges_file (bpecodes)
A : List[str] =os.path.join(lowercase, 'bpecodes' )
if not os.path.isfile(lowercase ):
raise ValueError(F'path to the file {bpecodes_file} does not exist!' )
A : List[str] =os.path.join(lowercase, VOCAB_FILES_NAMES['merges_file'] )
shutil.copyfile(lowercase, lowercase )
# model config
A : Tuple =os.path.join(lowercase, 'config.json' )
A : Tuple ={
'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-1_2,
'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(lowercase, 'w', encoding='utf-8' ) as f:
f.write(json.dumps(lowercase, ensure_ascii=lowercase, indent=lowercase ) )
# tokenizer config
A : int =os.path.join(lowercase, lowercase )
A : List[str] ={
'bos_token': '<s>',
'eos_token': '</s>',
'model_max_length': 1_024,
'pad_token': '<pad>',
'special_tokens_map_file': None,
'tokenizer_class': 'BioGptTokenizer',
'unk_token': '<unk>',
}
print(F'Generating {biogpt_tokenizer_config_file}' )
with open(lowercase, 'w', encoding='utf-8' ) as f:
f.write(json.dumps(lowercase, ensure_ascii=lowercase, indent=lowercase ) )
# model
A : List[Any] =chkpt['model']
# remove unneeded keys
A : List[Any] =[
'decoder.version',
]
for k in ignore_keys:
model_state_dict.pop(lowercase, lowercase )
A : str =list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith('output_projection.weight' ):
A : Union[str, Any] =model_state_dict.pop(lowercase )
else:
A : List[str] =model_state_dict.pop(lowercase )
A : Any =BioGptConfig.from_pretrained(lowercase )
A : str =BioGptForCausalLM(lowercase )
# check that it loads ok
model_new.load_state_dict(lowercase )
# save
A : Tuple =os.path.join(lowercase, lowercase )
print(F'Generating {pytorch_weights_dump_path}' )
torch.save(lowercase, lowercase )
print('Conversion is done!' )
if __name__ == "__main__":
_lowercase : Union[str, Any] =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.'''
)
_lowercase : List[Any] =parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 661
| 0
|
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __A : str ) -> Union[str, Any]:
"""simple docstring"""
return x + 2
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self : Dict ) -> List[Any]:
'''simple docstring'''
lowercase : Optional[int] ='''x = 3'''
lowercase : Any ={}
lowercase : Union[str, Any] =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
assert result == 3
self.assertDictEqual(UpperCAmelCase , {'''x''': 3} )
lowercase : str ='''x = y'''
lowercase : Optional[int] ={'''y''': 5}
lowercase : List[str] =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase , {'''x''': 5, '''y''': 5} )
def A__ ( self : str ) -> Optional[int]:
'''simple docstring'''
lowercase : Optional[int] ='''y = add_two(x)'''
lowercase : str ={'''x''': 3}
lowercase : List[str] =evaluate(UpperCAmelCase , {'''add_two''': add_two} , state=UpperCAmelCase )
assert result == 5
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''y''': 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowercase : Optional[Any] =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
assert result is None
assert "tried to execute add_two" in out.out
def A__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
lowercase : int ='''x = 3'''
lowercase : Dict ={}
lowercase : List[Any] =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
assert result == 3
self.assertDictEqual(UpperCAmelCase , {'''x''': 3} )
def A__ ( self : str ) -> Tuple:
'''simple docstring'''
lowercase : Optional[Any] ='''test_dict = {\'x\': x, \'y\': add_two(x)}'''
lowercase : str ={'''x''': 3}
lowercase : Tuple =evaluate(UpperCAmelCase , {'''add_two''': add_two} , state=UpperCAmelCase )
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''y''': 5} )
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} )
def A__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
lowercase : Optional[Any] ='''x = 3\ny = 5'''
lowercase : int ={}
lowercase : List[str] =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''y''': 5} )
def A__ ( self : Any ) -> Tuple:
'''simple docstring'''
lowercase : List[str] ='''text = f\'This is x: {x}.\''''
lowercase : Union[str, Any] ={'''x''': 3}
lowercase : Tuple =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''text''': '''This is x: 3.'''} )
def A__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
lowercase : Tuple ='''if x <= 3:\n y = 2\nelse:\n y = 5'''
lowercase : Union[str, Any] ={'''x''': 3}
lowercase : Any =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''y''': 2} )
lowercase : Optional[Any] ={'''x''': 8}
lowercase : str =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase , {'''x''': 8, '''y''': 5} )
def A__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
lowercase : List[str] ='''test_list = [x, add_two(x)]'''
lowercase : Any ={'''x''': 3}
lowercase : Tuple =evaluate(UpperCAmelCase , {'''add_two''': add_two} , state=UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , [3, 5] )
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''test_list''': [3, 5]} )
def A__ ( self : Any ) -> Tuple:
'''simple docstring'''
lowercase : str ='''y = x'''
lowercase : Dict ={'''x''': 3}
lowercase : Tuple =evaluate(UpperCAmelCase , {} , state=UpperCAmelCase )
assert result == 3
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''y''': 3} )
def A__ ( self : str ) -> List[str]:
'''simple docstring'''
lowercase : Any ='''test_list = [x, add_two(x)]\ntest_list[1]'''
lowercase : Any ={'''x''': 3}
lowercase : Union[str, Any] =evaluate(UpperCAmelCase , {'''add_two''': add_two} , state=UpperCAmelCase )
assert result == 5
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''test_list''': [3, 5]} )
lowercase : int ='''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
lowercase : Union[str, Any] ={'''x''': 3}
lowercase : Tuple =evaluate(UpperCAmelCase , {'''add_two''': add_two} , state=UpperCAmelCase )
assert result == 5
self.assertDictEqual(UpperCAmelCase , {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} )
def A__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
lowercase : Optional[int] ='''x = 0\nfor i in range(3):\n x = i'''
lowercase : List[str] ={}
lowercase : Union[str, Any] =evaluate(UpperCAmelCase , {'''range''': range} , state=UpperCAmelCase )
assert result == 2
self.assertDictEqual(UpperCAmelCase , {'''x''': 2, '''i''': 2} )
| 94
|
import collections
import importlib.util
import os
import re
from pathlib import Path
_lowercase : List[Any] ='''src/transformers'''
# Matches is_xxx_available()
_lowercase : List[str] =re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
_lowercase : Any =re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_lowercase : Optional[int] =re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
_lowercase : int =re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
_lowercase : Tuple =re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_lowercase : str =re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
_lowercase : List[Any] =re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
_lowercase : List[Any] =re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
_lowercase : List[str] =re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
_lowercase : Any =re.compile(R'''^\s*try:''')
# Catches a line with else:
_lowercase : Optional[int] =re.compile(R'''^\s*else:''')
def A__ ( lowercase: int ) -> Optional[Any]:
if _re_test_backend.search(lowercase ) is None:
return None
A : List[str] =[b[0] for b in _re_backend.findall(lowercase )]
backends.sort()
return "_and_".join(lowercase )
def A__ ( lowercase: Tuple ) -> int:
with open(lowercase, 'r', encoding='utf-8', newline='\n' ) as f:
A : str =f.readlines()
A : List[str] =0
while line_index < len(lowercase ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(lowercase ):
return None
# First grab the objects without a specific backend in _import_structure
A : Union[str, Any] =[]
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
A : Union[str, Any] =lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(lowercase ):
A : List[str] =_re_one_line_import_struct.search(lowercase ).groups()[0]
A : Optional[int] =re.findall('\[([^\]]+)\]', lowercase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
A : int =_re_import_struct_key_value.search(lowercase )
if single_line_import_search is not None:
A : List[str] =[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(lowercase ) > 0]
objects.extend(lowercase )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
A : Optional[int] ={'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
A : Dict =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
A : int =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
A : str =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
A : List[Any] =lines[line_index]
if _re_import_struct_add_one.search(lowercase ) is not None:
objects.append(_re_import_struct_add_one.search(lowercase ).groups()[0] )
elif _re_import_struct_add_many.search(lowercase ) is not None:
A : List[str] =_re_import_struct_add_many.search(lowercase ).groups()[0].split(', ' )
A : Optional[Any] =[obj[1:-1] for obj in imports if len(lowercase ) > 0]
objects.extend(lowercase )
elif _re_between_brackets.search(lowercase ) is not None:
A : int =_re_between_brackets.search(lowercase ).groups()[0].split(', ' )
A : List[str] =[obj[1:-1] for obj in imports if len(lowercase ) > 0]
objects.extend(lowercase )
elif _re_quote_object.search(lowercase ) is not None:
objects.append(_re_quote_object.search(lowercase ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
A : Optional[Any] =objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
A : int =[]
while (
line_index < len(lowercase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
A : List[str] =lines[line_index]
A : Optional[int] =_re_import.search(lowercase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
A : Dict ={'none': objects}
# Let's continue with backend-specific objects
while line_index < len(lowercase ):
# If the line is an if is_backend_available, we grab all objects associated.
A : Optional[Any] =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
A : str =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
A : List[Any] =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
A : List[str] =lines[line_index]
A : Optional[Any] =_re_import.search(lowercase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
A : Any =objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def A__ ( lowercase: Dict, lowercase: str ) -> int:
def find_duplicates(lowercase: int ):
return [k for k, v in collections.Counter(lowercase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
A : Dict =[]
for key in import_dict_objects.keys():
A : Optional[Any] =find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}' )
A : str =find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
A : Tuple ='base imports' if key == 'none' else F'{key} backend'
errors.append(F'Differences for {name}:' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F' {a} in TYPE_HINT but not in _import_structure.' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F' {a} in _import_structure but not in TYPE_HINT.' )
return errors
def A__ ( ) -> int:
A : List[str] =[]
for root, _, files in os.walk(lowercase ):
if "__init__.py" in files:
A : Optional[int] =os.path.join(lowercase, '__init__.py' )
A : str =parse_init(lowercase )
if objects is not None:
A : Union[str, Any] =analyze_results(*lowercase )
if len(lowercase ) > 0:
A : Optional[int] =F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
failures.append('\n'.join(lowercase ) )
if len(lowercase ) > 0:
raise ValueError('\n\n'.join(lowercase ) )
def A__ ( ) -> Dict:
A : List[Any] =[]
for path, directories, files in os.walk(lowercase ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(lowercase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(lowercase ) / folder).glob('*.py' ) ) ) == 0:
continue
A : List[Any] =str((Path(lowercase ) / folder).relative_to(lowercase ) )
A : Union[str, Any] =short_path.replace(os.path.sep, '.' )
submodules.append(lowercase )
for fname in files:
if fname == "__init__.py":
continue
A : int =str((Path(lowercase ) / fname).relative_to(lowercase ) )
A : str =short_path.replace('.py', '' ).replace(os.path.sep, '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(lowercase )
return submodules
_lowercase : Dict =[
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
]
def A__ ( ) -> List[str]:
# This is to make sure the transformers module imported is the one in the repo.
A : Optional[Any] =importlib.util.spec_from_file_location(
'transformers', os.path.join(lowercase, '__init__.py' ), submodule_search_locations=[PATH_TO_TRANSFORMERS], )
A : List[Any] =spec.loader.load_module()
A : Union[str, Any] =[
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(lowercase ) > 0:
A : List[Any] ='\n'.join(F'- {module}' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registered in the main init of Transformers:\n'
F'{list_of_modules}\n'
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 305
| 0
|
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
snake_case = ['image_processor', 'tokenizer']
snake_case = 'AutoImageProcessor'
snake_case = 'AutoTokenizer'
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : List[Any]=None , SCREAMING_SNAKE_CASE_ : List[str]=None , **SCREAMING_SNAKE_CASE_ : List[Any] ):
lowerCamelCase__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __UpperCamelCase , )
lowerCamelCase__ = kwargs.pop("""feature_extractor""" )
lowerCamelCase__ = 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__(__UpperCamelCase , __UpperCamelCase )
lowerCamelCase__ = self.image_processor
lowerCamelCase__ = False
def __call__( self : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Optional[Any] ):
if self._in_target_context_manager:
return self.current_processor(*__UpperCamelCase , **__UpperCamelCase )
lowerCamelCase__ = kwargs.pop("""images""" , __UpperCamelCase )
lowerCamelCase__ = kwargs.pop("""text""" , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
lowerCamelCase__ = args[0]
lowerCamelCase__ = args[1:]
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
lowerCamelCase__ = self.image_processor(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
if text is not None:
lowerCamelCase__ = self.tokenizer(__UpperCamelCase , **__UpperCamelCase )
if text is None:
return inputs
elif images is None:
return encodings
else:
lowerCamelCase__ = encodings["""input_ids"""]
return inputs
def __UpperCAmelCase ( self : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Tuple ):
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def __UpperCAmelCase ( self : Tuple , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@contextmanager
def __UpperCAmelCase ( self : Tuple ):
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your images inputs, or in a separate call.""" )
lowerCamelCase__ = True
lowerCamelCase__ = self.tokenizer
yield
lowerCamelCase__ = self.image_processor
lowerCamelCase__ = False
def __UpperCAmelCase ( self : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[Any]=False , SCREAMING_SNAKE_CASE_ : Union[str, Any]=None ):
if added_vocab is None:
lowerCamelCase__ = self.tokenizer.get_added_vocab()
lowerCamelCase__ = {}
while tokens:
lowerCamelCase__ = re.search(r"""<s_(.*?)>""" , __UpperCamelCase , re.IGNORECASE )
if start_token is None:
break
lowerCamelCase__ = start_token.group(1 )
lowerCamelCase__ = re.search(rf"""</s_{key}>""" , __UpperCamelCase , re.IGNORECASE )
lowerCamelCase__ = start_token.group()
if end_token is None:
lowerCamelCase__ = tokens.replace(__UpperCamelCase , """""" )
else:
lowerCamelCase__ = end_token.group()
lowerCamelCase__ = re.escape(__UpperCamelCase )
lowerCamelCase__ = re.escape(__UpperCamelCase )
lowerCamelCase__ = re.search(f"""{start_token_escaped}(.*?){end_token_escaped}""" , __UpperCamelCase , re.IGNORECASE )
if content is not None:
lowerCamelCase__ = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
lowerCamelCase__ = self.tokenajson(__UpperCamelCase , is_inner_value=__UpperCamelCase , added_vocab=__UpperCamelCase )
if value:
if len(__UpperCamelCase ) == 1:
lowerCamelCase__ = value[0]
lowerCamelCase__ = value
else: # leaf nodes
lowerCamelCase__ = []
for leaf in content.split(r"""<sep/>""" ):
lowerCamelCase__ = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
lowerCamelCase__ = leaf[1:-2] # for categorical special tokens
output[key].append(__UpperCamelCase )
if len(output[key] ) == 1:
lowerCamelCase__ = output[key][0]
lowerCamelCase__ = tokens[tokens.find(__UpperCamelCase ) + len(__UpperCamelCase ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=__UpperCamelCase , added_vocab=__UpperCamelCase )
if len(__UpperCamelCase ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def __UpperCAmelCase ( self : Tuple ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , __UpperCamelCase , )
return self.image_processor_class
@property
def __UpperCAmelCase ( self : List[str] ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , __UpperCamelCase , )
return self.image_processor
| 719
|
"""simple docstring"""
from manim import *
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
def __UpperCAmelCase ( self : int ):
lowerCamelCase__ = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase__ = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
lowerCamelCase__ = Rectangle(height=0.2_5 , width=0.2_5 )
lowerCamelCase__ = [mem.copy() for i in range(6 )]
lowerCamelCase__ = [mem.copy() for i in range(6 )]
lowerCamelCase__ = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
lowerCamelCase__ = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
lowerCamelCase__ = VGroup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
lowerCamelCase__ = Text("""CPU""" , font_size=24 )
lowerCamelCase__ = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = [mem.copy() for i in range(4 )]
lowerCamelCase__ = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
lowerCamelCase__ = Text("""GPU""" , font_size=24 )
lowerCamelCase__ = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
gpu.move_to([-1, -1, 0] )
self.add(SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = [mem.copy() for i in range(6 )]
lowerCamelCase__ = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
lowerCamelCase__ = Text("""Model""" , font_size=24 )
lowerCamelCase__ = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
model.move_to([3, -1.0, 0] )
self.add(SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = []
lowerCamelCase__ = []
for i, rect in enumerate(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase__ = fill.copy().set_fill(SCREAMING_SNAKE_CASE_ , opacity=0.8 )
target.move_to(SCREAMING_SNAKE_CASE_ )
model_arr.append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0.0 ).set_fill(SCREAMING_SNAKE_CASE_ , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(SCREAMING_SNAKE_CASE_ )
self.add(*SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = [meta_mem.copy() for i in range(6 )]
lowerCamelCase__ = [meta_mem.copy() for i in range(6 )]
lowerCamelCase__ = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
lowerCamelCase__ = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
lowerCamelCase__ = VGroup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
lowerCamelCase__ = Text("""Disk""" , font_size=24 )
lowerCamelCase__ = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
disk.move_to([-4, -1.2_5, 0] )
self.add(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCamelCase__ = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , )
blue_text.next_to(SCREAMING_SNAKE_CASE_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = MarkupText(
f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase__ = Square(0.3 )
input.set_fill(SCREAMING_SNAKE_CASE_ , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , SCREAMING_SNAKE_CASE_ , buff=0.5 )
self.play(Write(SCREAMING_SNAKE_CASE_ ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=SCREAMING_SNAKE_CASE_ , buff=0.0_2 )
self.play(MoveToTarget(SCREAMING_SNAKE_CASE_ ) )
self.play(FadeOut(SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase__ = Arrow(start=SCREAMING_SNAKE_CASE_ , end=SCREAMING_SNAKE_CASE_ , color=SCREAMING_SNAKE_CASE_ , buff=0.5 )
a.next_to(model_arr[0].get_left() , SCREAMING_SNAKE_CASE_ , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
lowerCamelCase__ = MarkupText(
f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(SCREAMING_SNAKE_CASE_ , run_time=3 ) )
lowerCamelCase__ = {"""run_time""": 1, """fade_in""": True, """fade_out""": True, """buff""": 0.0_2}
self.play(
Write(SCREAMING_SNAKE_CASE_ ) , Circumscribe(model_arr[0] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , Circumscribe(model_cpu_arr[0] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , Circumscribe(gpu_rect[0] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
lowerCamelCase__ = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.0_2 , SCREAMING_SNAKE_CASE_ , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.0_2 )
lowerCamelCase__ = AnimationGroup(
FadeOut(SCREAMING_SNAKE_CASE_ , run_time=0.5 ) , MoveToTarget(SCREAMING_SNAKE_CASE_ , run_time=0.5 ) , FadeIn(SCREAMING_SNAKE_CASE_ , run_time=0.5 ) , lag_ratio=0.2 )
self.play(SCREAMING_SNAKE_CASE_ )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
lowerCamelCase__ = 0.7
self.play(
Circumscribe(model_arr[i] , **SCREAMING_SNAKE_CASE_ ) , Circumscribe(cpu_left_col_base[i] , **SCREAMING_SNAKE_CASE_ ) , Circumscribe(cpu_left_col_base[i + 1] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , Circumscribe(gpu_rect[0] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , Circumscribe(model_arr[i + 1] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.0_2 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , Circumscribe(cpu_left_col_base[-1] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , Circumscribe(gpu_rect[0] , color=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
lowerCamelCase__ = a_c
lowerCamelCase__ = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.0_2 , buff=0.5 )
self.play(
FadeOut(SCREAMING_SNAKE_CASE_ ) , FadeOut(SCREAMING_SNAKE_CASE_ , run_time=0.5 ) , )
lowerCamelCase__ = MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed.""" , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(SCREAMING_SNAKE_CASE_ , run_time=3 ) , MoveToTarget(SCREAMING_SNAKE_CASE_ ) )
self.wait()
| 258
| 0
|
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A : Any = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : int , *__UpperCamelCase : str , __UpperCamelCase : Optional[Any]=None , __UpperCamelCase : Dict=None , __UpperCamelCase : Any=None , **__UpperCamelCase : Union[str, Any] )->str:
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
_UpperCAmelCase = eval_examples
_UpperCAmelCase = post_process_function
_UpperCAmelCase = quant_trainer_args
_UpperCAmelCase = 1_2_8 # default number of calibration samples
def lowercase__ ( self : int , __UpperCamelCase : Optional[int]=None )->Tuple:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
_UpperCAmelCase = calib_dataset if calib_dataset is not None else self.calib_dataset
_UpperCAmelCase = self._remove_unused_columns(__UpperCamelCase , description='''Calibration''' )
return DataLoader(
__UpperCamelCase , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=__UpperCamelCase , )
def lowercase__ ( self : str , __UpperCamelCase : Any=None )->List[str]:
_UpperCAmelCase = self.train_dataset if calib_dataset is None else calib_dataset
_UpperCAmelCase = self.get_calib_dataloader(__UpperCamelCase )
_UpperCAmelCase = self.model
quant_trainer.configure_model(__UpperCamelCase , self.quant_trainer_args , calib=__UpperCamelCase )
model.eval()
quant_trainer.enable_calibration(__UpperCamelCase )
logger.info('''***** Running calibration *****''' )
logger.info(F' Num examples = {self.calib_num}' )
logger.info(F' Batch size = {calib_dataloader.batch_size}' )
for step, inputs in enumerate(__UpperCamelCase ):
# Prediction step
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.prediction_step(__UpperCamelCase , __UpperCamelCase , prediction_loss_only=__UpperCamelCase )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(__UpperCamelCase , self.quant_trainer_args )
_UpperCAmelCase = model
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : List[str]=None , __UpperCamelCase : List[Any]=None , __UpperCamelCase : Any=None , __UpperCamelCase : str = "eval" )->Tuple:
_UpperCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
_UpperCAmelCase = self.get_eval_dataloader(__UpperCamelCase )
_UpperCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_UpperCAmelCase = self.compute_metrics
_UpperCAmelCase = None
_UpperCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_UpperCAmelCase = eval_loop(
__UpperCamelCase , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__UpperCamelCase , )
finally:
_UpperCAmelCase = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_UpperCAmelCase = self.post_process_function(__UpperCamelCase , __UpperCamelCase , output.predictions )
_UpperCAmelCase = self.compute_metrics(__UpperCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'{metric_key_prefix}_' ):
_UpperCAmelCase = metrics.pop(__UpperCamelCase )
self.log(__UpperCamelCase )
else:
_UpperCAmelCase = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_UpperCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , __UpperCamelCase )
return metrics
def lowercase__ ( self : int , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any]=None , __UpperCamelCase : str = "test" )->List[str]:
_UpperCAmelCase = self.get_test_dataloader(__UpperCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
_UpperCAmelCase = self.compute_metrics
_UpperCAmelCase = None
_UpperCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_UpperCAmelCase = eval_loop(
__UpperCamelCase , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__UpperCamelCase , )
finally:
_UpperCAmelCase = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_UpperCAmelCase = self.post_process_function(__UpperCamelCase , __UpperCamelCase , output.predictions , '''predict''' )
_UpperCAmelCase = self.compute_metrics(__UpperCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'{metric_key_prefix}_' ):
_UpperCAmelCase = metrics.pop(__UpperCamelCase )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__UpperCamelCase )
def lowercase__ ( self : Dict , __UpperCamelCase : Optional[int]="./" )->Union[str, Any]:
_UpperCAmelCase = self.eval_dataset
_UpperCAmelCase = self.get_eval_dataloader(__UpperCamelCase )
_UpperCAmelCase = next(iter(__UpperCamelCase ) )
# saving device - to make it consistent
_UpperCAmelCase = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
_UpperCAmelCase = tuple(v.to(__UpperCamelCase ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
_UpperCAmelCase = True
_UpperCAmelCase = self.model.to(__UpperCamelCase )
model.eval()
model.float()
_UpperCAmelCase = model.module if hasattr(__UpperCamelCase , '''module''' ) else model
quant_trainer.configure_model(__UpperCamelCase , self.quant_trainer_args )
_UpperCAmelCase = os.path.join(__UpperCamelCase , '''model.onnx''' )
logger.info(F'exporting model to {output_model_file}' )
_UpperCAmelCase = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , export_params=__UpperCamelCase , opset_version=1_3 , do_constant_folding=__UpperCamelCase , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=__UpperCamelCase , )
logger.info('''onnx export finished''' )
| 602
|
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__A : List[str] = logging.get_logger(__name__)
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : str=False ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a PyTorch model in Flax, 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
if not is_sharded:
_UpperCAmelCase = os.path.abspath(_SCREAMING_SNAKE_CASE )
logger.info(f'Loading PyTorch weights from {pt_path}' )
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )
logger.info(f'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.' )
_UpperCAmelCase = convert_pytorch_state_dict_to_flax(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
_UpperCAmelCase = convert_pytorch_sharded_state_dict_to_flax(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return flax_state_dict
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple[str] , _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : Dict[str, jnp.ndarray] , _SCREAMING_SNAKE_CASE : str , ):
'''simple docstring'''
def is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE : Tuple[str] ) -> bool:
return len(set(_SCREAMING_SNAKE_CASE ) & {key, (model_prefix,) + key} ) > 0
# layer norm
_UpperCAmelCase = pt_tuple_key[:-1] + ('''scale''',)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
_UpperCAmelCase = pt_tuple_key[:-1] + ('''mean''',)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
_UpperCAmelCase = pt_tuple_key[:-1] + ('''var''',)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE ):
return renamed_pt_tuple_key, pt_tensor
# embedding
_UpperCAmelCase = pt_tuple_key[:-1] + ('''embedding''',)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
_UpperCAmelCase = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_UpperCAmelCase = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_UpperCAmelCase = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_UpperCAmelCase = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
_UpperCAmelCase = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
_UpperCAmelCase = pt_tuple_key[-2] + '''_g'''
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
_UpperCAmelCase = pt_tuple_key[-2] + '''_v'''
if name is not None:
_UpperCAmelCase = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
_UpperCAmelCase = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
_UpperCAmelCase = flax_model.params['''params''']
else:
_UpperCAmelCase = flax_model.params
_UpperCAmelCase = flatten_dict(_SCREAMING_SNAKE_CASE )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_UpperCAmelCase = flatten_dict(flax_model.params['''batch_stats'''] )
random_flax_state_dict.update(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = {}
_UpperCAmelCase = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
_UpperCAmelCase = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_UpperCAmelCase = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
_UpperCAmelCase = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_UpperCAmelCase = pt_tuple_key[1:]
# Correctly rename weight parameters
_UpperCAmelCase , _UpperCAmelCase = rename_key_and_reshape_tensor(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# add model prefix if necessary
_UpperCAmelCase = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_UpperCAmelCase = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
_UpperCAmelCase = jnp.asarray(_SCREAMING_SNAKE_CASE )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
# also add unexpected weight so that warning is thrown
_UpperCAmelCase = jnp.asarray(_SCREAMING_SNAKE_CASE )
else:
# also add unexpected weight so that warning is thrown
_UpperCAmelCase = jnp.asarray(_SCREAMING_SNAKE_CASE )
return unflatten_dict(_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
import torch
# Load the index
_UpperCAmelCase = {}
for shard_file in shard_filenames:
# load using msgpack utils
_UpperCAmelCase = torch.load(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
_UpperCAmelCase = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_UpperCAmelCase = flax_model.params['''params''']
_UpperCAmelCase = flatten_dict(_SCREAMING_SNAKE_CASE )
random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats'''] ) )
else:
_UpperCAmelCase = flax_model.params
_UpperCAmelCase = flatten_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
_UpperCAmelCase = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_UpperCAmelCase = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
_UpperCAmelCase = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_UpperCAmelCase = pt_tuple_key[1:]
# Correctly rename weight parameters
_UpperCAmelCase , _UpperCAmelCase = rename_key_and_reshape_tensor(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# add model prefix if necessary
_UpperCAmelCase = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_UpperCAmelCase = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
_UpperCAmelCase = jnp.asarray(_SCREAMING_SNAKE_CASE )
continue
if "var" in flax_key[-1]:
_UpperCAmelCase = jnp.asarray(_SCREAMING_SNAKE_CASE )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
# also add unexpected weight so that warning is thrown
_UpperCAmelCase = jnp.asarray(_SCREAMING_SNAKE_CASE )
else:
# also add unexpected weight so that warning is thrown
_UpperCAmelCase = jnp.asarray(_SCREAMING_SNAKE_CASE )
return unflatten_dict(_SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = os.path.abspath(_SCREAMING_SNAKE_CASE )
logger.info(f'Loading Flax weights from {flax_checkpoint_path}' )
# import correct flax class
_UpperCAmelCase = getattr(_SCREAMING_SNAKE_CASE , '''Flax''' + model.__class__.__name__ )
# load flax weight dict
with open(_SCREAMING_SNAKE_CASE , '''rb''' ) as state_f:
try:
_UpperCAmelCase = from_bytes(_SCREAMING_SNAKE_CASE , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f'Unable to convert {flax_checkpoint_path} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a 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 _SCREAMING_SNAKE_CASE : x.dtype == jnp.bfloataa , _SCREAMING_SNAKE_CASE ) ).values()
if any(_SCREAMING_SNAKE_CASE ):
# convert all weights to fp32 if the 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 _SCREAMING_SNAKE_CASE : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = flatten_dict(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = pt_model.state_dict()
_UpperCAmelCase = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
_UpperCAmelCase = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
# 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[0] == pt_model.base_model_prefix
_UpperCAmelCase = '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
_UpperCAmelCase = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
_UpperCAmelCase = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_SCREAMING_SNAKE_CASE ) not in pt_model_dict:
# conv layer
_UpperCAmelCase = flax_key_tuple[:-1] + ('''weight''',)
_UpperCAmelCase = jnp.transpose(_SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(_SCREAMING_SNAKE_CASE ) not in pt_model_dict:
# linear layer
_UpperCAmelCase = flax_key_tuple[:-1] + ('''weight''',)
_UpperCAmelCase = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_UpperCAmelCase = flax_key_tuple[:-1] + ('''weight''',)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
_UpperCAmelCase = flax_key_tuple[:-1] + ('''running_mean''',)
elif "var" in flax_key_tuple[-1]:
_UpperCAmelCase = flax_key_tuple[:-1] + ('''running_var''',)
if "batch_stats" in flax_state:
_UpperCAmelCase = '''.'''.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
_UpperCAmelCase = '''.'''.join(_SCREAMING_SNAKE_CASE )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
_UpperCAmelCase = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
_UpperCAmelCase = key.split('''.''' )
_UpperCAmelCase = None
if key_components[-3::2] == ["parametrizations", "original0"]:
_UpperCAmelCase = key_components[-2] + '''_g'''
elif key_components[-3::2] == ["parametrizations", "original1"]:
_UpperCAmelCase = key_components[-2] + '''_v'''
if name is not None:
_UpperCAmelCase = key_components[:-3] + [name]
_UpperCAmelCase = '''.'''.join(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = key
if flax_key in special_pt_names:
_UpperCAmelCase = special_pt_names[flax_key]
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(_SCREAMING_SNAKE_CASE ) if not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) else flax_tensor
_UpperCAmelCase = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# remove from missing keys
missing_keys.remove(_SCREAMING_SNAKE_CASE )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(_SCREAMING_SNAKE_CASE )
pt_model.load_state_dict(_SCREAMING_SNAKE_CASE )
# re-transform missing_keys to list
_UpperCAmelCase = list(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 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).''' )
else:
logger.warning(f'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n' )
if len(_SCREAMING_SNAKE_CASE ) > 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.''' )
else:
logger.warning(
f'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
'''If your task is similar to the task the model of the checkpoint was trained on, '''
f'you can already use {pt_model.__class__.__name__} for predictions without further training.' )
return pt_model
| 602
| 1
|
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 720
|
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __a ( __A ):
'''simple docstring'''
def __init__( self , *UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ):
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
SCREAMING_SNAKE_CASE_ : Dict = eval_examples
SCREAMING_SNAKE_CASE_ : int = post_process_function
def __snake_case ( self , UpperCamelCase__ = None , UpperCamelCase__=None , UpperCamelCase__ = None , UpperCamelCase__ = "eval" , **UpperCamelCase__ , ):
SCREAMING_SNAKE_CASE_ : int = gen_kwargs.copy()
SCREAMING_SNAKE_CASE_ : Tuple = (
gen_kwargs['max_length'] if gen_kwargs.get('max_length' ) is not None else self.args.generation_max_length
)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
gen_kwargs['num_beams'] if gen_kwargs.get('num_beams' ) is not None else self.args.generation_num_beams
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = gen_kwargs
SCREAMING_SNAKE_CASE_ : Tuple = self.eval_dataset if eval_dataset is None else eval_dataset
SCREAMING_SNAKE_CASE_ : int = self.get_eval_dataloader(UpperCamelCase__ )
SCREAMING_SNAKE_CASE_ : Any = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.compute_metrics
SCREAMING_SNAKE_CASE_ : Optional[Any] = None
SCREAMING_SNAKE_CASE_ : Union[str, Any] = time.time()
SCREAMING_SNAKE_CASE_ : int = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE_ : List[Any] = eval_loop(
UpperCamelCase__ , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase__ , metric_key_prefix=UpperCamelCase__ , )
finally:
SCREAMING_SNAKE_CASE_ : Optional[int] = compute_metrics
SCREAMING_SNAKE_CASE_ : Dict = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
UpperCamelCase__ , UpperCamelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
SCREAMING_SNAKE_CASE_ : List[str] = self.post_process_function(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.compute_metrics(UpperCamelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = metrics.pop(UpperCamelCase__ )
metrics.update(output.metrics )
else:
SCREAMING_SNAKE_CASE_ : Tuple = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(UpperCamelCase__ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
SCREAMING_SNAKE_CASE_ : Dict = self.callback_handler.on_evaluate(self.args , self.state , self.control , UpperCamelCase__ )
return metrics
def __snake_case ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__ = "test" , **UpperCamelCase__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = gen_kwargs.copy()
SCREAMING_SNAKE_CASE_ : Any = self.get_test_dataloader(UpperCamelCase__ )
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE_ : Tuple = self.compute_metrics
SCREAMING_SNAKE_CASE_ : str = None
SCREAMING_SNAKE_CASE_ : Optional[Any] = time.time()
SCREAMING_SNAKE_CASE_ : List[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE_ : List[str] = eval_loop(
UpperCamelCase__ , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase__ , metric_key_prefix=UpperCamelCase__ , )
finally:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = compute_metrics
SCREAMING_SNAKE_CASE_ : List[str] = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
UpperCamelCase__ , UpperCamelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
SCREAMING_SNAKE_CASE_ : Optional[int] = self.post_process_function(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , 'predict' )
SCREAMING_SNAKE_CASE_ : List[Any] = self.compute_metrics(UpperCamelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
SCREAMING_SNAKE_CASE_ : Optional[int] = metrics.pop(UpperCamelCase__ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=UpperCamelCase__ )
| 97
| 0
|
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