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'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """filename with blanks.csv"""] )
@pytest.mark.parametrize("""revision""" , [None, """v2"""] )
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
lowerCamelCase__ : Dict = hf_hub_url(repo_id=UpperCamelCase , path=UpperCamelCase , revision=UpperCamelCase )
assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(UpperCamelCase )}'''
| 41 |
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 : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """codegen"""
UpperCamelCase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any, __A : Optional[int]=5_0_4_0_0, __A : Tuple=2_0_4_8, __A : Optional[int]=2_0_4_8, __A : List[str]=4_0_9_6, __A : List[str]=2_8, __A : Union[str, Any]=1_6, __A : Tuple=6_4, __A : Union[str, Any]=None, __A : Union[str, Any]="gelu_new", __A : Any=0.0, __A : Dict=0.0, __A : str=0.0, __A : Optional[int]=1E-5, __A : Any=0.0_2, __A : Any=True, __A : Union[str, Any]=5_0_2_5_6, __A : List[str]=5_0_2_5_6, __A : int=False, **__A : List[Any], ):
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Tuple = n_ctx
UpperCAmelCase : Tuple = n_positions
UpperCAmelCase : Optional[int] = n_embd
UpperCAmelCase : Union[str, Any] = n_layer
UpperCAmelCase : List[str] = n_head
UpperCAmelCase : Tuple = n_inner
UpperCAmelCase : int = rotary_dim
UpperCAmelCase : List[Any] = activation_function
UpperCAmelCase : List[str] = resid_pdrop
UpperCAmelCase : Optional[Any] = embd_pdrop
UpperCAmelCase : str = attn_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : Any = bos_token_id
UpperCAmelCase : List[str] = eos_token_id
super().__init__(
bos_token_id=__A, eos_token_id=__A, tie_word_embeddings=__A, **__A )
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Any, __A : PretrainedConfig, __A : str = "default", __A : List[PatchingSpec] = None, __A : bool = False, ):
super().__init__(__A, task=__A, patching_specs=__A, use_past=__A )
if not getattr(self._config, '''pad_token_id''', __A ):
# TODO: how to do that better?
UpperCAmelCase : Union[str, Any] = 0
@property
def __magic_name__ ( self : str ):
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__A, direction='''inputs''' )
UpperCAmelCase : int = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase : List[Any] = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __magic_name__ ( self : Dict ):
return self._config.n_layer
@property
def __magic_name__ ( self : List[str] ):
return self._config.n_head
def __magic_name__ ( self : str, __A : PreTrainedTokenizer, __A : int = -1, __A : int = -1, __A : bool = False, __A : Optional[TensorType] = None, ):
UpperCAmelCase : Union[str, Any] = super(__A, self ).generate_dummy_inputs(
__A, batch_size=__A, seq_length=__A, is_pair=__A, framework=__A )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase : Union[str, 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
UpperCAmelCase , UpperCAmelCase : str = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase : str = seqlen + 2
UpperCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase : Optional[int] = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
UpperCAmelCase : Union[str, Any] = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase : Optional[Any] = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase : Dict = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__A, __A, dtype=__A )], dim=1 )
return ordered_inputs
@property
def __magic_name__ ( self : Tuple ):
return 1_3
| 336 | 0 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = """xlnet"""
__lowercase = ["""mems"""]
__lowercase = {
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowerCAmelCase_=3_20_00 , lowerCAmelCase_=10_24 , lowerCAmelCase_=24 , lowerCAmelCase_=16 , lowerCAmelCase_=40_96 , lowerCAmelCase_="gelu" , lowerCAmelCase_=True , lowerCAmelCase_="bi" , lowerCAmelCase_=0.02 , lowerCAmelCase_=1E-12 , lowerCAmelCase_=0.1 , lowerCAmelCase_=5_12 , lowerCAmelCase_=None , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=-1 , lowerCAmelCase_=False , lowerCAmelCase_="last" , lowerCAmelCase_=True , lowerCAmelCase_="tanh" , lowerCAmelCase_=0.1 , lowerCAmelCase_=5 , lowerCAmelCase_=5 , lowerCAmelCase_=5 , lowerCAmelCase_=1 , lowerCAmelCase_=2 , **lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = vocab_size
_snake_case = d_model
_snake_case = n_layer
_snake_case = n_head
if d_model % n_head != 0:
raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})' )
_snake_case = d_model // n_head
_snake_case = ff_activation
_snake_case = d_inner
_snake_case = untie_r
_snake_case = attn_type
_snake_case = initializer_range
_snake_case = layer_norm_eps
_snake_case = dropout
_snake_case = mem_len
_snake_case = reuse_len
_snake_case = bi_data
_snake_case = clamp_len
_snake_case = same_length
_snake_case = summary_type
_snake_case = summary_use_proj
_snake_case = summary_activation
_snake_case = summary_last_dropout
_snake_case = start_n_top
_snake_case = end_n_top
_snake_case = bos_token_id
_snake_case = pad_token_id
_snake_case = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
' instead.' , lowerCAmelCase_ , )
_snake_case = kwargs['use_cache']
_snake_case = use_mems_eval
_snake_case = use_mems_train
super().__init__(pad_token_id=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def lowerCamelCase ( self ):
"""simple docstring"""
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 , lowerCAmelCase_ ):
"""simple docstring"""
raise NotImplementedError(
F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 42 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 336 | 0 |
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 lowerCamelCase_ :
'''simple docstring'''
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=0.6 , __lowercase=None , ) -> Tuple:
__UpperCamelCase :List[str] = parent
__UpperCamelCase :List[Any] = batch_size
__UpperCamelCase :str = image_size
__UpperCamelCase :List[Any] = patch_size
__UpperCamelCase :List[str] = num_channels
__UpperCamelCase :Union[str, Any] = is_training
__UpperCamelCase :List[str] = use_labels
__UpperCamelCase :Tuple = hidden_size
__UpperCamelCase :str = num_hidden_layers
__UpperCamelCase :List[Any] = num_attention_heads
__UpperCamelCase :Optional[Any] = intermediate_size
__UpperCamelCase :List[str] = hidden_act
__UpperCamelCase :str = hidden_dropout_prob
__UpperCamelCase :List[str] = attention_probs_dropout_prob
__UpperCamelCase :Union[str, Any] = type_sequence_label_size
__UpperCamelCase :List[str] = initializer_range
__UpperCamelCase :Optional[int] = mask_ratio
__UpperCamelCase :Optional[int] = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
__UpperCamelCase :Optional[Any] = (image_size // patch_size) ** 2
__UpperCamelCase :Any = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCamelCase :Tuple = None
if self.use_labels:
__UpperCamelCase :str = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__UpperCamelCase :List[Any] = self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ ( self) -> Tuple:
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=__lowercase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> Any:
__UpperCamelCase :Any = TFViTMAEModel(config=__lowercase)
__UpperCamelCase :Union[str, Any] = model(__lowercase , training=__lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> List[str]:
__UpperCamelCase :str = TFViTMAEForPreTraining(__lowercase)
__UpperCamelCase :str = model(__lowercase , training=__lowercase)
# expected sequence length = num_patches
__UpperCamelCase :List[str] = (self.image_size // self.patch_size) ** 2
__UpperCamelCase :Union[str, Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))
# test greyscale images
__UpperCamelCase :List[str] = 1
__UpperCamelCase :List[str] = TFViTMAEForPreTraining(__lowercase)
__UpperCamelCase :int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
__UpperCamelCase :Dict = model(__lowercase , training=__lowercase)
__UpperCamelCase :List[str] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :Optional[int] = self.prepare_config_and_inputs()
((__UpperCamelCase) , (__UpperCamelCase) , (__UpperCamelCase)) :List[str] = config_and_inputs
__UpperCamelCase :Dict = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : str = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
a__ : Dict = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
a__ : Tuple = False
a__ : str = False
a__ : Optional[Any] = False
a__ : Union[str, Any] = False
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :List[str] = TFViTMAEModelTester(self)
__UpperCamelCase :List[str] = ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37)
def UpperCamelCase__ ( self) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''')
def UpperCamelCase__ ( self) -> str:
pass
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase , __UpperCamelCase :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCamelCase :List[Any] = model_class(__lowercase)
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer))
__UpperCamelCase :Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowercase , tf.keras.layers.Layer))
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase , __UpperCamelCase :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCamelCase :Tuple = model_class(__lowercase)
__UpperCamelCase :int = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCamelCase :Optional[int] = [*signature.parameters.keys()]
__UpperCamelCase :Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase)
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase)
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__lowercase)
def UpperCamelCase__ ( self) -> Optional[Any]:
# make the mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :Tuple = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
__UpperCamelCase :str = model_class(__lowercase)
__UpperCamelCase :Optional[int] = self._prepare_for_class(__lowercase , __lowercase)
__UpperCamelCase :Dict = model(__lowercase , noise=__lowercase)
__UpperCamelCase :int = copy.deepcopy(self._prepare_for_class(__lowercase , __lowercase))
__UpperCamelCase :Union[str, Any] = model(**__lowercase , noise=__lowercase)
__UpperCamelCase :Tuple = outputs_dict[0].numpy()
__UpperCamelCase :Union[str, Any] = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords)) , 1E-6)
def UpperCamelCase__ ( self) -> Optional[int]:
# make the mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :str = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :int = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :str = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
def prepare_numpy_arrays(__lowercase):
__UpperCamelCase :Optional[int] = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__lowercase):
__UpperCamelCase :Optional[Any] = v.numpy()
else:
__UpperCamelCase :Optional[int] = np.array(__lowercase)
return inputs_np_dict
for model_class in self.all_model_classes:
__UpperCamelCase :int = model_class(__lowercase)
__UpperCamelCase :Tuple = self._prepare_for_class(__lowercase , __lowercase)
__UpperCamelCase :Any = prepare_numpy_arrays(__lowercase)
__UpperCamelCase :Any = model(__lowercase , noise=__lowercase)
__UpperCamelCase :Tuple = model(**__lowercase , noise=__lowercase)
self.assert_outputs_same(__lowercase , __lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> List[Any]:
# make masks reproducible
np.random.seed(2)
__UpperCamelCase :Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2)
__UpperCamelCase :Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
__UpperCamelCase :Dict = tf.constant(__lowercase)
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
__UpperCamelCase :Any = tf_noise
super().check_pt_tf_models(__lowercase , __lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Tuple:
# make mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :Dict = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :Optional[int] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__),)
for module_member_name in dir(__lowercase)
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(__lowercase , __lowercase),)
if isinstance(__lowercase , __lowercase)
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__lowercase , '''_keras_serializable''' , __lowercase)
}
__UpperCamelCase :Union[str, Any] = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :List[str] = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
__UpperCamelCase :str = tf.convert_to_tensor(__lowercase)
inputs_dict.update({'''noise''': noise})
for main_layer_class in tf_main_layer_classes:
__UpperCamelCase :Optional[int] = main_layer_class(__lowercase)
__UpperCamelCase :Optional[Any] = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype) for name, tensor in inputs_dict.items()
}
__UpperCamelCase :Dict = tf.keras.Model(__lowercase , outputs=main_layer(__lowercase))
__UpperCamelCase :str = model(__lowercase)
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :str = os.path.join(__lowercase , '''keras_model.h5''')
model.save(__lowercase)
__UpperCamelCase :List[Any] = tf.keras.models.load_model(
__lowercase , custom_objects={main_layer_class.__name__: main_layer_class})
assert isinstance(__lowercase , tf.keras.Model)
__UpperCamelCase :Optional[Any] = model(__lowercase)
self.assert_outputs_same(__lowercase , __lowercase)
@slow
def UpperCamelCase__ ( self) -> Dict:
# make mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :Optional[Any] = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :Any = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
__UpperCamelCase :Optional[int] = model_class(__lowercase)
__UpperCamelCase :Union[str, Any] = self._prepare_for_class(__lowercase , __lowercase)
__UpperCamelCase :Optional[int] = model(__lowercase , noise=__lowercase)
if model_class.__name__ == "TFViTMAEModel":
__UpperCamelCase :Any = outputs.last_hidden_state.numpy()
__UpperCamelCase :Optional[Any] = 0
else:
__UpperCamelCase :List[str] = outputs.logits.numpy()
__UpperCamelCase :Optional[int] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowercase , saved_model=__lowercase)
__UpperCamelCase :Optional[int] = model_class.from_pretrained(__lowercase)
__UpperCamelCase :List[str] = model(__lowercase , noise=__lowercase)
if model_class.__name__ == "TFViTMAEModel":
__UpperCamelCase :List[Any] = after_outputs['''last_hidden_state'''].numpy()
__UpperCamelCase :List[Any] = 0
else:
__UpperCamelCase :Any = after_outputs['''logits'''].numpy()
__UpperCamelCase :Tuple = 0
__UpperCamelCase :Any = np.amax(np.abs(out_a - out_a))
self.assertLessEqual(__lowercase , 1E-5)
def UpperCamelCase__ ( self) -> Union[str, Any]:
# make mask reproducible
np.random.seed(2)
__UpperCamelCase , __UpperCamelCase :Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase :str = int((config.image_size // config.patch_size) ** 2)
__UpperCamelCase :Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
for model_class in self.all_model_classes:
__UpperCamelCase :Tuple = model_class(__lowercase)
__UpperCamelCase :Any = self._prepare_for_class(__lowercase , __lowercase)
__UpperCamelCase :Tuple = model(__lowercase , noise=__lowercase)
__UpperCamelCase :List[Any] = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__lowercase)
__UpperCamelCase :Optional[Any] = model_class.from_config(model.get_config())
# make sure it also accepts a normal config
__UpperCamelCase :Any = model_class.from_config(model.config)
__UpperCamelCase :List[Any] = new_model(__lowercase) # Build model
new_model.set_weights(model.get_weights())
__UpperCamelCase :str = new_model(__lowercase , noise=__lowercase)
self.assert_outputs_same(__lowercase , __lowercase)
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''')
def UpperCamelCase__ ( self) -> Dict:
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''')
def UpperCamelCase__ ( self) -> Any:
pass
@slow
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :List[Any] = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''')
self.assertIsNotNone(__lowercase)
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase__ ( self) -> Optional[Any]:
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''') if is_vision_available() else None
@slow
def UpperCamelCase__ ( self) -> List[str]:
# make random mask reproducible across the PT and TF model
np.random.seed(2)
__UpperCamelCase :Optional[Any] = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''')
__UpperCamelCase :Optional[int] = self.default_image_processor
__UpperCamelCase :Optional[int] = prepare_img()
__UpperCamelCase :Optional[int] = image_processor(images=__lowercase , 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)
__UpperCamelCase :Union[str, Any] = ViTMAEConfig()
__UpperCamelCase :Union[str, Any] = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
__UpperCamelCase :Tuple = np.random.uniform(size=(1, num_patches))
# forward pass
__UpperCamelCase :int = model(**__lowercase , noise=__lowercase)
# verify the logits
__UpperCamelCase :Optional[int] = tf.convert_to_tensor([1, 196, 768])
self.assertEqual(outputs.logits.shape , __lowercase)
__UpperCamelCase :List[Any] = tf.convert_to_tensor(
[[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])
tf.debugging.assert_near(outputs.logits[0, :3, :3] , __lowercase , atol=1E-4)
| 43 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCAmelCase :
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
@classmethod
def __magic_name__ ( cls : Any ):
return cls()
@dataclass
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@property
def __magic_name__ ( self : Optional[int] ):
return True
@register_to_config
def __init__( self : Optional[int], __A : float = 0.0_2, __A : float = 1_0_0, __A : float = 1.0_0_7, __A : float = 8_0, __A : float = 0.0_5, __A : float = 5_0, ):
pass
def __magic_name__ ( self : Optional[Any] ):
return KarrasVeSchedulerState.create()
def __magic_name__ ( self : int, __A : KarrasVeSchedulerState, __A : int, __A : Tuple = () ):
UpperCAmelCase : Optional[Any] = jnp.arange(0, __A )[::-1].copy()
UpperCAmelCase : Union[str, Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A, schedule=jnp.array(__A, dtype=jnp.floataa ), timesteps=__A, )
def __magic_name__ ( self : List[Any], __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : random.KeyArray, ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase : int = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1 )
else:
UpperCAmelCase : Optional[int] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase : Union[str, Any] = random.split(__A, num=1 )
UpperCAmelCase : List[str] = self.config.s_noise * random.normal(key=__A, shape=sample.shape )
UpperCAmelCase : Tuple = sigma + gamma * sigma
UpperCAmelCase : List[str] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : int = sample_hat + sigma_hat * model_output
UpperCAmelCase : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : jnp.ndarray, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
UpperCAmelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Optional[Any], __A : KarrasVeSchedulerState, __A : Optional[int], __A : int, __A : Union[str, Any] ):
raise NotImplementedError()
| 336 | 0 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[str] ) -> Dict:
_lowerCAmelCase : Any = min(_lowerCamelCase ) # min() finds the minimum value
_lowerCAmelCase : Dict = max(_lowerCamelCase ) # max() finds the maximum value
_lowerCAmelCase : Tuple = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
_lowerCAmelCase : Optional[Any] = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(_lowerCamelCase ,_lowerCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
_lowerCAmelCase : Tuple = 0
for count in range(_lowerCamelCase ):
while holes[count] > 0:
holes[count] -= 1
_lowerCAmelCase : Optional[int] = count + min_val
i += 1
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
_lowerCAmelCase : List[str] = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(_lowerCamelCase )
print("""Sorted order is:""" ,""" """.join(_lowerCamelCase ) )
if __name__ == "__main__":
main()
| 44 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCAmelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def a__ ( ) -> Dict:
if os.name == "nt":
UpperCAmelCase : List[str] = CursorInfo()
UpperCAmelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def a__ ( ) -> Optional[int]:
if os.name == "nt":
UpperCAmelCase : int = CursorInfo()
UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Any = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def a__ ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 336 | 0 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num_examples=42 , dataset_name='''my_dataset''' )} ),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num_examples=42 )} ),
SplitDict({'''train''': SplitInfo()} ),
] , )
def lowercase ( lowerCAmelCase__ : SplitDict ) -> List[Any]:
__a = split_dict._to_yaml_list()
assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
__a = SplitDict._from_yaml_list(lowerCAmelCase__ )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
__a = None
# the split name of split_dict takes over the name of the split info object
__a = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
'''split_info''' , [SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__ ), SplitInfo(dataset_name='''my_dataset''' )] )
def lowercase ( lowerCAmelCase__ : int ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
__a = asdict(SplitDict({'''train''': split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 45 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 | 0 |
"""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 CLIPImageProcessor, CLIPProcessor
@require_vision
class lowercase ( unittest.TestCase ):
def _snake_case ( self ) -> int:
lowerCAmelCase = tempfile.mkdtemp()
# fmt: off
lowerCAmelCase = ["""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
lowerCAmelCase = dict(zip(lowercase , range(len(lowercase ) ) ) )
lowerCAmelCase = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
lowerCAmelCase = {"""unk_token""": """<unk>"""}
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase = 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 ) )
lowerCAmelCase = {
"""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],
}
lowerCAmelCase = os.path.join(self.tmpdirname , lowercase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(lowercase , lowercase )
def _snake_case ( self , **lowercase ) -> Dict:
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowercase )
def _snake_case ( self , **lowercase ) -> List[str]:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowercase )
def _snake_case ( self , **lowercase ) -> int:
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **lowercase )
def _snake_case ( self ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def _snake_case ( self ) -> Optional[int]:
lowerCAmelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCAmelCase = [Image.fromarray(np.moveaxis(lowercase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = self.get_rust_tokenizer()
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = CLIPProcessor(tokenizer=lowercase , image_processor=lowercase )
processor_slow.save_pretrained(self.tmpdirname )
lowerCAmelCase = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=lowercase )
lowerCAmelCase = CLIPProcessor(tokenizer=lowercase , image_processor=lowercase )
processor_fast.save_pretrained(self.tmpdirname )
lowerCAmelCase = CLIPProcessor.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 _snake_case ( self ) -> Any:
lowerCAmelCase = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase = self.get_image_processor(do_normalize=lowercase , padding_value=1.0 )
lowerCAmelCase = CLIPProcessor.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 _snake_case ( self ) -> int:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = CLIPProcessor(tokenizer=lowercase , image_processor=lowercase )
lowerCAmelCase = self.prepare_image_inputs()
lowerCAmelCase = image_processor(lowercase , return_tensors="""np""" )
lowerCAmelCase = processor(images=lowercase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = CLIPProcessor(tokenizer=lowercase , image_processor=lowercase )
lowerCAmelCase = """lower newer"""
lowerCAmelCase = processor(text=lowercase )
lowerCAmelCase = tokenizer(lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = CLIPProcessor(tokenizer=lowercase , image_processor=lowercase )
lowerCAmelCase = """lower newer"""
lowerCAmelCase = self.prepare_image_inputs()
lowerCAmelCase = 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 _snake_case ( self ) -> int:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = CLIPProcessor(tokenizer=lowercase , image_processor=lowercase )
lowerCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase = processor.batch_decode(lowercase )
lowerCAmelCase = tokenizer.batch_decode(lowercase )
self.assertListEqual(lowercase , lowercase )
def _snake_case ( self ) -> str:
lowerCAmelCase = self.get_image_processor()
lowerCAmelCase = self.get_tokenizer()
lowerCAmelCase = CLIPProcessor(tokenizer=lowercase , image_processor=lowercase )
lowerCAmelCase = """lower newer"""
lowerCAmelCase = self.prepare_image_inputs()
lowerCAmelCase = processor(text=lowercase , images=lowercase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 46 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCamelCase : str = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
"processing_trocr": ["TrOCRProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[int] = [
"TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrOCRForCausalLM",
"TrOCRPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 47 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( UpperCAmelCase : Dict=None ) -> Optional[int]:
if subparsers is not None:
UpperCAmelCase : Tuple = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase : Dict = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase : Optional[int] = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=UpperCAmelCase , default=UpperCAmelCase , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=UpperCAmelCase , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=UpperCAmelCase , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase : Union[str, Any] = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=UpperCAmelCase , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase )
return parser
def a__ ( UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase : List[str] = defaults.commands
if not args.tpu_name:
UpperCAmelCase : Tuple = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase : int = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase : Tuple = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase : Dict = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , UpperCAmelCase ):
UpperCAmelCase : Optional[int] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , UpperCAmelCase ):
UpperCAmelCase : int = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase : Optional[int] = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
UpperCAmelCase : int = '''; '''.join(UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase : Any = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(UpperCAmelCase )}''' )
return
subprocess.run(UpperCAmelCase )
print('''Successfully setup pod.''' )
def a__ ( ) -> Any:
UpperCAmelCase : Any = tpu_command_parser()
UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(UpperCAmelCase )
| 336 | 0 |
import logging
from transformers.configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[str] = logging.getLogger(__name__)
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = """masked_bert"""
def __init__( self , UpperCamelCase__=3_0522 , UpperCamelCase__=768 , UpperCamelCase__=12 , UpperCamelCase__=12 , UpperCamelCase__=3072 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-12 , UpperCamelCase__=0 , UpperCamelCase__="topK" , UpperCamelCase__="constant" , UpperCamelCase__=0.0 , **UpperCamelCase__ , ) -> List[Any]:
super().__init__(pad_token_id=UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase : int = vocab_size
lowerCamelCase : Tuple = hidden_size
lowerCamelCase : Dict = num_hidden_layers
lowerCamelCase : Union[str, Any] = num_attention_heads
lowerCamelCase : List[str] = hidden_act
lowerCamelCase : List[str] = intermediate_size
lowerCamelCase : Dict = hidden_dropout_prob
lowerCamelCase : Tuple = attention_probs_dropout_prob
lowerCamelCase : Tuple = max_position_embeddings
lowerCamelCase : Any = type_vocab_size
lowerCamelCase : str = initializer_range
lowerCamelCase : Union[str, Any] = layer_norm_eps
lowerCamelCase : int = pruning_method
lowerCamelCase : Tuple = mask_init
lowerCamelCase : List[Any] = mask_scale
| 48 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 0 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def __snake_case ( _UpperCAmelCase ):
__a = VideoMAEConfig()
set_architecture_configs(_UpperCAmelCase , _UpperCAmelCase )
if "finetuned" not in model_name:
__a = False
if "finetuned" in model_name:
__a = '''huggingface/label-files'''
if "kinetics" in model_name:
__a = 400
__a = '''kinetics400-id2label.json'''
elif "ssv2" in model_name:
__a = 174
__a = '''something-something-v2-id2label.json'''
else:
raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.''' )
__a = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
__a = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__a = idalabel
__a = {v: k for k, v in idalabel.items()}
return config
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
if "small" in model_name:
__a = 384
__a = 1536
__a = 12
__a = 16
__a = 12
__a = 3
__a = 192
__a = 768
elif "large" in model_name:
__a = 1024
__a = 4096
__a = 24
__a = 16
__a = 12
__a = 8
__a = 512
__a = 2048
elif "huge" in model_name:
__a = 1280
__a = 5120
__a = 32
__a = 16
__a = 12
__a = 8
__a = 640
__a = 2560
elif "base" not in model_name:
raise ValueError('''Model name should include either "small", "base", "large", or "huge"''' )
def __snake_case ( _UpperCAmelCase ):
if "encoder." in name:
__a = name.replace('''encoder.''' , '''''' )
if "cls_token" in name:
__a = name.replace('''cls_token''' , '''videomae.embeddings.cls_token''' )
if "decoder_pos_embed" in name:
__a = name.replace('''decoder_pos_embed''' , '''decoder.decoder_pos_embed''' )
if "pos_embed" in name and "decoder" not in name:
__a = name.replace('''pos_embed''' , '''videomae.embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
__a = name.replace('''patch_embed.proj''' , '''videomae.embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
__a = name.replace('''patch_embed.norm''' , '''videomae.embeddings.norm''' )
if "decoder.blocks" in name:
__a = name.replace('''decoder.blocks''' , '''decoder.decoder_layers''' )
if "blocks" in name:
__a = name.replace('''blocks''' , '''videomae.encoder.layer''' )
if "attn.proj" in name:
__a = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name and "bias" not in name:
__a = name.replace('''attn''' , '''attention.self''' )
if "attn" in name:
__a = name.replace('''attn''' , '''attention.attention''' )
if "norm1" in name:
__a = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
__a = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
__a = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
__a = name.replace('''mlp.fc2''' , '''output.dense''' )
if "decoder_embed" in name:
__a = name.replace('''decoder_embed''' , '''decoder.decoder_embed''' )
if "decoder_norm" in name:
__a = name.replace('''decoder_norm''' , '''decoder.decoder_norm''' )
if "decoder_pred" in name:
__a = name.replace('''decoder_pred''' , '''decoder.decoder_pred''' )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
__a = name.replace('''norm.weight''' , '''videomae.layernorm.weight''' )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
__a = name.replace('''norm.bias''' , '''videomae.layernorm.bias''' )
if "head" in name and "decoder" not in name:
__a = name.replace('''head''' , '''classifier''' )
return name
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
for key in orig_state_dict.copy().keys():
__a = orig_state_dict.pop(_UpperCAmelCase )
if key.startswith('''encoder.''' ):
__a = key.replace('''encoder.''' , '''''' )
if "qkv" in key:
__a = key.split('''.''' )
if key.startswith('''decoder.blocks''' ):
__a = config.decoder_hidden_size
__a = int(key_split[2] )
__a = '''decoder.decoder_layers.'''
if "weight" in key:
__a = val[:dim, :]
__a = val[dim : dim * 2, :]
__a = val[-dim:, :]
else:
__a = config.hidden_size
__a = int(key_split[1] )
__a = '''videomae.encoder.layer.'''
if "weight" in key:
__a = val[:dim, :]
__a = val[dim : dim * 2, :]
__a = val[-dim:, :]
else:
__a = val
return orig_state_dict
def __snake_case ( ):
__a = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
__a = np.load(_UpperCAmelCase )
return list(_UpperCAmelCase )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = get_videomae_config(_UpperCAmelCase )
if "finetuned" in model_name:
__a = VideoMAEForVideoClassification(_UpperCAmelCase )
else:
__a = VideoMAEForPreTraining(_UpperCAmelCase )
# download original checkpoint, hosted on Google Drive
__a = '''pytorch_model.bin'''
gdown.cached_download(_UpperCAmelCase , _UpperCAmelCase , quiet=_UpperCAmelCase )
__a = torch.load(_UpperCAmelCase , map_location='''cpu''' )
if "model" in files:
__a = files['''model''']
else:
__a = files['''module''']
__a = convert_state_dict(_UpperCAmelCase , _UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
model.eval()
# verify model on basic input
__a = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
__a = prepare_video()
__a = image_processor(_UpperCAmelCase , return_tensors='''pt''' )
if "finetuned" not in model_name:
__a = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' , filename='''bool_masked_pos.pt''' )
__a = torch.load(_UpperCAmelCase )
__a = model(**_UpperCAmelCase )
__a = outputs.logits
__a = [
'''videomae-small-finetuned-kinetics''',
'''videomae-small-finetuned-ssv2''',
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
'''videomae-base-short''',
'''videomae-base-short-finetuned-kinetics''',
'''videomae-base''',
'''videomae-base-finetuned-kinetics''',
'''videomae-large''',
'''videomae-large-finetuned-kinetics''',
'''videomae-huge-finetuned-kinetics''',
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
'''videomae-base-short-ssv2''',
'''videomae-base-short-finetuned-ssv2''',
'''videomae-base-ssv2''',
'''videomae-base-finetuned-ssv2''',
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
__a = torch.Size([1, 400] )
__a = torch.tensor([-0.92_91, -0.40_61, -0.93_07] )
elif model_name == "videomae-small-finetuned-ssv2":
__a = torch.Size([1, 174] )
__a = torch.tensor([0.26_71, -0.46_89, -0.82_35] )
elif model_name == "videomae-base":
__a = torch.Size([1, 1408, 1536] )
__a = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]] )
elif model_name == "videomae-base-short":
__a = torch.Size([1, 1408, 1536] )
__a = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]] )
# we verified the loss both for normalized and unnormalized targets for this one
__a = torch.tensor([0.51_42] ) if config.norm_pix_loss else torch.tensor([0.64_69] )
elif model_name == "videomae-large":
__a = torch.Size([1, 1408, 1536] )
__a = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]] )
elif model_name == "videomae-large-finetuned-kinetics":
__a = torch.Size([1, 400] )
__a = torch.tensor([0.07_71, 0.00_11, -0.36_25] )
elif model_name == "videomae-huge-finetuned-kinetics":
__a = torch.Size([1, 400] )
__a = torch.tensor([0.24_33, 0.16_32, -0.48_94] )
elif model_name == "videomae-base-short-finetuned-kinetics":
__a = torch.Size([1, 400] )
__a = torch.tensor([0.65_88, 0.09_90, -0.24_93] )
elif model_name == "videomae-base-finetuned-kinetics":
__a = torch.Size([1, 400] )
__a = torch.tensor([0.36_69, -0.06_88, -0.24_21] )
elif model_name == "videomae-base-short-ssv2":
__a = torch.Size([1, 1408, 1536] )
__a = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
__a = torch.Size([1, 174] )
__a = torch.tensor([-0.05_37, -0.15_39, -0.32_66] )
elif model_name == "videomae-base-ssv2":
__a = torch.Size([1, 1408, 1536] )
__a = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]] )
elif model_name == "videomae-base-finetuned-ssv2":
__a = torch.Size([1, 174] )
__a = torch.tensor([0.19_61, -0.83_37, -0.63_89] )
else:
raise ValueError(f'Model name not supported. Should be one of {model_names}' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , _UpperCAmelCase , atol=1E-4 )
else:
print('''Logits:''' , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , _UpperCAmelCase , atol=1E-4 )
print('''Logits ok!''' )
# verify loss, if applicable
if model_name == "videomae-base-short":
__a = outputs.loss
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-4 )
print('''Loss ok!''' )
if pytorch_dump_folder_path is not None:
print(f'Saving model and image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print('''Pushing to the hub...''' )
model.push_to_hub(_UpperCAmelCase , organization='''nielsr''' )
if __name__ == "__main__":
__snake_case :str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4''',
type=str,
help=(
'''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
''' download link.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''/Users/nielsrogge/Documents/VideoMAE/Test''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--model_name''', default='''videomae-base''', type=str, help='''Name of the model.''')
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
__snake_case :Union[str, Any] = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 49 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __UpperCAmelCase ( lowerCamelCase__ ):
def __get__( self : Tuple, __A : Optional[Any], __A : Optional[int]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
UpperCAmelCase : str = '''__cached_''' + self.fget.__name__
UpperCAmelCase : int = getattr(__A, __A, __A )
if cached is None:
UpperCAmelCase : Any = self.fget(__A )
setattr(__A, __A, __A )
return cached
def a__ ( UpperCAmelCase : Optional[Any] ) -> Any:
UpperCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_torch_fx_proxy(UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(UpperCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : str ) -> Tuple:
return _is_numpy(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
import torch
return isinstance(UpperCAmelCase , torch.Tensor )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
return False if not is_torch_available() else _is_torch(UpperCAmelCase )
def a__ ( UpperCAmelCase : Tuple ) -> List[str]:
import torch
return isinstance(UpperCAmelCase , torch.device )
def a__ ( UpperCAmelCase : Any ) -> Any:
return False if not is_torch_available() else _is_torch_device(UpperCAmelCase )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
import torch
if isinstance(UpperCAmelCase , UpperCAmelCase ):
if hasattr(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
else:
return False
return isinstance(UpperCAmelCase , torch.dtype )
def a__ ( UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> str:
import tensorflow as tf
return isinstance(UpperCAmelCase , tf.Tensor )
def a__ ( UpperCAmelCase : int ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCAmelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(UpperCAmelCase )
return type(UpperCAmelCase ) == tf.Tensor
def a__ ( UpperCAmelCase : int ) -> List[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[Any] ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCAmelCase , jnp.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Optional[int]:
return False if not is_flax_available() else _is_jax(UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Tuple:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return [to_py_obj(UpperCAmelCase ) for o in obj]
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase ).tolist()
elif isinstance(UpperCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( UpperCAmelCase : Any ) -> List[str]:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_numpy(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return np.array(UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase )
else:
return obj
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(__A ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
UpperCAmelCase : int = getattr(self, class_fields[0].name )
UpperCAmelCase : str = all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__A ):
if isinstance(__A, __A ):
UpperCAmelCase : Tuple = first_field.items()
UpperCAmelCase : Any = True
else:
try:
UpperCAmelCase : Optional[Any] = iter(__A )
UpperCAmelCase : Optional[Any] = True
except TypeError:
UpperCAmelCase : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__A ):
if (
not isinstance(__A, (list, tuple) )
or not len(__A ) == 2
or not isinstance(element[0], __A )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
UpperCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
UpperCAmelCase : Union[str, Any] = element[1]
elif first_field is not None:
UpperCAmelCase : Union[str, Any] = first_field
else:
for field in class_fields:
UpperCAmelCase : Optional[Any] = getattr(self, field.name )
if v is not None:
UpperCAmelCase : Optional[int] = v
def __delitem__( self : Union[str, Any], *__A : str, **__A : Tuple ):
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : List[str], *__A : Union[str, Any], **__A : Optional[Any] ):
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Any, *__A : Dict, **__A : str ):
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Dict, *__A : int, **__A : Dict ):
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : List[str], __A : List[str] ):
if isinstance(__A, __A ):
UpperCAmelCase : int = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[Any], __A : Dict, __A : Union[str, Any] ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__A, __A )
super().__setattr__(__A, __A )
def __setitem__( self : Dict, __A : List[Any], __A : Union[str, Any] ):
# Will raise a KeyException if needed
super().__setitem__(__A, __A )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__A, __A )
def __magic_name__ ( self : List[str] ):
return tuple(self[k] for k in self.keys() )
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@classmethod
def __magic_name__ ( cls : List[Any], __A : Tuple ):
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """longest"""
UpperCamelCase = """max_length"""
UpperCamelCase = """do_not_pad"""
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
UpperCamelCase = """np"""
UpperCamelCase = """jax"""
class __UpperCAmelCase :
def __init__( self : Any, __A : List[ContextManager] ):
UpperCAmelCase : Tuple = context_managers
UpperCAmelCase : Tuple = ExitStack()
def __enter__( self : Any ):
for context_manager in self.context_managers:
self.stack.enter_context(__A )
def __exit__( self : List[Any], *__A : Union[str, Any], **__A : Dict ):
self.stack.__exit__(*__A, **__A )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : int = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a__ ( UpperCAmelCase : Dict ) -> Any:
UpperCAmelCase : List[Any] = model_class.__name__
UpperCAmelCase : Union[str, Any] = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a__ ( UpperCAmelCase : MutableMapping , UpperCAmelCase : str = "" , UpperCAmelCase : str = "." ) -> Union[str, Any]:
def _flatten_dict(UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str]="" , UpperCAmelCase : Any="." ):
for k, v in d.items():
UpperCAmelCase : List[str] = str(UpperCAmelCase ) + delimiter + str(UpperCAmelCase ) if parent_key else k
if v and isinstance(UpperCAmelCase , UpperCAmelCase ):
yield from flatten_dict(UpperCAmelCase , UpperCAmelCase , delimiter=UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
@contextmanager
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : bool = False ) -> Optional[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if is_numpy_array(UpperCAmelCase ):
return np.transpose(UpperCAmelCase , axes=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.T if axes is None else array.permute(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(UpperCAmelCase , perm=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.transpose(UpperCAmelCase , axes=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Optional[int] ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.reshape(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.reshape(UpperCAmelCase , UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=None ) -> Any:
if is_numpy_array(UpperCAmelCase ):
return np.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> str:
if is_numpy_array(UpperCAmelCase ):
return np.expand_dims(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.unsqueeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.size(UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.size(UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Dict:
for key, value in auto_map.items():
if isinstance(UpperCAmelCase , (tuple, list) ):
UpperCAmelCase : List[Any] = [f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
UpperCAmelCase : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a__ ( UpperCAmelCase : Tuple ) -> Union[str, Any]:
for base_class in inspect.getmro(UpperCAmelCase ):
UpperCAmelCase : Any = base_class.__module__
UpperCAmelCase : Dict = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 336 | 0 |
from __future__ import annotations
import math
_UpperCAmelCase : Any = """2020.9.26"""
_UpperCAmelCase : Optional[int] = """xcodz-dot, cclaus, dhruvmanila"""
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> tuple[float, float]:
if not all(isinstance(_UpperCAmelCase , (float, int) ) for val in locals().values() ):
lowerCamelCase__ : Any = F"""Input values must either be float or int: {list(locals().values() )}"""
raise TypeError(_UpperCAmelCase )
lowerCamelCase__ : str = ((x * distance) / (z + distance)) * scale
lowerCamelCase__ : Union[str, Any] = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> tuple[float, float, float]:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Axis must be a str' )
lowerCamelCase__ : List[str] = locals()
del input_variables["axis"]
if not all(isinstance(_UpperCAmelCase , (float, int) ) for val in input_variables.values() ):
lowerCamelCase__ : str = (
'Input values except axis must either be float or int: '
F"""{list(input_variables.values() )}"""
)
raise TypeError(_UpperCAmelCase )
lowerCamelCase__ : Any = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
lowerCamelCase__ : Dict = x * math.cos(_UpperCAmelCase ) - y * math.sin(_UpperCAmelCase )
lowerCamelCase__ : Dict = y * math.cos(_UpperCAmelCase ) + x * math.sin(_UpperCAmelCase )
lowerCamelCase__ : int = z
elif axis == "x":
lowerCamelCase__ : Dict = y * math.cos(_UpperCAmelCase ) - z * math.sin(_UpperCAmelCase )
lowerCamelCase__ : int = z * math.cos(_UpperCAmelCase ) + y * math.sin(_UpperCAmelCase )
lowerCamelCase__ : str = x
elif axis == "y":
lowerCamelCase__ : List[str] = x * math.cos(_UpperCAmelCase ) - z * math.sin(_UpperCAmelCase )
lowerCamelCase__ : int = z * math.cos(_UpperCAmelCase ) + x * math.sin(_UpperCAmelCase )
lowerCamelCase__ : Any = y
else:
raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\'' )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")
| 50 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LayoutLMTokenizer
UpperCamelCase = LayoutLMTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
UpperCAmelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : int = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __magic_name__ ( self : Union[str, Any], **__A : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], __A : int ):
UpperCAmelCase : Optional[Any] = '''UNwant\u00E9d,running'''
UpperCAmelCase : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [7, 4, 5, 1_0, 8, 9] )
def __magic_name__ ( self : Optional[int] ):
pass
| 336 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class __snake_case ( a ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
UpperCAmelCase__ : str = field(default='''text-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
UpperCAmelCase__ : ClassVar[Features] = Features({'''text''': Value('''string''' )} )
UpperCAmelCase__ : ClassVar[Features] = Features({'''labels''': ClassLabel} )
UpperCAmelCase__ : str = "text"
UpperCAmelCase__ : str = "labels"
def lowerCamelCase ( self : Optional[int] , _snake_case : List[str]):
"""simple docstring"""
if self.label_column not in features:
raise ValueError(F"""Column {self.label_column} is not present in features.""")
if not isinstance(features[self.label_column] , _snake_case):
raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
UpperCAmelCase_ = copy.deepcopy(self)
UpperCAmelCase_ = self.label_schema.copy()
UpperCAmelCase_ = features[self.label_column]
UpperCAmelCase_ = label_schema
return task_template
@property
def lowerCamelCase ( self : Dict):
"""simple docstring"""
return {
self.text_column: "text",
self.label_column: "labels",
}
| 51 |
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 __UpperCAmelCase :
def __init__( self : Any, __A : str, __A : Dict=1_3, __A : int=3_0, __A : Tuple=2, __A : Union[str, Any]=3, __A : Any=True, __A : str=True, __A : Dict=3_2, __A : List[Any]=2, __A : Optional[Any]=4, __A : Union[str, Any]=3_7, __A : int="gelu", __A : int=0.1, __A : List[Any]=0.1, __A : Tuple=1_0, __A : Tuple=0.0_2, __A : Any=3, __A : List[str]=0.6, __A : Any=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Optional[int] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Tuple = mask_ratio
UpperCAmelCase : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Optional[Any] ):
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=__A, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def __magic_name__ ( self : str, __A : List[Any], __A : Any, __A : Any ):
UpperCAmelCase : Optional[Any] = TFViTMAEModel(config=__A )
UpperCAmelCase : Tuple = model(__A, training=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : str, __A : int, __A : str ):
UpperCAmelCase : Dict = TFViTMAEForPreTraining(__A )
UpperCAmelCase : int = model(__A, training=__A )
# expected sequence length = num_patches
UpperCAmelCase : int = (self.image_size // self.patch_size) ** 2
UpperCAmelCase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase : Tuple = 1
UpperCAmelCase : List[Any] = TFViTMAEForPreTraining(__A )
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = model(__A, training=__A )
UpperCAmelCase : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , 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 __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = TFViTMAEModelTester(self )
UpperCAmelCase : int = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer) )
UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, tf.keras.layers.Layer ) )
def __magic_name__ ( self : str ):
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Any = model_class(__A )
UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : int = [*signature.parameters.keys()]
UpperCAmelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __magic_name__ ( self : int ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : str = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : Dict = model(__A, noise=__A )
UpperCAmelCase : Any = copy.deepcopy(self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Union[str, Any] = model(**__A, noise=__A )
UpperCAmelCase : Dict = outputs_dict[0].numpy()
UpperCAmelCase : Tuple = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ), 1E-6 )
def __magic_name__ ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__A : Union[str, Any] ):
UpperCAmelCase : str = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__A ):
UpperCAmelCase : Tuple = v.numpy()
else:
UpperCAmelCase : str = np.array(__A )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : Any = self._prepare_for_class(__A, __A )
UpperCAmelCase : Optional[int] = prepare_numpy_arrays(__A )
UpperCAmelCase : str = model(__A, noise=__A )
UpperCAmelCase : str = model(**__A, noise=__A )
self.assert_outputs_same(__A, __A )
def __magic_name__ ( self : int, __A : str, __A : Union[str, Any], __A : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase : Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : int = tf.constant(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase : List[Any] = tf_noise
super().check_pt_tf_models(__A, __A, __A )
def __magic_name__ ( self : str ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Union[str, Any] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__A )
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(__A, __A ),)
if isinstance(__A, __A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__A, '''_keras_serializable''', __A )
}
UpperCAmelCase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : str = tf.convert_to_tensor(__A )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCAmelCase : Tuple = main_layer_class(__A )
UpperCAmelCase : int = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCAmelCase : List[Any] = tf.keras.Model(__A, outputs=main_layer(__A ) )
UpperCAmelCase : List[Any] = model(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = os.path.join(__A, '''keras_model.h5''' )
model.save(__A )
UpperCAmelCase : List[str] = tf.keras.models.load_model(
__A, custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__A, tf.keras.Model )
UpperCAmelCase : Tuple = model(__A )
self.assert_outputs_same(__A, __A )
@slow
def __magic_name__ ( self : Dict ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(__A )
UpperCAmelCase : List[str] = self._prepare_for_class(__A, __A )
UpperCAmelCase : Union[str, Any] = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : Optional[int] = outputs.last_hidden_state.numpy()
UpperCAmelCase : Union[str, Any] = 0
else:
UpperCAmelCase : Optional[int] = outputs.logits.numpy()
UpperCAmelCase : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A, saved_model=__A )
UpperCAmelCase : Dict = model_class.from_pretrained(__A )
UpperCAmelCase : str = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : int = after_outputs['''last_hidden_state'''].numpy()
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Any = after_outputs['''logits'''].numpy()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A, 1E-5 )
def __magic_name__ ( self : Optional[Any] ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : List[Any] = model(__A, noise=__A )
UpperCAmelCase : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__A )
UpperCAmelCase : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCAmelCase : str = model_class.from_config(model.config )
UpperCAmelCase : List[str] = new_model(__A ) # Build model
new_model.set_weights(model.get_weights() )
UpperCAmelCase : Tuple = new_model(__A, noise=__A )
self.assert_outputs_same(__A, __A )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def __magic_name__ ( self : Tuple ):
pass
@slow
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(__A )
def a__ ( ) -> Dict:
UpperCAmelCase : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[str] ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase : Tuple = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
UpperCAmelCase : List[str] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : str = image_processor(images=__A, 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)
UpperCAmelCase : Optional[int] = ViTMAEConfig()
UpperCAmelCase : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCAmelCase : Optional[int] = model(**__A, noise=__A )
# verify the logits
UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3], __A, atol=1E-4 )
| 336 | 0 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
__lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCamelCase : Any = {
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
# See all BART models at https://huggingface.co/models?filter=bart
}
class A__ ( __snake_case ):
_UpperCAmelCase :Dict = 'bart'
_UpperCAmelCase :str = ['past_key_values']
_UpperCAmelCase :Any = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A_=5_0265 , A_=1024 , A_=12 , A_=4096 , A_=16 , A_=12 , A_=4096 , A_=16 , A_=0.0 , A_=0.0 , A_="gelu" , A_=1024 , A_=0.1 , A_=0.0 , A_=0.0 , A_=0.02 , A_=0.0 , A_=False , A_=True , A_=3 , A_=1 , A_=0 , A_=2 , A_=True , A_=2 , A_=2 , **A_ , ):
'''simple docstring'''
UpperCamelCase : int = vocab_size
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Any = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : List[Any] = encoder_layers
UpperCamelCase : int = encoder_attention_heads
UpperCamelCase : Optional[int] = decoder_ffn_dim
UpperCamelCase : List[str] = decoder_layers
UpperCamelCase : Optional[int] = decoder_attention_heads
UpperCamelCase : int = dropout
UpperCamelCase : int = attention_dropout
UpperCamelCase : Tuple = activation_dropout
UpperCamelCase : Tuple = activation_function
UpperCamelCase : int = init_std
UpperCamelCase : List[Any] = encoder_layerdrop
UpperCamelCase : List[str] = decoder_layerdrop
UpperCamelCase : Dict = classifier_dropout
UpperCamelCase : Optional[int] = use_cache
UpperCamelCase : List[Any] = encoder_layers
UpperCamelCase : int = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=A_ , pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , is_encoder_decoder=A_ , decoder_start_token_id=A_ , forced_eos_token_id=A_ , **A_ , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated" , A_ ):
UpperCamelCase : int = 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." )
class A__ ( __snake_case ):
@property
def __UpperCamelCase( self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase : Optional[int] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
UpperCamelCase : List[str] = {0: "batch"}
UpperCamelCase : Dict = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
UpperCamelCase : Dict = {0: "batch", 1: "decoder_sequence"}
UpperCamelCase : Union[str, Any] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(A_ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
UpperCamelCase : Any = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
UpperCamelCase , UpperCamelCase : Optional[int] = self.num_layers
for i in range(A_ ):
UpperCamelCase : Optional[Any] = {0: "batch", 2: "past_sequence + sequence"}
UpperCamelCase : Union[str, Any] = {0: "batch", 2: "past_sequence + sequence"}
else:
UpperCamelCase : Optional[Any] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
def __UpperCamelCase( self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase : Tuple = super().outputs
else:
UpperCamelCase : Dict = super(A_ , self ).outputs
if self.use_past:
UpperCamelCase , UpperCamelCase : int = self.num_layers
for i in range(A_ ):
UpperCamelCase : int = {0: "batch", 2: "past_sequence + sequence"}
UpperCamelCase : Tuple = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __UpperCamelCase( self , A_ , A_ = -1 , A_ = -1 , A_ = False , A_ = None , ):
'''simple docstring'''
UpperCamelCase : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
A_ , A_ , A_ , A_ , A_ )
# Generate decoder inputs
UpperCamelCase : List[Any] = seq_length if not self.use_past else 1
UpperCamelCase : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
A_ , A_ , A_ , A_ , A_ )
UpperCamelCase : Optional[int] = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
UpperCamelCase : List[Any] = dict(**A_ , **A_ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
UpperCamelCase , UpperCamelCase : Optional[Any] = common_inputs["input_ids"].shape
UpperCamelCase : List[Any] = common_inputs["decoder_input_ids"].shape[1]
UpperCamelCase , UpperCamelCase : List[str] = self.num_attention_heads
UpperCamelCase : int = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCamelCase : List[Any] = decoder_seq_length + 3
UpperCamelCase : str = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
UpperCamelCase : int = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(A_ , A_ )] , dim=1 )
UpperCamelCase : int = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
UpperCamelCase , UpperCamelCase : Union[str, Any] = self.num_layers
UpperCamelCase : Any = min(A_ , A_ )
UpperCamelCase : List[str] = max(A_ , A_ ) - min_num_layers
UpperCamelCase : Dict = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(A_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(A_ ),
torch.zeros(A_ ),
torch.zeros(A_ ),
torch.zeros(A_ ),
) )
# TODO: test this.
UpperCamelCase : Optional[Any] = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(A_ , A_ ):
common_inputs["past_key_values"].append((torch.zeros(A_ ), torch.zeros(A_ )) )
return common_inputs
def __UpperCamelCase( self , A_ , A_ = -1 , A_ = -1 , A_ = False , A_ = None , ):
'''simple docstring'''
UpperCamelCase : int = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
A_ , A_ , A_ , A_ , A_ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
UpperCamelCase , UpperCamelCase : Union[str, Any] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
UpperCamelCase : Optional[Any] = seqlen + 2
UpperCamelCase , UpperCamelCase : List[Any] = self.num_layers
UpperCamelCase , UpperCamelCase : Optional[int] = self.num_attention_heads
UpperCamelCase : str = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
UpperCamelCase : Optional[Any] = common_inputs["attention_mask"].dtype
UpperCamelCase : int = torch.cat(
[common_inputs["attention_mask"], torch.ones(A_ , A_ , dtype=A_ )] , dim=1 )
UpperCamelCase : Optional[Any] = [
(torch.zeros(A_ ), torch.zeros(A_ )) for _ in range(A_ )
]
return common_inputs
def __UpperCamelCase( self , A_ , A_ = -1 , A_ = -1 , A_ = False , A_ = None , ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = compute_effective_axis_dimension(
A_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCamelCase : Union[str, Any] = tokenizer.num_special_tokens_to_add(A_ )
UpperCamelCase : int = compute_effective_axis_dimension(
A_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=A_ )
# Generate dummy inputs according to compute batch and sequence
UpperCamelCase : int = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
UpperCamelCase : Dict = dict(tokenizer(A_ , return_tensors=A_ ) )
return common_inputs
def __UpperCamelCase( self , A_ , A_ = -1 , A_ = -1 , A_ = False , A_ = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase : Optional[int] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
A_ , batch_size=A_ , seq_length=A_ , is_pair=A_ , framework=A_ )
elif self.task == "causal-lm":
UpperCamelCase : List[str] = self._generate_dummy_inputs_for_causal_lm(
A_ , batch_size=A_ , seq_length=A_ , is_pair=A_ , framework=A_ )
else:
UpperCamelCase : List[str] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
A_ , batch_size=A_ , seq_length=A_ , is_pair=A_ , framework=A_ )
return common_inputs
def __UpperCamelCase( self , A_ , A_ , A_ , A_ ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
UpperCamelCase : Optional[Any] = super()._flatten_past_key_values_(A_ , A_ , A_ , A_ )
else:
UpperCamelCase : Optional[Any] = super(A_ , self )._flatten_past_key_values_(
A_ , A_ , A_ , A_ )
| 52 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_lowerCamelCase : List[Any] = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
_lowerCamelCase : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 336 | 0 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
a__ : Tuple =logging.getLogger(__name__)
def lowercase__ ( __lowercase : str ) -> str:
"""simple docstring"""
__UpperCamelCase = git.Repo(search_parent_directories=__lowercase )
__UpperCamelCase = {
'repo_id': str(__lowercase ),
'repo_sha': str(repo.head.object.hexsha ),
'repo_branch': str(repo.active_branch ),
}
with open(os.path.join(__lowercase , 'git_log.json' ) , 'w' ) as f:
json.dump(__lowercase , __lowercase , indent=4 )
def lowercase__ ( __lowercase : int ) -> List[Any]:
"""simple docstring"""
if params.n_gpu <= 0:
__UpperCamelCase = 0
__UpperCamelCase = -1
__UpperCamelCase = True
__UpperCamelCase = False
return
assert torch.cuda.is_available()
logger.info('Initializing GPUs' )
if params.n_gpu > 1:
assert params.local_rank != -1
__UpperCamelCase = int(os.environ['WORLD_SIZE'] )
__UpperCamelCase = int(os.environ['N_GPU_NODE'] )
__UpperCamelCase = int(os.environ['RANK'] )
# number of nodes / node ID
__UpperCamelCase = params.world_size // params.n_gpu_per_node
__UpperCamelCase = params.global_rank // params.n_gpu_per_node
__UpperCamelCase = True
assert params.n_nodes == int(os.environ['N_NODES'] )
assert params.node_id == int(os.environ['NODE_RANK'] )
# local job (single GPU)
else:
assert params.local_rank == -1
__UpperCamelCase = 1
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = 1
__UpperCamelCase = 1
__UpperCamelCase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
__UpperCamelCase = params.node_id == 0 and params.local_rank == 0
__UpperCamelCase = params.n_nodes > 1
# summary
__UpperCamelCase = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes )
logger.info(PREFIX + 'Node ID : %i' % params.node_id )
logger.info(PREFIX + 'Local rank : %i' % params.local_rank )
logger.info(PREFIX + 'World size : %i' % params.world_size )
logger.info(PREFIX + 'GPUs per node : %i' % params.n_gpu_per_node )
logger.info(PREFIX + 'Master : %s' % str(params.is_master ) )
logger.info(PREFIX + 'Multi-node : %s' % str(params.multi_node ) )
logger.info(PREFIX + 'Multi-GPU : %s' % str(params.multi_gpu ) )
logger.info(PREFIX + 'Hostname : %s' % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info('Initializing PyTorch distributed' )
torch.distributed.init_process_group(
init_method='env://' , backend='nccl' , )
def lowercase__ ( __lowercase : List[Any] ) -> Any:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 53 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] for _ in range(UpperCAmelCase )]
UpperCAmelCase : Union[str, Any] = run_maze(UpperCAmelCase , 0 , 0 , UpperCAmelCase )
if solved:
print('''\n'''.join(str(UpperCAmelCase ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def a__ ( UpperCAmelCase : list[list[int]] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Dict = len(UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase : Dict = 1
return True
UpperCAmelCase : Union[str, Any] = (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 : Any = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase : str = 1
# check for directions
if (
run_maze(UpperCAmelCase , i + 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j + 1 , UpperCAmelCase )
or run_maze(UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j - 1 , UpperCAmelCase )
):
return True
UpperCAmelCase : Any = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
a__ : Dict = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
a__ : List[str] = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
for i in range(len(lowerCAmelCase_ ) ):
__SCREAMING_SNAKE_CASE = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
__SCREAMING_SNAKE_CASE = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(lowerCAmelCase_ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(lowerCAmelCase_ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(lowerCAmelCase_ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
__SCREAMING_SNAKE_CASE = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(lowerCAmelCase_ )
return next_generation
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
for _ in range(lowerCAmelCase_ ):
# Create output image
__SCREAMING_SNAKE_CASE = Image.new("RGB" , (len(cells[0] ), len(lowerCAmelCase_ )) )
__SCREAMING_SNAKE_CASE = img.load()
# Save cells to image
for x in range(len(lowerCAmelCase_ ) ):
for y in range(len(cells[0] ) ):
__SCREAMING_SNAKE_CASE = 255 - cells[y][x] * 255
__SCREAMING_SNAKE_CASE = (colour, colour, colour)
# Save image
images.append(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = new_generation(lowerCAmelCase_ )
return images
if __name__ == "__main__":
a__ : Optional[Any] = generate_images(GLIDER, 1_6)
images[0].save('''out.gif''', save_all=True, append_images=images[1:])
| 54 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __UpperCAmelCase :
def __init__( self : List[Any], __A : List[str], __A : List[str]=1_3, __A : Any=6_4, __A : Optional[Any]=2, __A : str=3, __A : str=True, __A : str=True, __A : Optional[Any]=3_2, __A : List[str]=5, __A : int=4, __A : str=3_7, __A : str="gelu", __A : Dict=0.1, __A : List[Any]=0.1, __A : Dict=1_0, __A : int=0.0_2, __A : Any=[1, 1_6, 4, 4], __A : Optional[int]=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Any = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : List[str] = patch_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase : str = type_sequence_label_size
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : int = scope
UpperCAmelCase : List[str] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
UpperCAmelCase : str = (self.image_size // 3_2) ** 2
UpperCAmelCase : List[str] = num_patches + 1
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : str = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Any ):
UpperCAmelCase : Dict = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 1_6, 3_2],
'''num_groups''': 2,
}
return ViTHybridConfig(
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, backbone_featmap_shape=self.backbone_featmap_shape, backbone_config=__A, )
def __magic_name__ ( self : Optional[int], __A : Optional[int], __A : int, __A : Tuple ):
UpperCAmelCase : int = ViTHybridModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Tuple = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : Dict, __A : str, __A : List[str] ):
UpperCAmelCase : str = self.type_sequence_label_size
UpperCAmelCase : List[Any] = ViTHybridForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : int ):
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = config_and_inputs
UpperCAmelCase : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
UpperCamelCase = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Any = ViTHybridModelTester(self )
UpperCAmelCase : List[Any] = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : int ):
UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, nn.Linear ) )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(__A )
UpperCAmelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = _config_zero_init(__A )
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(config=__A )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
UpperCAmelCase : Union[str, Any] = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@slow
def __magic_name__ ( self : List[str] ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Union[str, Any] = ViTHybridModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> Tuple:
UpperCAmelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : str ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__A )
UpperCAmelCase : Tuple = self.default_image_processor
UpperCAmelCase : int = prepare_img()
UpperCAmelCase : Union[str, Any] = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(**__A )
# verify the logits
UpperCAmelCase : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Optional[Any] = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
@require_accelerate
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''', device_map='''auto''' )
UpperCAmelCase : Tuple = prepare_img()
UpperCAmelCase : Optional[int] = image_processor(images=__A, return_tensors='''pt''' )
UpperCAmelCase : Dict = model(**__A )
UpperCAmelCase : Any = outputs.logits
# model predicts one of the 1000 ImageNet classes
UpperCAmelCase : Dict = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx], '''tabby, tabby cat''' )
| 336 | 0 |
'''simple docstring'''
from torch import nn
def __snake_case ( UpperCAmelCase_ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'''Unsupported activation function: {act_fn}''' )
| 55 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_000 , 5_000 )
return (arr, r)
_lowerCamelCase : Any = make_dataset()
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, ...]:
for triplet in permutations(UpperCAmelCase , 3 ):
if sum(UpperCAmelCase ) == target:
return tuple(sorted(UpperCAmelCase ) )
return (0, 0, 0)
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, int, int]:
arr.sort()
UpperCAmelCase : Tuple = len(UpperCAmelCase )
for i in range(n - 1 ):
UpperCAmelCase , UpperCAmelCase : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a__ ( ) -> tuple[float, float]:
UpperCAmelCase : Union[str, Any] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
UpperCAmelCase : Tuple = '''
triplet_sum1(*dataset)
'''
UpperCAmelCase : List[str] = '''
triplet_sum2(*dataset)
'''
UpperCAmelCase : Tuple = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
UpperCAmelCase : str = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
return (min(UpperCAmelCase ), min(UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCamelCase : int = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 336 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a :
def __init__( self : Tuple , lowercase_ : Optional[int] , lowercase_ : str=3 , lowercase_ : Optional[int]=32 , lowercase_ : str=3 , lowercase_ : int=10 , lowercase_ : int=[10, 20, 30, 40] , lowercase_ : Optional[Any]=[1, 1, 2, 1] , lowercase_ : List[str]=True , lowercase_ : Optional[int]=True , lowercase_ : int="relu" , lowercase_ : Dict=3 , lowercase_ : Dict=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = num_channels
snake_case_ = embeddings_size
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = hidden_act
snake_case_ = num_labels
snake_case_ = scope
snake_case_ = len(lowercase_ )
def A_ ( self : Any ):
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.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels
def A_ ( self : Optional[Any] ):
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def A_ ( self : int , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Optional[int] ):
snake_case_ = RegNetModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ )
# 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 A_ ( self : Tuple , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Tuple ):
snake_case_ = self.num_labels
snake_case_ = RegNetForImageClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : str ):
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_torch
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
snake_case_ = (
{"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def A_ ( self : Tuple ):
snake_case_ = RegNetModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ )
def A_ ( self : Optional[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 A_ ( self : List[Any] ):
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def A_ ( self : Any ):
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def A_ ( self : Optional[Any] ):
pass
def A_ ( self : Dict ):
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(lowercase_ )
snake_case_ = inspect.signature(model.forward )
# 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] , lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def A_ ( self : Optional[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(config=lowercase_ )
for name, module in model.named_modules():
if isinstance(lowercase_ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
def A_ ( self : List[str] ):
def check_hidden_states_output(lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[str] ):
snake_case_ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
snake_case_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case_ = self.model_tester.num_stages
self.assertEqual(len(lowercase_ ) , expected_num_stages + 1 )
# RegNet'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 // 2, self.model_tester.image_size // 2] , )
snake_case_ ,snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
snake_case_ = layer_type
snake_case_ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def A_ ( self : List[Any] ):
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = RegNetModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def __magic_name__ ( ) -> Any:
'''simple docstring'''
snake_case_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class a ( unittest.TestCase ):
@cached_property
def A_ ( self : Any ):
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A_ ( self : Tuple ):
snake_case_ = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowercase_ )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=lowercase_ , return_tensors='''pt''' ).to(lowercase_ )
# forward pass
with torch.no_grad():
snake_case_ = model(**lowercase_ )
# verify the logits
snake_case_ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowercase_ )
snake_case_ = torch.tensor([-0.4180, -1.5051, -3.4836] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1e-4 ) )
| 56 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def __magic_name__ ( self : int ):
raise NotImplementedError()
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : str, __A : "AutoTokenizer", __A : bool = False, **__A : str ):
UpperCAmelCase : List[str] = tokenizer
UpperCAmelCase : str = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = True
def __magic_name__ ( self : Dict, __A : Optional[int] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
UpperCAmelCase : Union[str, Any] = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Any = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : Optional[Any] = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def __magic_name__ ( self : str ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
UpperCAmelCase : int = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : List[Any] = []
UpperCAmelCase : List[Any] = 0
else:
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : str = True
self.on_finalized_text(__A, stream_end=__A )
def __magic_name__ ( self : List[str], __A : str, __A : bool = False ):
print(__A, flush=__A, end='''''' if not stream_end else None )
def __magic_name__ ( self : List[Any], __A : Optional[int] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : "AutoTokenizer", __A : bool = False, __A : Optional[float] = None, **__A : str ):
super().__init__(__A, __A, **__A )
UpperCAmelCase : Dict = Queue()
UpperCAmelCase : Any = None
UpperCAmelCase : Any = timeout
def __magic_name__ ( self : Dict, __A : str, __A : bool = False ):
self.text_queue.put(__A, timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout )
def __iter__( self : int ):
return self
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[Any] = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 336 | 0 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : List[str] =CLIPConfig
__UpperCAmelCase : Dict =["""CLIPEncoderLayer"""]
def __init__( self , __a ):
super().__init__(__a )
__lowerCAmelCase = CLIPVisionModelWithProjection(config.vision_config )
__lowerCAmelCase = nn.Linear(config.vision_config.projection_dim , 1 )
__lowerCAmelCase = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def snake_case ( self , __a , __a , __a=0.5 , __a=0.5 ):
__lowerCAmelCase = self.vision_model(__a )[0]
__lowerCAmelCase = self.p_head(__a )
__lowerCAmelCase = nsfw_detected.flatten()
__lowerCAmelCase = nsfw_detected > p_threshold
__lowerCAmelCase = nsfw_detected.tolist()
if any(__a ):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed." )
for idx, nsfw_detected_ in enumerate(__a ):
if nsfw_detected_:
__lowerCAmelCase = np.zeros(images[idx].shape )
__lowerCAmelCase = self.w_head(__a )
__lowerCAmelCase = watermark_detected.flatten()
__lowerCAmelCase = watermark_detected > w_threshold
__lowerCAmelCase = watermark_detected.tolist()
if any(__a ):
logger.warning(
"Potential watermarked content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed." )
for idx, watermark_detected_ in enumerate(__a ):
if watermark_detected_:
__lowerCAmelCase = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 57 |
import numpy
# List of input, output pairs
_lowerCamelCase : Dict = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
_lowerCamelCase : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
_lowerCamelCase : Dict = [2, 4, 1, 5]
_lowerCamelCase : Dict = len(train_data)
_lowerCamelCase : int = 0.0_0_9
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]="train" ) -> Dict:
return calculate_hypothesis_value(UpperCAmelCase , UpperCAmelCase ) - output(
UpperCAmelCase , UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Any:
UpperCAmelCase : str = 0
for i in range(len(UpperCAmelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> Optional[int]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def a__ ( UpperCAmelCase : int , UpperCAmelCase : Optional[Any] ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : str=m ) -> Dict:
UpperCAmelCase : Optional[int] = 0
for i in range(UpperCAmelCase ):
if index == -1:
summation_value += _error(UpperCAmelCase )
else:
summation_value += _error(UpperCAmelCase ) * train_data[i][0][index]
return summation_value
def a__ ( UpperCAmelCase : Dict ) -> Dict:
UpperCAmelCase : Dict = summation_of_cost_derivative(UpperCAmelCase , UpperCAmelCase ) / m
return cost_derivative_value
def a__ ( ) -> List[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
UpperCAmelCase : List[str] = 0.000002
UpperCAmelCase : Any = 0
UpperCAmelCase : Dict = 0
while True:
j += 1
UpperCAmelCase : List[Any] = [0, 0, 0, 0]
for i in range(0 , len(UpperCAmelCase ) ):
UpperCAmelCase : List[str] = get_cost_derivative(i - 1 )
UpperCAmelCase : Tuple = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
UpperCAmelCase , UpperCAmelCase , atol=UpperCAmelCase , rtol=UpperCAmelCase , ):
break
UpperCAmelCase : int = temp_parameter_vector
print(('''Number of iterations:''', j) )
def a__ ( ) -> List[Any]:
for i in range(len(UpperCAmelCase ) ):
print(('''Actual output value:''', output(UpperCAmelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCAmelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 336 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = DebertaVaTokenizer
UpperCamelCase = DebertaVaTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def snake_case_( self ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A , unk_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case_( self , A ) -> Dict:
_SCREAMING_SNAKE_CASE = """this is a test"""
_SCREAMING_SNAKE_CASE = """this is a test"""
return input_text, output_text
def snake_case_( self ) -> Dict:
_SCREAMING_SNAKE_CASE = """<pad>"""
_SCREAMING_SNAKE_CASE = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) , A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) , A )
def snake_case_( self ) -> Any:
_SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """<unk>""" )
self.assertEqual(vocab_keys[-1] , """[PAD]""" )
self.assertEqual(len(A ) , 3_0001 )
def snake_case_( self ) -> Any:
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def snake_case_( self ) -> Any:
# fmt: off
_SCREAMING_SNAKE_CASE = """ \tHeLLo!how \n Are yoU? """
_SCREAMING_SNAKE_CASE = ["""▁hello""", """!""", """how""", """▁are""", """▁you""", """?"""]
# fmt: on
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A , do_lower_case=A )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = DebertaVaTokenizerFast(A , do_lower_case=A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
@unittest.skip("""There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.""" )
def snake_case_( self ) -> List[Any]:
pass
@unittest.skip("""There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.""" )
def snake_case_( self ) -> Optional[int]:
pass
def snake_case_( self ) -> Optional[Any]:
# fmt: off
_SCREAMING_SNAKE_CASE = """I was born in 92000, and this is falsé."""
_SCREAMING_SNAKE_CASE = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = DebertaVaTokenizerFast(A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
def snake_case_( self ) -> List[str]:
# fmt: off
_SCREAMING_SNAKE_CASE = """I was born in 92000, and this is falsé."""
_SCREAMING_SNAKE_CASE = ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A , do_lower_case=A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = DebertaVaTokenizerFast(A , do_lower_case=A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
def snake_case_( self ) -> str:
# fmt: off
_SCREAMING_SNAKE_CASE = """I was born in 92000, and this is falsé."""
_SCREAMING_SNAKE_CASE = ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """.""", ]
# fmt: on
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A , do_lower_case=A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = DebertaVaTokenizerFast(A , do_lower_case=A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
def snake_case_( self ) -> Union[str, Any]:
# fmt: off
_SCREAMING_SNAKE_CASE = """I was born in 92000, and this is falsé."""
_SCREAMING_SNAKE_CASE = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
# fmt: on
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A , do_lower_case=A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = DebertaVaTokenizerFast(A , do_lower_case=A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
def snake_case_( self ) -> List[str]:
# fmt: off
_SCREAMING_SNAKE_CASE = """ \tHeLLo!how \n Are yoU? """
_SCREAMING_SNAKE_CASE = ["""▁""", """<unk>""", """e""", """<unk>""", """o""", """!""", """how""", """▁""", """<unk>""", """re""", """▁yo""", """<unk>""", """?"""]
# fmt: on
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A , do_lower_case=A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = DebertaVaTokenizerFast(A , do_lower_case=A , split_by_punct=A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
def snake_case_( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """I was born in 92000, and this is falsé."""
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(tokenizer.encode(A , add_special_tokens=A ) )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A , add_special_tokens=A ) )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A , add_special_tokens=A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A , add_special_tokens=A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A )
self.assertListEqual(A , A )
def snake_case_( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = """This is a test"""
_SCREAMING_SNAKE_CASE = [13, 1, 4398, 25, 21, 1289]
_SCREAMING_SNAKE_CASE = ["""▁""", """T""", """his""", """▁is""", """▁a""", """▁test"""]
_SCREAMING_SNAKE_CASE = ["""▁""", """<unk>""", """his""", """▁is""", """▁a""", """▁test"""]
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A , keep_accents=A )
_SCREAMING_SNAKE_CASE = DebertaVaTokenizerFast(A , keep_accents=A )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A , add_special_tokens=A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A , add_special_tokens=A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(A , A )
# fmt: off
_SCREAMING_SNAKE_CASE = """I was born in 92000, and this is falsé."""
_SCREAMING_SNAKE_CASE = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
_SCREAMING_SNAKE_CASE = ["""▁""", """I""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """.""", ]
_SCREAMING_SNAKE_CASE = ["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """.""", ]
# fmt: on
_SCREAMING_SNAKE_CASE = tokenizer.encode(A , add_special_tokens=A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A , add_special_tokens=A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A )
self.assertListEqual(A , A )
_SCREAMING_SNAKE_CASE = rust_tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(A , A )
def snake_case_( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = DebertaVaTokenizer(A )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A , A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , A , )
@slow
def snake_case_( self ) -> List[str]:
# fmt: off
_SCREAMING_SNAKE_CASE = {"""input_ids""": [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=A , model_name="""microsoft/deberta-v2-xlarge""" , revision="""ad6e42c1532ddf3a15c39246b63f5559d558b670""" , )
| 58 |
def a__ ( UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] ) -> Optional[Any]:
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[Any] = len(UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
UpperCAmelCase : Optional[Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
UpperCAmelCase : Any = left
UpperCAmelCase : List[str] = point
elif point > right:
UpperCAmelCase : Any = right
UpperCAmelCase : List[str] = point
else:
if item < current_item:
UpperCAmelCase : Optional[int] = point - 1
else:
UpperCAmelCase : str = point + 1
return None
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : List[str] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , point + 1 , UpperCAmelCase )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> int:
if collection != sorted(UpperCAmelCase ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
_lowerCamelCase : Optional[int] = 0
if debug == 1:
_lowerCamelCase : Dict = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
_lowerCamelCase : List[Any] = 6_7
_lowerCamelCase : Optional[Any] = interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print("Not found")
| 336 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 59 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Any = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : List[str]=False ) -> Any:
UpperCAmelCase : Optional[int] = '''backbone.''' if is_semantic else ''''''
UpperCAmelCase : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
(f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
(f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
(f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
(f'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : str=False , UpperCAmelCase : Dict=False ) -> Any:
for i in range(config.num_hidden_layers ):
UpperCAmelCase : Tuple = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias''' )
UpperCAmelCase : List[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias''' )
UpperCAmelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase : str = q_bias
UpperCAmelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
UpperCAmelCase : int = state_dict.pop(f'''{prefix}blocks.{i}.gamma_1''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.gamma_2''' )
UpperCAmelCase : str = gamma_a
UpperCAmelCase : Dict = gamma_a
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : str = val
def a__ ( ) -> Optional[int]:
UpperCAmelCase : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase : Union[str, Any] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any]=False ) -> Union[str, Any]:
UpperCAmelCase : Dict = False if '''rvlcdip''' in checkpoint_url else True
UpperCAmelCase : Any = BeitConfig(use_absolute_position_embeddings=UpperCAmelCase , use_mask_token=UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
UpperCAmelCase : List[Any] = 1_024
UpperCAmelCase : Optional[Any] = 4_096
UpperCAmelCase : Any = 24
UpperCAmelCase : Union[str, Any] = 16
# labels
if "rvlcdip" in checkpoint_url:
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : List[Any] = '''huggingface/label-files'''
UpperCAmelCase : Any = '''rvlcdip-id2label.json'''
UpperCAmelCase : List[str] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Dict = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : Union[str, Any] = idalabel
UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )['''model''']
UpperCAmelCase : List[str] = create_rename_keys(UpperCAmelCase , has_lm_head=UpperCAmelCase )
for src, dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
read_in_q_k_v(UpperCAmelCase , UpperCAmelCase , has_lm_head=UpperCAmelCase )
# load HuggingFace model
UpperCAmelCase : Tuple = BeitForMaskedImageModeling(UpperCAmelCase ) if has_lm_head else BeitForImageClassification(UpperCAmelCase )
model.eval()
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image
UpperCAmelCase : Dict = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCAmelCase )
UpperCAmelCase : List[str] = prepare_img()
UpperCAmelCase : Optional[Any] = image_processor(images=UpperCAmelCase , return_tensors='''pt''' )
UpperCAmelCase : str = encoding['''pixel_values''']
UpperCAmelCase : Any = model(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify logits
UpperCAmelCase : List[Any] = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(UpperCAmelCase ), "Shape of logits not as expected"
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
if has_lm_head:
UpperCAmelCase : List[Any] = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
UpperCAmelCase : Any = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=UpperCAmelCase , )
if __name__ == "__main__":
_lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 336 | 0 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _snake_case ( _snake_case : Iterable[str] , _snake_case : int ):
lowerCAmelCase : Optional[int] = iter(_snake_case )
while True:
lowerCAmelCase : Tuple = tuple(itertools.islice(_snake_case , _snake_case ) )
if not chunk:
return
yield chunk
def _snake_case ( _snake_case : str ):
lowerCAmelCase : List[Any] = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
lowerCAmelCase : Union[str, Any] = ''''''
if len(_snake_case ) < 2:
return dirty
for i in range(len(_snake_case ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_snake_case ) & 1:
clean += "X"
return clean
def _snake_case ( _snake_case : str ):
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
lowerCAmelCase : Dict = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
lowerCAmelCase : Any = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_snake_case )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_snake_case )
return table
def _snake_case ( _snake_case : str , _snake_case : str ):
lowerCAmelCase : Union[str, Any] = generate_table(_snake_case )
lowerCAmelCase : str = prepare_input(_snake_case )
lowerCAmelCase : Any = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_snake_case , 2 ):
lowerCAmelCase, lowerCAmelCase : List[Any] = divmod(table.index(_snake_case ) , 5 )
lowerCAmelCase, lowerCAmelCase : List[str] = divmod(table.index(_snake_case ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def _snake_case ( _snake_case : str , _snake_case : str ):
lowerCAmelCase : Dict = generate_table(_snake_case )
lowerCAmelCase : Union[str, Any] = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_snake_case , 2 ):
lowerCAmelCase, lowerCAmelCase : List[str] = divmod(table.index(_snake_case ) , 5 )
lowerCAmelCase, lowerCAmelCase : Union[str, Any] = divmod(table.index(_snake_case ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 60 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[int], __A : Optional[int], __A : Any=1_3, __A : str=7, __A : Optional[int]=True, __A : Tuple=True, __A : Union[str, Any]=True, __A : Any=True, __A : Optional[int]=9_9, __A : Tuple=3_2, __A : str=5, __A : Union[str, Any]=4, __A : List[str]=3_7, __A : Tuple="gelu", __A : Optional[int]=0.1, __A : int=0.1, __A : Optional[Any]=5_1_2, __A : int=1_6, __A : Optional[Any]=2, __A : Union[str, Any]=0.0_2, __A : Optional[int]=4, ):
UpperCAmelCase : Any = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Any = seq_length
UpperCAmelCase : Tuple = is_training
UpperCAmelCase : str = use_attention_mask
UpperCAmelCase : List[str] = use_token_type_ids
UpperCAmelCase : int = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Any = num_choices
def __magic_name__ ( self : str ):
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase : List[Any] = None
if self.use_attention_mask:
UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase : Union[str, Any] = RobertaConfig(
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, )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self : int ):
UpperCAmelCase : Any = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = config_and_inputs
UpperCAmelCase : Any = True
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = FlaxRobertaModelTester(self )
@slow
def __magic_name__ ( self : Any ):
for model_class_name in self.all_model_classes:
UpperCAmelCase : Dict = model_class_name.from_pretrained('''roberta-base''', from_pt=__A )
UpperCAmelCase : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
| 336 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class A_ :
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = XGLMConfig
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
def __init__( self , lowercase_ , lowercase_=14 , lowercase_=7 , lowercase_=True , lowercase_=True , lowercase_=True , lowercase_=99 , lowercase_=32 , lowercase_=2 , lowercase_=4 , lowercase_=37 , lowercase_="gelu" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=512 , lowercase_=0.02 , ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = parent
UpperCAmelCase_ : Tuple = batch_size
UpperCAmelCase_ : Optional[int] = seq_length
UpperCAmelCase_ : Union[str, Any] = is_training
UpperCAmelCase_ : List[Any] = use_input_mask
UpperCAmelCase_ : Tuple = use_labels
UpperCAmelCase_ : List[Any] = vocab_size
UpperCAmelCase_ : Union[str, Any] = d_model
UpperCAmelCase_ : Optional[int] = num_hidden_layers
UpperCAmelCase_ : List[Any] = num_attention_heads
UpperCAmelCase_ : List[Any] = ffn_dim
UpperCAmelCase_ : int = activation_function
UpperCAmelCase_ : List[str] = activation_dropout
UpperCAmelCase_ : List[Any] = attention_dropout
UpperCAmelCase_ : List[str] = max_position_embeddings
UpperCAmelCase_ : Optional[Any] = initializer_range
UpperCAmelCase_ : Optional[Any] = None
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[str] = 2
UpperCAmelCase_ : Tuple = 1
def UpperCamelCase__ ( self ):
"""simple docstring"""
return XGLMConfig.from_pretrained("facebook/xglm-564M" )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
UpperCAmelCase_ : Optional[int] = None
if self.use_input_mask:
UpperCAmelCase_ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase_ : Union[str, Any] = self.get_config()
UpperCAmelCase_ : List[Any] = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def UpperCamelCase__ ( self ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=lowercase_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=lowercase_ , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) : Any = config_and_inputs
UpperCAmelCase_ : List[str] = {
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_tf
class A_ (lowercase__ ,lowercase__ ,unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
SCREAMING_SNAKE_CASE__ : Tuple = (TFXGLMForCausalLM,) if is_tf_available() else ()
SCREAMING_SNAKE_CASE__ : List[str] = (
{"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
)
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = TFXGLMModelTester(self )
UpperCAmelCase_ : Dict = ConfigTester(self , config_class=lowercase_ , n_embd=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ : Union[str, Any] = TFXGLMModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor." )
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class A_ (unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase__ ( self , lowercase_=True ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
UpperCAmelCase_ : Union[str, Any] = tf.convert_to_tensor([[2, 268, 9865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
UpperCAmelCase_ : Tuple = [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
# fmt: on
UpperCAmelCase_ : List[str] = model.generate(lowercase_ , do_sample=lowercase_ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , lowercase_ )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Any = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
UpperCAmelCase_ : Any = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
tf.random.set_seed(0 )
UpperCAmelCase_ : Optional[Any] = tokenizer("Today is a nice day and" , return_tensors="tf" )
UpperCAmelCase_ : Optional[Any] = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0" ):
UpperCAmelCase_ : List[Any] = model.generate(lowercase_ , do_sample=lowercase_ , seed=[7, 0] )
UpperCAmelCase_ : Dict = tokenizer.decode(output_ids[0] , skip_special_tokens=lowercase_ )
UpperCAmelCase_ : Dict = (
"Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
)
self.assertEqual(lowercase_ , lowercase_ )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Any = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
UpperCAmelCase_ : Tuple = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
UpperCAmelCase_ : Any = "left"
# use different length sentences to test batching
UpperCAmelCase_ : List[str] = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
UpperCAmelCase_ : List[Any] = tokenizer(lowercase_ , return_tensors="tf" , padding=lowercase_ )
UpperCAmelCase_ : Optional[int] = inputs["input_ids"]
UpperCAmelCase_ : Union[str, Any] = model.generate(input_ids=lowercase_ , attention_mask=inputs["attention_mask"] , max_new_tokens=12 )
UpperCAmelCase_ : Optional[int] = tokenizer(sentences[0] , return_tensors="tf" ).input_ids
UpperCAmelCase_ : Optional[Any] = model.generate(input_ids=lowercase_ , max_new_tokens=12 )
UpperCAmelCase_ : List[str] = tokenizer(sentences[1] , return_tensors="tf" ).input_ids
UpperCAmelCase_ : List[Any] = model.generate(input_ids=lowercase_ , max_new_tokens=12 )
UpperCAmelCase_ : Optional[int] = tokenizer.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )
UpperCAmelCase_ : List[Any] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowercase_ )
UpperCAmelCase_ : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=lowercase_ )
UpperCAmelCase_ : Tuple = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(lowercase_ , [non_padded_sentence, padded_sentence] )
| 61 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"vocab_file": "vocab.txt"}
_lowerCamelCase : List[str] = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase : List[Any] = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def a__ ( UpperCAmelCase : List[str] ) -> Any:
with open(UpperCAmelCase , '''r''' ) as f:
UpperCAmelCase : Dict = f.read().splitlines()
return [l.strip() for l in lines]
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, __A : Dict, __A : List[Any]="<unk>", __A : List[str]="<cls>", __A : Any="<pad>", __A : Union[str, Any]="<mask>", __A : int="<eos>", **__A : Tuple, ):
super().__init__(**__A )
UpperCAmelCase : Tuple = load_vocab_file(__A )
UpperCAmelCase : List[Any] = dict(enumerate(self.all_tokens ) )
UpperCAmelCase : str = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase : Union[str, Any] = unk_token
UpperCAmelCase : Optional[Any] = cls_token
UpperCAmelCase : Optional[int] = pad_token
UpperCAmelCase : Optional[int] = mask_token
UpperCAmelCase : List[str] = eos_token
UpperCAmelCase : Optional[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def __magic_name__ ( self : Tuple, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : List[Any], __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : Optional[Any], **__A : Union[str, Any] ):
return text.split()
def __magic_name__ ( self : Optional[int], __A : Dict=False ):
return len(self._id_to_token )
def __magic_name__ ( self : int ):
return {token: i for i, token in enumerate(self.all_tokens )}
def __magic_name__ ( self : Tuple, __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : Union[str, Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Optional[int] = [self.cls_token_id]
UpperCAmelCase : Optional[int] = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def __magic_name__ ( self : Any, __A : List, __A : Optional[List] = None, __A : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase : Dict = [1] + ([0] * len(__A )) + [1]
if token_ids_a is not None:
mask += [0] * len(__A ) + [1]
return mask
def __magic_name__ ( self : Optional[int], __A : List[Any], __A : Dict ):
UpperCAmelCase : Union[str, Any] = os.path.join(__A, (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(__A, '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def __magic_name__ ( self : Dict ):
return self.get_vocab_size(with_added_tokens=__A )
def __magic_name__ ( self : Optional[int], __A : Union[List[str], List[AddedToken]], __A : bool = False ):
return super()._add_tokens(__A, special_tokens=__A )
| 336 | 0 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class UpperCAmelCase__ ( A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = PriorTransformer
UpperCAmelCase__ : List[str] = "hidden_states"
@property
def _a ( self ) -> int:
__UpperCamelCase =4
__UpperCamelCase =8
__UpperCamelCase =7
__UpperCamelCase =floats_tensor((batch_size, embedding_dim) ).to(A_ )
__UpperCamelCase =floats_tensor((batch_size, embedding_dim) ).to(A_ )
__UpperCamelCase =floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(A_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def _a ( self , A_=0 ) -> Dict:
torch.manual_seed(A_ )
__UpperCamelCase =4
__UpperCamelCase =8
__UpperCamelCase =7
__UpperCamelCase =torch.randn((batch_size, embedding_dim) ).to(A_ )
__UpperCamelCase =torch.randn((batch_size, embedding_dim) ).to(A_ )
__UpperCamelCase =torch.randn((batch_size, num_embeddings, embedding_dim) ).to(A_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def _a ( self ) -> Tuple:
return (4, 8)
@property
def _a ( self ) -> List[Any]:
return (4, 8)
def _a ( self ) -> str:
__UpperCamelCase ={
'num_attention_heads': 2,
'attention_head_dim': 4,
'num_layers': 2,
'embedding_dim': 8,
'num_embeddings': 7,
'additional_embeddings': 4,
}
__UpperCamelCase =self.dummy_input
return init_dict, inputs_dict
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase , __UpperCamelCase =PriorTransformer.from_pretrained(
'hf-internal-testing/prior-dummy' , output_loading_info=A_ )
self.assertIsNotNone(A_ )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(A_ )
__UpperCamelCase =model(**self.dummy_input )[0]
assert hidden_states is not None, "Make sure output is not None"
def _a ( self ) -> Any:
__UpperCamelCase , __UpperCamelCase =self.prepare_init_args_and_inputs_for_common()
__UpperCamelCase =self.model_class(**A_ )
__UpperCamelCase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCamelCase =[*signature.parameters.keys()]
__UpperCamelCase =['hidden_states', 'timestep']
self.assertListEqual(arg_names[:2] , A_ )
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase =PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy' )
__UpperCamelCase =model.to(A_ )
if hasattr(A_ , 'set_default_attn_processor' ):
model.set_default_attn_processor()
__UpperCamelCase =self.get_dummy_seed_input()
with torch.no_grad():
__UpperCamelCase =model(**A_ )[0]
__UpperCamelCase =output[0, :5].flatten().cpu()
print(A_ )
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
__UpperCamelCase =torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
self.assertTrue(torch_all_close(A_ , A_ , rtol=1E-2 ) )
@slow
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self , A_=1 , A_=768 , A_=77 , A_=0 ) -> Union[str, Any]:
torch.manual_seed(A_ )
__UpperCamelCase =batch_size
__UpperCamelCase =embedding_dim
__UpperCamelCase =num_embeddings
__UpperCamelCase =torch.randn((batch_size, embedding_dim) ).to(A_ )
__UpperCamelCase =torch.randn((batch_size, embedding_dim) ).to(A_ )
__UpperCamelCase =torch.randn((batch_size, num_embeddings, embedding_dim) ).to(A_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def _a ( self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[13, [-0.5861, 0.1283, -0.0931, 0.0882, 0.4476, 0.1329, -0.0498, 0.0640]],
[37, [-0.4913, 0.0110, -0.0483, 0.0541, 0.4954, -0.0170, 0.0354, 0.1651]],
# fmt: on
] )
def _a ( self , A_ , A_ ) -> int:
__UpperCamelCase =PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior' , subfolder='prior' )
model.to(A_ )
__UpperCamelCase =self.get_dummy_seed_input(seed=A_ )
with torch.no_grad():
__UpperCamelCase =model(**A_ )[0]
assert list(sample.shape ) == [1, 768]
__UpperCamelCase =sample[0, :8].flatten().cpu()
print(A_ )
__UpperCamelCase =torch.tensor(A_ )
assert torch_all_close(A_ , A_ , atol=1E-3 )
| 62 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__A, '''tf_padding''' ) )
self.parent.assertTrue(hasattr(__A, '''depth_multiplier''' ) )
class __UpperCAmelCase :
def __init__( self : int, __A : List[Any], __A : str=1_3, __A : Dict=3, __A : int=3_2, __A : int=0.2_5, __A : List[str]=8, __A : int=8, __A : Dict=6, __A : str=3_2, __A : Any=True, __A : str=True, __A : int=True, __A : Union[str, Any]="relu6", __A : Any=1_2_8_0, __A : List[Any]=0.1, __A : Optional[Any]=0.0_2, __A : Tuple=True, __A : List[Any]=True, __A : str=1_0, __A : Optional[Any]=None, ):
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : List[str] = batch_size
UpperCAmelCase : List[str] = num_channels
UpperCAmelCase : str = image_size
UpperCAmelCase : Optional[int] = depth_multiplier
UpperCAmelCase : Union[str, Any] = depth_divisible_by
UpperCAmelCase : Optional[Any] = min_depth
UpperCAmelCase : List[str] = expand_ratio
UpperCAmelCase : Dict = tf_padding
UpperCAmelCase : str = output_stride
UpperCAmelCase : Union[str, Any] = first_layer_is_expansion
UpperCAmelCase : List[Any] = finegrained_output
UpperCAmelCase : Optional[Any] = hidden_act
UpperCAmelCase : str = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
UpperCAmelCase : Optional[Any] = classifier_dropout_prob
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : List[str] = is_training
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : Any = scope
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Dict = None
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Dict = ids_tensor([self.batch_size], self.num_labels )
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
UpperCAmelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : Any ):
return MobileNetVaConfig(
num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, depth_divisible_by=self.depth_divisible_by, min_depth=self.min_depth, expand_ratio=self.expand_ratio, output_stride=self.output_stride, first_layer_is_expansion=self.first_layer_is_expansion, finegrained_output=self.finegrained_output, hidden_act=self.hidden_act, tf_padding=self.tf_padding, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def __magic_name__ ( self : List[Any], __A : Dict, __A : Optional[Any], __A : Optional[int], __A : Union[str, Any] ):
UpperCAmelCase : Any = MobileNetVaModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[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,
), )
self.parent.assertEqual(
result.pooler_output.shape, (self.batch_size, self.last_hidden_size), )
def __magic_name__ ( self : str, __A : Union[str, Any], __A : Dict, __A : Optional[Any], __A : str ):
UpperCAmelCase : Optional[int] = self.num_labels
UpperCAmelCase : Any = MobileNetVaForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[int] = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any], __A : Optional[Any], __A : List[str], __A : Dict, __A : Dict ):
UpperCAmelCase : Tuple = self.num_labels
UpperCAmelCase : Dict = MobileNetVaForSemanticSegmentation(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = 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,
), )
UpperCAmelCase : Optional[Any] = 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 __magic_name__ ( self : Tuple ):
UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = config_and_inputs
UpperCAmelCase : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
"""feature-extraction""": MobileNetVaModel,
"""image-classification""": MobileNetVaForImageClassification,
"""image-segmentation""": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = MobileNetVaModelTester(self )
UpperCAmelCase : List[Any] = MobileNetVaConfigTester(self, config_class=__A, has_text_modality=__A )
def __magic_name__ ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''' )
def __magic_name__ ( self : Tuple ):
pass
@unittest.skip(reason='''MobileNetV2 does not output attentions''' )
def __magic_name__ ( self : Any ):
pass
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(__A )
UpperCAmelCase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : int ):
def check_hidden_states_output(__A : Any, __A : Optional[Any], __A : str ):
UpperCAmelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
UpperCAmelCase : Dict = model(**self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Optional[Any] = outputs.hidden_states
UpperCAmelCase : List[Any] = 1_6
self.assertEqual(len(__A ), __A )
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : int ):
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__A )
@slow
def __magic_name__ ( self : Dict ):
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] = MobileNetVaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> int:
UpperCAmelCase : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[Any] ):
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''' ) if is_vision_available() else None
)
@slow
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : List[Any] = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''' ).to(__A )
UpperCAmelCase : Optional[int] = self.default_image_processor
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : Dict = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : str = model(**__A )
# verify the logits
UpperCAmelCase : int = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Tuple = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = model.to(__A )
UpperCAmelCase : Tuple = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = prepare_img()
UpperCAmelCase : int = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Union[str, Any] = model(**__A )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify the logits
UpperCAmelCase : Tuple = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor(
[
[[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
[[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
[[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
], device=__A, )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], __A, atol=1E-4 ) )
| 336 | 0 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a =(EulerDiscreteScheduler,)
__a =10
def UpperCamelCase__ ( self : List[Any] , **__a : Union[str, Any] ):
_a = {
"num_train_timesteps": 11_00,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**__a )
return config
def UpperCamelCase__ ( self : int ):
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a )
def UpperCamelCase__ ( self : str ):
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__a , beta_end=__a )
def UpperCamelCase__ ( self : int ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a )
def UpperCamelCase__ ( self : List[Any] ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def UpperCamelCase__ ( self : Any ):
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
_a = torch.manual_seed(0 )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma
_a = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
_a = scheduler.scale_model_input(__a , __a )
_a = model(__a , __a )
_a = scheduler.step(__a , __a , __a , generator=__a )
_a = output.prev_sample
_a = torch.sum(torch.abs(__a ) )
_a = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def UpperCamelCase__ ( self : Any ):
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config(prediction_type="v_prediction" )
_a = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
_a = torch.manual_seed(0 )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma
_a = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
_a = scheduler.scale_model_input(__a , __a )
_a = model(__a , __a )
_a = scheduler.step(__a , __a , __a , generator=__a )
_a = output.prev_sample
_a = torch.sum(torch.abs(__a ) )
_a = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.2_6_7_6e-0_6 ) < 1e-3
def UpperCamelCase__ ( self : List[str] ):
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
_a = torch.manual_seed(0 )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_a = sample.to(__a )
for t in scheduler.timesteps:
_a = scheduler.scale_model_input(__a , __a )
_a = model(__a , __a )
_a = scheduler.step(__a , __a , __a , generator=__a )
_a = output.prev_sample
_a = torch.sum(torch.abs(__a ) )
_a = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def UpperCamelCase__ ( self : Optional[Any] ):
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**__a , use_karras_sigmas=__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
_a = torch.manual_seed(0 )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_a = sample.to(__a )
for t in scheduler.timesteps:
_a = scheduler.scale_model_input(__a , __a )
_a = model(__a , __a )
_a = scheduler.step(__a , __a , __a , generator=__a )
_a = output.prev_sample
_a = torch.sum(torch.abs(__a ) )
_a = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 124.52299499511719 ) < 1e-2
assert abs(result_mean.item() - 0.16213932633399963 ) < 1e-3
| 63 |
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 : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """codegen"""
UpperCamelCase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any, __A : Optional[int]=5_0_4_0_0, __A : Tuple=2_0_4_8, __A : Optional[int]=2_0_4_8, __A : List[str]=4_0_9_6, __A : List[str]=2_8, __A : Union[str, Any]=1_6, __A : Tuple=6_4, __A : Union[str, Any]=None, __A : Union[str, Any]="gelu_new", __A : Any=0.0, __A : Dict=0.0, __A : str=0.0, __A : Optional[int]=1E-5, __A : Any=0.0_2, __A : Any=True, __A : Union[str, Any]=5_0_2_5_6, __A : List[str]=5_0_2_5_6, __A : int=False, **__A : List[Any], ):
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Tuple = n_ctx
UpperCAmelCase : Tuple = n_positions
UpperCAmelCase : Optional[int] = n_embd
UpperCAmelCase : Union[str, Any] = n_layer
UpperCAmelCase : List[str] = n_head
UpperCAmelCase : Tuple = n_inner
UpperCAmelCase : int = rotary_dim
UpperCAmelCase : List[Any] = activation_function
UpperCAmelCase : List[str] = resid_pdrop
UpperCAmelCase : Optional[Any] = embd_pdrop
UpperCAmelCase : str = attn_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : Any = bos_token_id
UpperCAmelCase : List[str] = eos_token_id
super().__init__(
bos_token_id=__A, eos_token_id=__A, tie_word_embeddings=__A, **__A )
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Any, __A : PretrainedConfig, __A : str = "default", __A : List[PatchingSpec] = None, __A : bool = False, ):
super().__init__(__A, task=__A, patching_specs=__A, use_past=__A )
if not getattr(self._config, '''pad_token_id''', __A ):
# TODO: how to do that better?
UpperCAmelCase : Union[str, Any] = 0
@property
def __magic_name__ ( self : str ):
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__A, direction='''inputs''' )
UpperCAmelCase : int = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase : List[Any] = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __magic_name__ ( self : Dict ):
return self._config.n_layer
@property
def __magic_name__ ( self : List[str] ):
return self._config.n_head
def __magic_name__ ( self : str, __A : PreTrainedTokenizer, __A : int = -1, __A : int = -1, __A : bool = False, __A : Optional[TensorType] = None, ):
UpperCAmelCase : Union[str, Any] = super(__A, self ).generate_dummy_inputs(
__A, batch_size=__A, seq_length=__A, is_pair=__A, framework=__A )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase : Union[str, 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
UpperCAmelCase , UpperCAmelCase : str = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase : str = seqlen + 2
UpperCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase : Optional[int] = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
UpperCAmelCase : Union[str, Any] = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase : Optional[Any] = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase : Dict = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__A, __A, dtype=__A )], dim=1 )
return ordered_inputs
@property
def __magic_name__ ( self : Tuple ):
return 1_3
| 336 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 64 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 336 | 0 |
from typing import List
import numpy as np
def lowerCAmelCase_ ( __A ) -> int:
'''simple docstring'''
UpperCAmelCase__ = {key: len(__A ) for key, value in gen_kwargs.items() if isinstance(__A, __A )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
"Sharding is ambiguous for this dataset: "
+ "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
+ "\n".join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items() )
+ "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
+ "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
) )
UpperCAmelCase__ = max(lists_lengths.values(), default=0 )
return max(1, __A )
def lowerCAmelCase_ ( __A, __A ) -> List[range]:
'''simple docstring'''
UpperCAmelCase__ = []
for group_idx in range(__A ):
UpperCAmelCase__ = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
UpperCAmelCase__ = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
UpperCAmelCase__ = range(__A, start + num_shards_to_add )
shards_indices_per_group.append(__A )
return shards_indices_per_group
def lowerCAmelCase_ ( __A, __A ) -> List[dict]:
'''simple docstring'''
UpperCAmelCase__ = _number_of_shards_in_gen_kwargs(__A )
if num_shards == 1:
return [dict(__A )]
else:
UpperCAmelCase__ = _distribute_shards(num_shards=__A, max_num_jobs=__A )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(__A, __A )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(__A ) )
]
def lowerCAmelCase_ ( __A ) -> dict:
'''simple docstring'''
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key], __A )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def lowerCAmelCase_ ( __A, __A ) -> dict:
'''simple docstring'''
UpperCAmelCase__ = {len(__A ) for value in gen_kwargs.values() if isinstance(__A, __A )}
UpperCAmelCase__ = {}
for size in list_sizes:
UpperCAmelCase__ = list(range(__A ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
UpperCAmelCase__ = dict(__A )
for key, value in shuffled_kwargs.items():
if isinstance(__A, __A ):
UpperCAmelCase__ = [value[i] for i in indices_per_size[len(__A )]]
return shuffled_kwargs
| 65 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCAmelCase :
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
@classmethod
def __magic_name__ ( cls : Any ):
return cls()
@dataclass
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@property
def __magic_name__ ( self : Optional[int] ):
return True
@register_to_config
def __init__( self : Optional[int], __A : float = 0.0_2, __A : float = 1_0_0, __A : float = 1.0_0_7, __A : float = 8_0, __A : float = 0.0_5, __A : float = 5_0, ):
pass
def __magic_name__ ( self : Optional[Any] ):
return KarrasVeSchedulerState.create()
def __magic_name__ ( self : int, __A : KarrasVeSchedulerState, __A : int, __A : Tuple = () ):
UpperCAmelCase : Optional[Any] = jnp.arange(0, __A )[::-1].copy()
UpperCAmelCase : Union[str, Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A, schedule=jnp.array(__A, dtype=jnp.floataa ), timesteps=__A, )
def __magic_name__ ( self : List[Any], __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : random.KeyArray, ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase : int = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1 )
else:
UpperCAmelCase : Optional[int] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase : Union[str, Any] = random.split(__A, num=1 )
UpperCAmelCase : List[str] = self.config.s_noise * random.normal(key=__A, shape=sample.shape )
UpperCAmelCase : Tuple = sigma + gamma * sigma
UpperCAmelCase : List[str] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : int = sample_hat + sigma_hat * model_output
UpperCAmelCase : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : jnp.ndarray, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
UpperCAmelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Optional[Any], __A : KarrasVeSchedulerState, __A : Optional[int], __A : int, __A : Union[str, Any] ):
raise NotImplementedError()
| 336 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Optional[int] = torch.load(_lowercase, map_location="""cpu""" )
if "model" in sd.keys():
snake_case_ :str = torch.load(_lowercase, map_location="""cpu""" )["""model"""]
# pop unnecessary weights
snake_case_ :Tuple = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
snake_case_ :str = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
snake_case_ :List[Any] = sd.pop(_lowercase )
snake_case_ :Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
snake_case_ :Any = sd[key]
# We split QKV in separate Q,K,V
snake_case_ :Dict = key.replace(""".qkv_proj.""", """.q_proj.""" )
snake_case_ :Optional[Any] = key.replace(""".qkv_proj.""", """.k_proj.""" )
snake_case_ :Optional[Any] = key.replace(""".qkv_proj.""", """.v_proj.""" )
snake_case_ :Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
snake_case_, snake_case_, snake_case_ :Any = torch.split(_lowercase, depth // 3, dim=0 )
snake_case_ :List[Any] = q
snake_case_ :Union[str, Any] = k
snake_case_ :Optional[int] = v
del sd[key]
return sd
@torch.no_grad()
def A_ ( _lowercase, _lowercase, _lowercase=None ):
'''simple docstring'''
snake_case_ :Optional[int] = load_checkpoint(_lowercase )
if config is not None:
snake_case_ :List[str] = OPTConfig.from_pretrained(_lowercase )
else:
snake_case_ :List[Any] = OPTConfig()
snake_case_ :str = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
__a = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 66 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCAmelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def a__ ( ) -> Dict:
if os.name == "nt":
UpperCAmelCase : List[str] = CursorInfo()
UpperCAmelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def a__ ( ) -> Optional[int]:
if os.name == "nt":
UpperCAmelCase : int = CursorInfo()
UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Any = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def a__ ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 336 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( ) -> int:
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(UpperCamelCase__ , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f'{solution() = }')
| 67 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase__ = 1_6
lowerCAmelCase__ = 3_2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Accelerator , SCREAMING_SNAKE_CASE_: int = 1_6 ) -> Tuple:
'''simple docstring'''
A__ = AutoTokenizer.from_pretrained("bert-base-cased" )
A__ = load_dataset("glue" , "mrpc" )
def tokenize_function(SCREAMING_SNAKE_CASE_: Dict ):
# max_length=None => use the model max length (it's actually the default)
A__ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A__ = datasets.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A__ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(SCREAMING_SNAKE_CASE_: Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A__ = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A__ = 1_6
elif accelerator.mixed_precision != "no":
A__ = 8
else:
A__ = None
return tokenizer.pad(
SCREAMING_SNAKE_CASE_ , padding="longest" , max_length=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_tensors="pt" , )
# Instantiate dataloaders.
A__ = DataLoader(
tokenized_datasets["train"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
A__ = DataLoader(
tokenized_datasets["validation"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase__ = mocked_dataloaders # noqa: F811
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: Tuple ) -> str:
'''simple docstring'''
if os.environ.get("TESTING_MOCKED_DATALOADERS" , SCREAMING_SNAKE_CASE_ ) == "1":
A__ = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
A__ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
A__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A__ = config["lr"]
A__ = int(config["num_epochs"] )
A__ = int(config["seed"] )
A__ = int(config["batch_size"] )
set_seed(SCREAMING_SNAKE_CASE_ )
A__ , A__ = get_dataloaders(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A__ = evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
A__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A__ = batch_size // MAX_GPU_BATCH_SIZE
A__ = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A__ = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=SCREAMING_SNAKE_CASE_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A__ = model.to(accelerator.device )
# Instantiate optimizer
A__ = AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE_ )
# Instantiate scheduler
A__ = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE_ , num_warmup_steps=1_0_0 , num_training_steps=(len(SCREAMING_SNAKE_CASE_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A__ , A__ , A__ , A__ , A__ = accelerator.prepare(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
A__ = os.path.split(SCREAMING_SNAKE_CASE_ )[-1].split("." )[0]
accelerator.init_trackers(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE_ ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
A__ = 0
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A__ = model(**SCREAMING_SNAKE_CASE_ )
A__ = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
A__ = loss / gradient_accumulation_steps
accelerator.backward(SCREAMING_SNAKE_CASE_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
A__ = model(**SCREAMING_SNAKE_CASE_ )
A__ = outputs.logits.argmax(dim=-1 )
A__ , A__ = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ , )
A__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , SCREAMING_SNAKE_CASE_ )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(SCREAMING_SNAKE_CASE_ ),
"epoch": epoch,
} , step=SCREAMING_SNAKE_CASE_ , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowerCAmelCase__ ( ) -> Optional[Any]:
'''simple docstring'''
A__ = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
parser.add_argument(
"--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
parser.add_argument(
"--project_dir" , type=SCREAMING_SNAKE_CASE_ , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
A__ = parser.parse_args()
A__ = {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
training_function(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 68 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
snake_case_ = nn.functional.normalize(UpperCAmelCase )
snake_case_ = nn.functional.normalize(UpperCAmelCase )
return torch.mm(UpperCAmelCase , normalized_text_embeds.t() )
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = CLIPConfig
SCREAMING_SNAKE_CASE_ = ["CLIPEncoderLayer"]
def __init__( self, lowerCAmelCase__) -> Optional[int]:
super().__init__(lowerCAmelCase__)
snake_case_ = CLIPVisionModel(config.vision_config)
snake_case_ = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=lowerCAmelCase__)
snake_case_ = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=lowerCAmelCase__)
snake_case_ = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=lowerCAmelCase__)
snake_case_ = nn.Parameter(torch.ones(17), requires_grad=lowerCAmelCase__)
snake_case_ = nn.Parameter(torch.ones(3), requires_grad=lowerCAmelCase__)
@torch.no_grad()
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__) -> Tuple:
snake_case_ = self.vision_model(lowerCAmelCase__)[1] # pooled_output
snake_case_ = self.visual_projection(lowerCAmelCase__)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ = cosine_distance(lowerCAmelCase__, self.special_care_embeds).cpu().float().numpy()
snake_case_ = cosine_distance(lowerCAmelCase__, self.concept_embeds).cpu().float().numpy()
snake_case_ = []
snake_case_ = image_embeds.shape[0]
for i in range(lowerCAmelCase__):
snake_case_ = {'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ = 0.0
for concept_idx in range(len(special_cos_dist[0])):
snake_case_ = special_cos_dist[i][concept_idx]
snake_case_ = self.special_care_embeds_weights[concept_idx].item()
snake_case_ = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]})
snake_case_ = 0.01
for concept_idx in range(len(cos_dist[0])):
snake_case_ = cos_dist[i][concept_idx]
snake_case_ = self.concept_embeds_weights[concept_idx].item()
snake_case_ = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(lowerCAmelCase__)
result.append(lowerCAmelCase__)
snake_case_ = [len(res['bad_concepts']) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__) -> Optional[int]:
snake_case_ = self.vision_model(lowerCAmelCase__)[1] # pooled_output
snake_case_ = self.visual_projection(lowerCAmelCase__)
snake_case_ = cosine_distance(lowerCAmelCase__, self.special_care_embeds)
snake_case_ = cosine_distance(lowerCAmelCase__, self.concept_embeds)
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ = 0.0
snake_case_ = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
snake_case_ = torch.any(special_scores > 0, dim=1)
snake_case_ = special_care * 0.01
snake_case_ = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
snake_case_ = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
snake_case_ = torch.any(concept_scores > 0, dim=1)
return images, has_nsfw_concepts
| 69 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( UpperCAmelCase : Dict=None ) -> Optional[int]:
if subparsers is not None:
UpperCAmelCase : Tuple = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase : Dict = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase : Optional[int] = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=UpperCAmelCase , default=UpperCAmelCase , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=UpperCAmelCase , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=UpperCAmelCase , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase : Union[str, Any] = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=UpperCAmelCase , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase )
return parser
def a__ ( UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase : List[str] = defaults.commands
if not args.tpu_name:
UpperCAmelCase : Tuple = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase : int = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase : Tuple = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase : Dict = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , UpperCAmelCase ):
UpperCAmelCase : Optional[int] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , UpperCAmelCase ):
UpperCAmelCase : int = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase : Optional[int] = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
UpperCAmelCase : int = '''; '''.join(UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase : Any = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(UpperCAmelCase )}''' )
return
subprocess.run(UpperCAmelCase )
print('''Successfully setup pod.''' )
def a__ ( ) -> Any:
UpperCAmelCase : Any = tpu_command_parser()
UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(UpperCAmelCase )
| 336 | 0 |
'''simple docstring'''
A__ : str =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = [False] * len(lowerCAmelCase )
_lowerCAmelCase = [s]
_lowerCAmelCase = True
while queue:
_lowerCAmelCase = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowerCAmelCase )
_lowerCAmelCase = True
_lowerCAmelCase = u
return visited[t]
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = [-1] * (len(lowerCAmelCase ))
_lowerCAmelCase = 0
_lowerCAmelCase = []
_lowerCAmelCase = [i[:] for i in graph] # Record original cut, copy.
while bfs(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
_lowerCAmelCase = float("""Inf""" )
_lowerCAmelCase = sink
while s != source:
# Find the minimum value in select path
_lowerCAmelCase = min(lowerCAmelCase , graph[parent[s]][s] )
_lowerCAmelCase = parent[s]
max_flow += path_flow
_lowerCAmelCase = sink
while v != source:
_lowerCAmelCase = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_lowerCAmelCase = parent[v]
for i in range(len(lowerCAmelCase ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 70 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 0 |
def A ( a_ = 1_000_000 ) -> int:
__UpperCamelCase : List[Any] =limit + 1
__UpperCamelCase : Any =[0] * limit
for first_term in range(1 ,a_ ):
for n in range(a_ ,a_ ,a_ ):
__UpperCamelCase : str =first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
__UpperCamelCase : Dict =sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"{solution() = }")
| 71 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __UpperCAmelCase ( lowerCamelCase__ ):
def __get__( self : Tuple, __A : Optional[Any], __A : Optional[int]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
UpperCAmelCase : str = '''__cached_''' + self.fget.__name__
UpperCAmelCase : int = getattr(__A, __A, __A )
if cached is None:
UpperCAmelCase : Any = self.fget(__A )
setattr(__A, __A, __A )
return cached
def a__ ( UpperCAmelCase : Optional[Any] ) -> Any:
UpperCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_torch_fx_proxy(UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(UpperCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : str ) -> Tuple:
return _is_numpy(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
import torch
return isinstance(UpperCAmelCase , torch.Tensor )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
return False if not is_torch_available() else _is_torch(UpperCAmelCase )
def a__ ( UpperCAmelCase : Tuple ) -> List[str]:
import torch
return isinstance(UpperCAmelCase , torch.device )
def a__ ( UpperCAmelCase : Any ) -> Any:
return False if not is_torch_available() else _is_torch_device(UpperCAmelCase )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
import torch
if isinstance(UpperCAmelCase , UpperCAmelCase ):
if hasattr(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
else:
return False
return isinstance(UpperCAmelCase , torch.dtype )
def a__ ( UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> str:
import tensorflow as tf
return isinstance(UpperCAmelCase , tf.Tensor )
def a__ ( UpperCAmelCase : int ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCAmelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(UpperCAmelCase )
return type(UpperCAmelCase ) == tf.Tensor
def a__ ( UpperCAmelCase : int ) -> List[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[Any] ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCAmelCase , jnp.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Optional[int]:
return False if not is_flax_available() else _is_jax(UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Tuple:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return [to_py_obj(UpperCAmelCase ) for o in obj]
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase ).tolist()
elif isinstance(UpperCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( UpperCAmelCase : Any ) -> List[str]:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_numpy(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return np.array(UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase )
else:
return obj
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(__A ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
UpperCAmelCase : int = getattr(self, class_fields[0].name )
UpperCAmelCase : str = all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__A ):
if isinstance(__A, __A ):
UpperCAmelCase : Tuple = first_field.items()
UpperCAmelCase : Any = True
else:
try:
UpperCAmelCase : Optional[Any] = iter(__A )
UpperCAmelCase : Optional[Any] = True
except TypeError:
UpperCAmelCase : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__A ):
if (
not isinstance(__A, (list, tuple) )
or not len(__A ) == 2
or not isinstance(element[0], __A )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
UpperCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
UpperCAmelCase : Union[str, Any] = element[1]
elif first_field is not None:
UpperCAmelCase : Union[str, Any] = first_field
else:
for field in class_fields:
UpperCAmelCase : Optional[Any] = getattr(self, field.name )
if v is not None:
UpperCAmelCase : Optional[int] = v
def __delitem__( self : Union[str, Any], *__A : str, **__A : Tuple ):
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : List[str], *__A : Union[str, Any], **__A : Optional[Any] ):
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Any, *__A : Dict, **__A : str ):
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Dict, *__A : int, **__A : Dict ):
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : List[str], __A : List[str] ):
if isinstance(__A, __A ):
UpperCAmelCase : int = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[Any], __A : Dict, __A : Union[str, Any] ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__A, __A )
super().__setattr__(__A, __A )
def __setitem__( self : Dict, __A : List[Any], __A : Union[str, Any] ):
# Will raise a KeyException if needed
super().__setitem__(__A, __A )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__A, __A )
def __magic_name__ ( self : List[str] ):
return tuple(self[k] for k in self.keys() )
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@classmethod
def __magic_name__ ( cls : List[Any], __A : Tuple ):
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """longest"""
UpperCamelCase = """max_length"""
UpperCamelCase = """do_not_pad"""
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
UpperCamelCase = """np"""
UpperCamelCase = """jax"""
class __UpperCAmelCase :
def __init__( self : Any, __A : List[ContextManager] ):
UpperCAmelCase : Tuple = context_managers
UpperCAmelCase : Tuple = ExitStack()
def __enter__( self : Any ):
for context_manager in self.context_managers:
self.stack.enter_context(__A )
def __exit__( self : List[Any], *__A : Union[str, Any], **__A : Dict ):
self.stack.__exit__(*__A, **__A )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : int = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a__ ( UpperCAmelCase : Dict ) -> Any:
UpperCAmelCase : List[Any] = model_class.__name__
UpperCAmelCase : Union[str, Any] = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a__ ( UpperCAmelCase : MutableMapping , UpperCAmelCase : str = "" , UpperCAmelCase : str = "." ) -> Union[str, Any]:
def _flatten_dict(UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str]="" , UpperCAmelCase : Any="." ):
for k, v in d.items():
UpperCAmelCase : List[str] = str(UpperCAmelCase ) + delimiter + str(UpperCAmelCase ) if parent_key else k
if v and isinstance(UpperCAmelCase , UpperCAmelCase ):
yield from flatten_dict(UpperCAmelCase , UpperCAmelCase , delimiter=UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
@contextmanager
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : bool = False ) -> Optional[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if is_numpy_array(UpperCAmelCase ):
return np.transpose(UpperCAmelCase , axes=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.T if axes is None else array.permute(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(UpperCAmelCase , perm=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.transpose(UpperCAmelCase , axes=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Optional[int] ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.reshape(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.reshape(UpperCAmelCase , UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=None ) -> Any:
if is_numpy_array(UpperCAmelCase ):
return np.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> str:
if is_numpy_array(UpperCAmelCase ):
return np.expand_dims(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.unsqueeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.size(UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.size(UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Dict:
for key, value in auto_map.items():
if isinstance(UpperCAmelCase , (tuple, list) ):
UpperCAmelCase : List[Any] = [f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
UpperCAmelCase : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a__ ( UpperCAmelCase : Tuple ) -> Union[str, Any]:
for base_class in inspect.getmro(UpperCAmelCase ):
UpperCAmelCase : Any = base_class.__module__
UpperCAmelCase : Dict = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 336 | 0 |
"""simple docstring"""
def snake_case_ ( A_ : int ):
'''simple docstring'''
assert isinstance(A_, A_ ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
_lowerCamelCase : List[Any] = F'''The input value of [n={number}] has to be > 0'''
raise ValueError(A_ )
else:
_lowerCamelCase : Dict = sylvester(number - 1 )
_lowerCamelCase : Dict = num - 1
_lowerCamelCase : Optional[int] = num
return lower * upper + 1
if __name__ == "__main__":
print(F"""The 8th number in Sylvester's sequence: {sylvester(8)}""")
| 72 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LayoutLMTokenizer
UpperCamelCase = LayoutLMTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
UpperCAmelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : int = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __magic_name__ ( self : Union[str, Any], **__A : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], __A : int ):
UpperCAmelCase : Optional[Any] = '''UNwant\u00E9d,running'''
UpperCAmelCase : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [7, 4, 5, 1_0, 8, 9] )
def __magic_name__ ( self : Optional[int] ):
pass
| 336 | 0 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
"""stable diffusion controlnet""",
"""0.22.0""",
"""Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.""",
standard_warn=False,
stacklevel=3,
)
| 73 |
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 __UpperCAmelCase :
def __init__( self : Any, __A : str, __A : Dict=1_3, __A : int=3_0, __A : Tuple=2, __A : Union[str, Any]=3, __A : Any=True, __A : str=True, __A : Dict=3_2, __A : List[Any]=2, __A : Optional[Any]=4, __A : Union[str, Any]=3_7, __A : int="gelu", __A : int=0.1, __A : List[Any]=0.1, __A : Tuple=1_0, __A : Tuple=0.0_2, __A : Any=3, __A : List[str]=0.6, __A : Any=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Optional[int] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Tuple = mask_ratio
UpperCAmelCase : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Optional[Any] ):
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=__A, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def __magic_name__ ( self : str, __A : List[Any], __A : Any, __A : Any ):
UpperCAmelCase : Optional[Any] = TFViTMAEModel(config=__A )
UpperCAmelCase : Tuple = model(__A, training=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : str, __A : int, __A : str ):
UpperCAmelCase : Dict = TFViTMAEForPreTraining(__A )
UpperCAmelCase : int = model(__A, training=__A )
# expected sequence length = num_patches
UpperCAmelCase : int = (self.image_size // self.patch_size) ** 2
UpperCAmelCase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase : Tuple = 1
UpperCAmelCase : List[Any] = TFViTMAEForPreTraining(__A )
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = model(__A, training=__A )
UpperCAmelCase : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , 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 __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = TFViTMAEModelTester(self )
UpperCAmelCase : int = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer) )
UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, tf.keras.layers.Layer ) )
def __magic_name__ ( self : str ):
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Any = model_class(__A )
UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : int = [*signature.parameters.keys()]
UpperCAmelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __magic_name__ ( self : int ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : str = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : Dict = model(__A, noise=__A )
UpperCAmelCase : Any = copy.deepcopy(self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Union[str, Any] = model(**__A, noise=__A )
UpperCAmelCase : Dict = outputs_dict[0].numpy()
UpperCAmelCase : Tuple = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ), 1E-6 )
def __magic_name__ ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__A : Union[str, Any] ):
UpperCAmelCase : str = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__A ):
UpperCAmelCase : Tuple = v.numpy()
else:
UpperCAmelCase : str = np.array(__A )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : Any = self._prepare_for_class(__A, __A )
UpperCAmelCase : Optional[int] = prepare_numpy_arrays(__A )
UpperCAmelCase : str = model(__A, noise=__A )
UpperCAmelCase : str = model(**__A, noise=__A )
self.assert_outputs_same(__A, __A )
def __magic_name__ ( self : int, __A : str, __A : Union[str, Any], __A : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase : Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : int = tf.constant(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase : List[Any] = tf_noise
super().check_pt_tf_models(__A, __A, __A )
def __magic_name__ ( self : str ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Union[str, Any] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__A )
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(__A, __A ),)
if isinstance(__A, __A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__A, '''_keras_serializable''', __A )
}
UpperCAmelCase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : str = tf.convert_to_tensor(__A )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCAmelCase : Tuple = main_layer_class(__A )
UpperCAmelCase : int = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCAmelCase : List[Any] = tf.keras.Model(__A, outputs=main_layer(__A ) )
UpperCAmelCase : List[Any] = model(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = os.path.join(__A, '''keras_model.h5''' )
model.save(__A )
UpperCAmelCase : List[str] = tf.keras.models.load_model(
__A, custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__A, tf.keras.Model )
UpperCAmelCase : Tuple = model(__A )
self.assert_outputs_same(__A, __A )
@slow
def __magic_name__ ( self : Dict ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(__A )
UpperCAmelCase : List[str] = self._prepare_for_class(__A, __A )
UpperCAmelCase : Union[str, Any] = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : Optional[int] = outputs.last_hidden_state.numpy()
UpperCAmelCase : Union[str, Any] = 0
else:
UpperCAmelCase : Optional[int] = outputs.logits.numpy()
UpperCAmelCase : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A, saved_model=__A )
UpperCAmelCase : Dict = model_class.from_pretrained(__A )
UpperCAmelCase : str = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : int = after_outputs['''last_hidden_state'''].numpy()
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Any = after_outputs['''logits'''].numpy()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A, 1E-5 )
def __magic_name__ ( self : Optional[Any] ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : List[Any] = model(__A, noise=__A )
UpperCAmelCase : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__A )
UpperCAmelCase : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCAmelCase : str = model_class.from_config(model.config )
UpperCAmelCase : List[str] = new_model(__A ) # Build model
new_model.set_weights(model.get_weights() )
UpperCAmelCase : Tuple = new_model(__A, noise=__A )
self.assert_outputs_same(__A, __A )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def __magic_name__ ( self : Tuple ):
pass
@slow
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(__A )
def a__ ( ) -> Dict:
UpperCAmelCase : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[str] ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase : Tuple = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
UpperCAmelCase : List[str] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : str = image_processor(images=__A, 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)
UpperCAmelCase : Optional[int] = ViTMAEConfig()
UpperCAmelCase : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCAmelCase : Optional[int] = model(**__A, noise=__A )
# verify the logits
UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3], __A, atol=1E-4 )
| 336 | 0 |
"""simple docstring"""
import math
def _snake_case ( snake_case__ : list , snake_case__ : int = 0 , snake_case__ : int = 0 ):
A = end or len(snake_case__ )
for i in range(snake_case__ , snake_case__ ):
A = i
A = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
A = array[temp_index - 1]
temp_index -= 1
A = temp_index_value
return array
def _snake_case ( snake_case__ : list , snake_case__ : int , snake_case__ : int ): # Max Heap
A = index
A = 2 * index + 1 # Left Node
A = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
A = left_index
if right_index < heap_size and array[largest] < array[right_index]:
A = right_index
if largest != index:
A , A = array[largest], array[index]
heapify(snake_case__ , snake_case__ , snake_case__ )
def _snake_case ( snake_case__ : list ):
A = len(snake_case__ )
for i in range(n // 2 , -1 , -1 ):
heapify(snake_case__ , snake_case__ , snake_case__ )
for i in range(n - 1 , 0 , -1 ):
A , A = array[0], array[i]
heapify(snake_case__ , 0 , snake_case__ )
return array
def _snake_case ( snake_case__ : list , snake_case__ : int , snake_case__ : int , snake_case__ : int ):
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _snake_case ( snake_case__ : list , snake_case__ : int , snake_case__ : int , snake_case__ : int ):
A = low
A = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
A , A = array[j], array[i]
i += 1
def _snake_case ( snake_case__ : list ):
if len(snake_case__ ) == 0:
return array
A = 2 * math.ceil(math.loga(len(snake_case__ ) ) )
A = 16
return intro_sort(snake_case__ , 0 , len(snake_case__ ) , snake_case__ , snake_case__ )
def _snake_case ( snake_case__ : list , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int ):
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(snake_case__ )
max_depth -= 1
A = median_of_a(snake_case__ , snake_case__ , start + ((end - start) // 2) + 1 , end - 1 )
A = partition(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
intro_sort(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
A = p
return insertion_sort(snake_case__ , snake_case__ , snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase = input('''Enter numbers separated by a comma : ''').strip()
_lowercase = [float(item) for item in user_input.split(''',''')]
print(sort(unsorted)) | 74 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_lowerCamelCase : List[Any] = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
_lowerCamelCase : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 336 | 0 |
'''simple docstring'''
def a_ ( __snake_case : list ) -> bool:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(__snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(__snake_case ) == 1:
return True
lowerCamelCase_ =series[1] - series[0]
for index in range(len(__snake_case ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def a_ ( __snake_case : list ) -> float:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(__snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
lowerCamelCase_ =0
for val in series:
answer += val
return answer / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] for _ in range(UpperCAmelCase )]
UpperCAmelCase : Union[str, Any] = run_maze(UpperCAmelCase , 0 , 0 , UpperCAmelCase )
if solved:
print('''\n'''.join(str(UpperCAmelCase ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def a__ ( UpperCAmelCase : list[list[int]] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Dict = len(UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase : Dict = 1
return True
UpperCAmelCase : Union[str, Any] = (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 : Any = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase : str = 1
# check for directions
if (
run_maze(UpperCAmelCase , i + 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j + 1 , UpperCAmelCase )
or run_maze(UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j - 1 , UpperCAmelCase )
):
return True
UpperCAmelCase : Any = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE : set[int] = set()
return any(
node not in visited and depth_first_search(_a , _a , _a , _a)
for node in graph)
def lowerCamelCase__ ( _a , _a , _a , _a):
visited.add(_a)
rec_stk.add(_a)
for node in graph[vertex]:
if node not in visited:
if depth_first_search(_a , _a , _a , _a):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(_a)
return False
if __name__ == "__main__":
from doctest import testmod
testmod() | 76 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __UpperCAmelCase :
def __init__( self : List[Any], __A : List[str], __A : List[str]=1_3, __A : Any=6_4, __A : Optional[Any]=2, __A : str=3, __A : str=True, __A : str=True, __A : Optional[Any]=3_2, __A : List[str]=5, __A : int=4, __A : str=3_7, __A : str="gelu", __A : Dict=0.1, __A : List[Any]=0.1, __A : Dict=1_0, __A : int=0.0_2, __A : Any=[1, 1_6, 4, 4], __A : Optional[int]=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Any = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : List[str] = patch_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase : str = type_sequence_label_size
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : int = scope
UpperCAmelCase : List[str] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
UpperCAmelCase : str = (self.image_size // 3_2) ** 2
UpperCAmelCase : List[str] = num_patches + 1
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : str = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Any ):
UpperCAmelCase : Dict = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 1_6, 3_2],
'''num_groups''': 2,
}
return ViTHybridConfig(
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, backbone_featmap_shape=self.backbone_featmap_shape, backbone_config=__A, )
def __magic_name__ ( self : Optional[int], __A : Optional[int], __A : int, __A : Tuple ):
UpperCAmelCase : int = ViTHybridModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Tuple = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : Dict, __A : str, __A : List[str] ):
UpperCAmelCase : str = self.type_sequence_label_size
UpperCAmelCase : List[Any] = ViTHybridForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : int ):
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = config_and_inputs
UpperCAmelCase : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
UpperCamelCase = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Any = ViTHybridModelTester(self )
UpperCAmelCase : List[Any] = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : int ):
UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, nn.Linear ) )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(__A )
UpperCAmelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = _config_zero_init(__A )
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(config=__A )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
UpperCAmelCase : Union[str, Any] = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@slow
def __magic_name__ ( self : List[str] ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Union[str, Any] = ViTHybridModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> Tuple:
UpperCAmelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : str ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__A )
UpperCAmelCase : Tuple = self.default_image_processor
UpperCAmelCase : int = prepare_img()
UpperCAmelCase : Union[str, Any] = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(**__A )
# verify the logits
UpperCAmelCase : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Optional[Any] = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
@require_accelerate
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''', device_map='''auto''' )
UpperCAmelCase : Tuple = prepare_img()
UpperCAmelCase : Optional[int] = image_processor(images=__A, return_tensors='''pt''' )
UpperCAmelCase : Dict = model(**__A )
UpperCAmelCase : Any = outputs.logits
# model predicts one of the 1000 ImageNet classes
UpperCAmelCase : Dict = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx], '''tabby, tabby cat''' )
| 336 | 0 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(_a)
class UpperCAmelCase_ ( _a):
def __init__( self , *a , **a ) -> Union[str, Any]:
super().__init__(*a , **a )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _UpperCAmelCase ( self , a=None , a=None , a=None ) -> Dict:
lowercase__ : int = {}
lowercase__ : List[str] = {}
if prompt is not None:
lowercase__ : Any = prompt
if generate_kwargs is not None:
lowercase__ : Dict = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowercase__ : str = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
' please use only one' )
lowercase__ : List[str] = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , a , **a ) -> List[str]:
return super().__call__(a , **a )
def _UpperCAmelCase ( self , a , a=None ) -> Optional[Any]:
lowercase__ : List[Any] = load_image(a )
if prompt is not None:
if not isinstance(a , a ):
raise ValueError(
f"""Received an invalid text input, got - {type(a )} - but expected a single string. """
'Note also that one single text can be provided for conditional image to text generation.' )
lowercase__ : Optional[Any] = self.model.config.model_type
if model_type == "git":
lowercase__ : List[str] = self.image_processor(images=a , return_tensors=self.framework )
lowercase__ : List[Any] = self.tokenizer(text=a , add_special_tokens=a ).input_ids
lowercase__ : int = [self.tokenizer.cls_token_id] + input_ids
lowercase__ : Tuple = torch.tensor(a ).unsqueeze(0 )
model_inputs.update({'input_ids': input_ids} )
elif model_type == "pix2struct":
lowercase__ : Dict = self.image_processor(images=a , header_text=a , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowercase__ : int = self.image_processor(images=a , return_tensors=self.framework )
lowercase__ : Optional[int] = self.tokenizer(a , return_tensors=self.framework )
model_inputs.update(a )
else:
raise ValueError(f"""Model type {model_type} does not support conditional text generation""" )
else:
lowercase__ : Any = self.image_processor(images=a , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowercase__ : Optional[int] = None
return model_inputs
def _UpperCAmelCase ( self , a , a=None ) -> Dict:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['input_ids'] , a )
and all(x is None for x in model_inputs['input_ids'] )
):
lowercase__ : Tuple = None
if generate_kwargs is None:
lowercase__ : Optional[Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowercase__ : Optional[int] = model_inputs.pop(self.model.main_input_name )
lowercase__ : List[Any] = self.model.generate(a , **a , **a )
return model_outputs
def _UpperCAmelCase ( self , a ) -> Optional[Any]:
lowercase__ : List[str] = []
for output_ids in model_outputs:
lowercase__ : str = {
'generated_text': self.tokenizer.decode(
a , skip_special_tokens=a , )
}
records.append(a )
return records
| 77 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_000 , 5_000 )
return (arr, r)
_lowerCamelCase : Any = make_dataset()
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, ...]:
for triplet in permutations(UpperCAmelCase , 3 ):
if sum(UpperCAmelCase ) == target:
return tuple(sorted(UpperCAmelCase ) )
return (0, 0, 0)
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, int, int]:
arr.sort()
UpperCAmelCase : Tuple = len(UpperCAmelCase )
for i in range(n - 1 ):
UpperCAmelCase , UpperCAmelCase : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a__ ( ) -> tuple[float, float]:
UpperCAmelCase : Union[str, Any] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
UpperCAmelCase : Tuple = '''
triplet_sum1(*dataset)
'''
UpperCAmelCase : List[str] = '''
triplet_sum2(*dataset)
'''
UpperCAmelCase : Tuple = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
UpperCAmelCase : str = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
return (min(UpperCAmelCase ), min(UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCamelCase : int = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 336 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
snake_case_ = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_ )
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Optional[int] , *lowercase_ :Dict , **lowercase_ :Optional[int] ) -> List[str]:
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , 'vision' )
self.check_model_type(lowercase_ )
def __call__( self :str , lowercase_ :Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowercase_ :str ) -> Optional[int]:
return super().__call__(lowercase_ , **lowercase_ )
def UpperCAmelCase__ ( self :Dict , **lowercase_ :str ) -> List[str]:
return {}, {}, {}
def UpperCAmelCase__ ( self :str , lowercase_ :Optional[int] ) -> Any:
UpperCAmelCase = load_image(lowercase_ )
UpperCAmelCase = image.size
UpperCAmelCase = self.image_processor(images=lowercase_ , return_tensors=self.framework )
return model_inputs
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :int ) -> str:
UpperCAmelCase = self.model(**lowercase_ )
return model_outputs
def UpperCAmelCase__ ( self :str , lowercase_ :Union[str, Any] ) -> Tuple:
UpperCAmelCase = model_outputs.predicted_depth
UpperCAmelCase = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='bicubic' , align_corners=lowercase_ )
UpperCAmelCase = prediction.squeeze().cpu().numpy()
UpperCAmelCase = (output * 2_55 / np.max(lowercase_ )).astype('uint8' )
UpperCAmelCase = Image.fromarray(lowercase_ )
UpperCAmelCase = {}
UpperCAmelCase = predicted_depth
UpperCAmelCase = depth
return output_dict
| 78 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def __magic_name__ ( self : int ):
raise NotImplementedError()
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : str, __A : "AutoTokenizer", __A : bool = False, **__A : str ):
UpperCAmelCase : List[str] = tokenizer
UpperCAmelCase : str = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = True
def __magic_name__ ( self : Dict, __A : Optional[int] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
UpperCAmelCase : Union[str, Any] = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Any = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : Optional[Any] = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def __magic_name__ ( self : str ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
UpperCAmelCase : int = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : List[Any] = []
UpperCAmelCase : List[Any] = 0
else:
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : str = True
self.on_finalized_text(__A, stream_end=__A )
def __magic_name__ ( self : List[str], __A : str, __A : bool = False ):
print(__A, flush=__A, end='''''' if not stream_end else None )
def __magic_name__ ( self : List[Any], __A : Optional[int] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : "AutoTokenizer", __A : bool = False, __A : Optional[float] = None, **__A : str ):
super().__init__(__A, __A, **__A )
UpperCAmelCase : Dict = Queue()
UpperCAmelCase : Any = None
UpperCAmelCase : Any = timeout
def __magic_name__ ( self : Dict, __A : str, __A : bool = False ):
self.text_queue.put(__A, timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout )
def __iter__( self : int ):
return self
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[Any] = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 336 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
snake_case = '''encoder-decoder'''
snake_case = True
def __init__( self : Optional[int] , **__UpperCAmelCase : Any ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
_A = kwargs.pop("encoder" )
_A = encoder_config.pop("model_type" )
_A = kwargs.pop("decoder" )
_A = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
_A = AutoConfig.for_model(__UpperCAmelCase , **__UpperCAmelCase )
_A = AutoConfig.for_model(__UpperCAmelCase , **__UpperCAmelCase )
_A = True
@classmethod
def lowerCAmelCase ( cls : str , __UpperCAmelCase : PretrainedConfig , __UpperCAmelCase : PretrainedConfig , **__UpperCAmelCase : str ):
'''simple docstring'''
logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
_A = True
_A = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__UpperCAmelCase )
def lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
_A = copy.deepcopy(self.__dict__ )
_A = self.encoder.to_dict()
_A = self.decoder.to_dict()
_A = self.__class__.model_type
return output
| 79 |
import numpy
# List of input, output pairs
_lowerCamelCase : Dict = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
_lowerCamelCase : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
_lowerCamelCase : Dict = [2, 4, 1, 5]
_lowerCamelCase : Dict = len(train_data)
_lowerCamelCase : int = 0.0_0_9
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]="train" ) -> Dict:
return calculate_hypothesis_value(UpperCAmelCase , UpperCAmelCase ) - output(
UpperCAmelCase , UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Any:
UpperCAmelCase : str = 0
for i in range(len(UpperCAmelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> Optional[int]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def a__ ( UpperCAmelCase : int , UpperCAmelCase : Optional[Any] ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : str=m ) -> Dict:
UpperCAmelCase : Optional[int] = 0
for i in range(UpperCAmelCase ):
if index == -1:
summation_value += _error(UpperCAmelCase )
else:
summation_value += _error(UpperCAmelCase ) * train_data[i][0][index]
return summation_value
def a__ ( UpperCAmelCase : Dict ) -> Dict:
UpperCAmelCase : Dict = summation_of_cost_derivative(UpperCAmelCase , UpperCAmelCase ) / m
return cost_derivative_value
def a__ ( ) -> List[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
UpperCAmelCase : List[str] = 0.000002
UpperCAmelCase : Any = 0
UpperCAmelCase : Dict = 0
while True:
j += 1
UpperCAmelCase : List[Any] = [0, 0, 0, 0]
for i in range(0 , len(UpperCAmelCase ) ):
UpperCAmelCase : List[str] = get_cost_derivative(i - 1 )
UpperCAmelCase : Tuple = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
UpperCAmelCase , UpperCAmelCase , atol=UpperCAmelCase , rtol=UpperCAmelCase , ):
break
UpperCAmelCase : int = temp_parameter_vector
print(('''Number of iterations:''', j) )
def a__ ( ) -> List[Any]:
for i in range(len(UpperCAmelCase ) ):
print(('''Actual output value:''', output(UpperCAmelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCAmelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 336 | 0 |
'''simple docstring'''
import string
from math import logaa
def _UpperCamelCase ( __A , __A ) -> int:
'''simple docstring'''
UpperCamelCase__ = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
UpperCamelCase__ = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def _UpperCamelCase ( __A , __A ) -> tuple[int, int]:
'''simple docstring'''
UpperCamelCase__ = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
UpperCamelCase__ = corpus_without_punctuation.split("\n" )
UpperCamelCase__ = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__A ))
def _UpperCamelCase ( __A , __A , __A=False ) -> float:
'''simple docstring'''
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def _UpperCamelCase ( __A , __A ) -> float:
'''simple docstring'''
return round(tf * idf , 3 )
| 80 |
def a__ ( UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] ) -> Optional[Any]:
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[Any] = len(UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
UpperCAmelCase : Optional[Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
UpperCAmelCase : Any = left
UpperCAmelCase : List[str] = point
elif point > right:
UpperCAmelCase : Any = right
UpperCAmelCase : List[str] = point
else:
if item < current_item:
UpperCAmelCase : Optional[int] = point - 1
else:
UpperCAmelCase : str = point + 1
return None
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : List[str] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , point + 1 , UpperCAmelCase )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> int:
if collection != sorted(UpperCAmelCase ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
_lowerCamelCase : Optional[int] = 0
if debug == 1:
_lowerCamelCase : Dict = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
_lowerCamelCase : List[Any] = 6_7
_lowerCamelCase : Optional[Any] = interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print("Not found")
| 336 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
lowerCamelCase_ : str = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
lowerCamelCase_ : Any = TaTokenizerFast
lowerCamelCase_ : Tuple = {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Tuple = [
"""MT5EncoderModel""",
"""MT5ForConditionalGeneration""",
"""MT5ForQuestionAnswering""",
"""MT5Model""",
"""MT5PreTrainedModel""",
"""MT5Stack""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Union[str, Any] = ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : List[Any] = ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
lowerCamelCase_ : Optional[int] = _LazyModule(
__name__,
globals()["""__file__"""],
_import_structure,
extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
module_spec=__spec__,
) | 81 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Any = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : List[str]=False ) -> Any:
UpperCAmelCase : Optional[int] = '''backbone.''' if is_semantic else ''''''
UpperCAmelCase : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
(f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
(f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
(f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
(f'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : str=False , UpperCAmelCase : Dict=False ) -> Any:
for i in range(config.num_hidden_layers ):
UpperCAmelCase : Tuple = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias''' )
UpperCAmelCase : List[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias''' )
UpperCAmelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase : str = q_bias
UpperCAmelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
UpperCAmelCase : int = state_dict.pop(f'''{prefix}blocks.{i}.gamma_1''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.gamma_2''' )
UpperCAmelCase : str = gamma_a
UpperCAmelCase : Dict = gamma_a
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : str = val
def a__ ( ) -> Optional[int]:
UpperCAmelCase : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase : Union[str, Any] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any]=False ) -> Union[str, Any]:
UpperCAmelCase : Dict = False if '''rvlcdip''' in checkpoint_url else True
UpperCAmelCase : Any = BeitConfig(use_absolute_position_embeddings=UpperCAmelCase , use_mask_token=UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
UpperCAmelCase : List[Any] = 1_024
UpperCAmelCase : Optional[Any] = 4_096
UpperCAmelCase : Any = 24
UpperCAmelCase : Union[str, Any] = 16
# labels
if "rvlcdip" in checkpoint_url:
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : List[Any] = '''huggingface/label-files'''
UpperCAmelCase : Any = '''rvlcdip-id2label.json'''
UpperCAmelCase : List[str] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Dict = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : Union[str, Any] = idalabel
UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )['''model''']
UpperCAmelCase : List[str] = create_rename_keys(UpperCAmelCase , has_lm_head=UpperCAmelCase )
for src, dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
read_in_q_k_v(UpperCAmelCase , UpperCAmelCase , has_lm_head=UpperCAmelCase )
# load HuggingFace model
UpperCAmelCase : Tuple = BeitForMaskedImageModeling(UpperCAmelCase ) if has_lm_head else BeitForImageClassification(UpperCAmelCase )
model.eval()
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image
UpperCAmelCase : Dict = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCAmelCase )
UpperCAmelCase : List[str] = prepare_img()
UpperCAmelCase : Optional[Any] = image_processor(images=UpperCAmelCase , return_tensors='''pt''' )
UpperCAmelCase : str = encoding['''pixel_values''']
UpperCAmelCase : Any = model(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify logits
UpperCAmelCase : List[Any] = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(UpperCAmelCase ), "Shape of logits not as expected"
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
if has_lm_head:
UpperCAmelCase : List[Any] = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
UpperCAmelCase : Any = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=UpperCAmelCase , )
if __name__ == "__main__":
_lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 336 | 0 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""-m""" , """--pretrained_model_name_or_path""" , type=snake_case , default=snake_case , required=snake_case , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , )
parser.add_argument(
"""-c""" , """--caption""" , type=snake_case , default="""robotic cat with wings""" , help="""Text used to generate images.""" , )
parser.add_argument(
"""-n""" , """--images_num""" , type=snake_case , default=4 , help="""How much images to generate.""" , )
parser.add_argument(
"""-s""" , """--seed""" , type=snake_case , default=42 , help="""Seed for random process.""" , )
parser.add_argument(
"""-ci""" , """--cuda_id""" , type=snake_case , default=0 , help="""cuda_id.""" , )
_lowerCAmelCase = parser.parse_args()
return args
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if not len(snake_case ) == rows * cols:
raise ValueError("""The specified number of rows and columns are not correct.""" )
_lowerCAmelCase , _lowerCAmelCase = imgs[0].size
_lowerCAmelCase = Image.new("""RGB""" , size=(cols * w, rows * h) )
_lowerCAmelCase , _lowerCAmelCase = grid.size
for i, img in enumerate(snake_case ):
grid.paste(snake_case , box=(i % cols * w, i // cols * h) )
return grid
def _UpperCAmelCase ( snake_case , snake_case="robotic cat with wings" , snake_case=7.5 , snake_case=50 , snake_case=1 , snake_case=42 , ):
"""simple docstring"""
_lowerCAmelCase = torch.Generator(pipeline.device ).manual_seed(snake_case )
_lowerCAmelCase = pipeline(
snake_case , guidance_scale=snake_case , num_inference_steps=snake_case , generator=snake_case , num_images_per_prompt=snake_case , ).images
_lowerCAmelCase = int(math.sqrt(snake_case ) )
_lowerCAmelCase = image_grid(snake_case , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
A__ = parse_args()
# Load models and create wrapper for stable diffusion
A__ = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
A__ = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
A__ = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
A__ = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")
A__ = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
A__ = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
A__ = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, """unet""", unet)
else:
A__ = unet.to(torch.device("""cuda""", args.cuda_id))
A__ = pipeline.to(unet.device)
A__ , A__ = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
A__ = os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))
| 82 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[int], __A : Optional[int], __A : Any=1_3, __A : str=7, __A : Optional[int]=True, __A : Tuple=True, __A : Union[str, Any]=True, __A : Any=True, __A : Optional[int]=9_9, __A : Tuple=3_2, __A : str=5, __A : Union[str, Any]=4, __A : List[str]=3_7, __A : Tuple="gelu", __A : Optional[int]=0.1, __A : int=0.1, __A : Optional[Any]=5_1_2, __A : int=1_6, __A : Optional[Any]=2, __A : Union[str, Any]=0.0_2, __A : Optional[int]=4, ):
UpperCAmelCase : Any = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Any = seq_length
UpperCAmelCase : Tuple = is_training
UpperCAmelCase : str = use_attention_mask
UpperCAmelCase : List[str] = use_token_type_ids
UpperCAmelCase : int = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Any = num_choices
def __magic_name__ ( self : str ):
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase : List[Any] = None
if self.use_attention_mask:
UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase : Union[str, Any] = RobertaConfig(
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, )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self : int ):
UpperCAmelCase : Any = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = config_and_inputs
UpperCAmelCase : Any = True
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = FlaxRobertaModelTester(self )
@slow
def __magic_name__ ( self : Any ):
for model_class_name in self.all_model_classes:
UpperCAmelCase : Dict = model_class_name.from_pretrained('''roberta-base''', from_pt=__A )
UpperCAmelCase : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
| 336 | 0 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
snake_case_ : Union[str, Any] = 'bert-base-cased'
snake_case_ : Any = 'google/pegasus-xsum'
snake_case_ : Tuple = [' Sam ate lunch today.', 'Sams lunch ingredients.']
snake_case_ : List[str] = ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
snake_case_ : int = 'patrickvonplaten/t5-tiny-random'
snake_case_ : Union[str, Any] = 'sshleifer/bart-tiny-random'
snake_case_ : Any = 'sshleifer/tiny-mbart'
snake_case_ : Any = 'sshleifer/tiny-marian-en-de'
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase : Any = '\n'.join(UpperCAmelCase_ )
Path(UpperCAmelCase_ ).open('w' ).writelines(UpperCAmelCase_ )
def A__ ( UpperCAmelCase_ ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(UpperCAmelCase_ , f'{split}.source' ) , UpperCAmelCase_ )
_dump_articles(os.path.join(UpperCAmelCase_ , f'{split}.target' ) , UpperCAmelCase_ )
return tmp_dir
class lowercase__ ( lowercase ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] ,)
@slow
def UpperCamelCase_ ( self : Tuple ,lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : Tuple = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : int = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
_UpperCamelCase : Optional[Any] = max(len(tokenizer.encode(lowerCamelCase__ ) ) for a in ARTICLES )
_UpperCamelCase : str = max(len(tokenizer.encode(lowerCamelCase__ ) ) for a in SUMMARIES )
_UpperCamelCase : Dict = 4
_UpperCamelCase : List[Any] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
_UpperCamelCase , _UpperCamelCase : int = 'ro_RO', 'de_DE' # ignored for all but mbart, but never causes error.
_UpperCamelCase : Optional[Any] = SeqaSeqDataset(
lowerCamelCase__ ,data_dir=lowerCamelCase__ ,type_path='train' ,max_source_length=lowerCamelCase__ ,max_target_length=lowerCamelCase__ ,src_lang=lowerCamelCase__ ,tgt_lang=lowerCamelCase__ ,)
_UpperCamelCase : Union[str, Any] = DataLoader(lowerCamelCase__ ,batch_size=2 ,collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(lowerCamelCase__ ,lowerCamelCase__ )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
_UpperCamelCase : Tuple = shift_tokens_right(batch['labels'] ,tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : Any ):
'''simple docstring'''
_UpperCamelCase : List[Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : Any = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
_UpperCamelCase : Optional[Any] = max(len(tokenizer.encode(lowerCamelCase__ ) ) for a in ARTICLES )
_UpperCamelCase : Dict = max(len(tokenizer.encode(lowerCamelCase__ ) ) for a in SUMMARIES )
_UpperCamelCase : Union[str, Any] = 4
_UpperCamelCase : List[str] = LegacySeqaSeqDataset(
lowerCamelCase__ ,data_dir=lowerCamelCase__ ,type_path='train' ,max_source_length=20 ,max_target_length=lowerCamelCase__ ,)
_UpperCamelCase : Any = DataLoader(lowerCamelCase__ ,batch_size=2 ,collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : str = AutoTokenizer.from_pretrained('facebook/mbart-large-cc25' )
_UpperCamelCase : Dict = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
_UpperCamelCase : Dict = tmp_dir.joinpath('train.source' ).open().readlines()
_UpperCamelCase : Any = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(lowerCamelCase__ ,lowerCamelCase__ ,128 ,lowerCamelCase__ )
_UpperCamelCase : Tuple = {x.name for x in tmp_dir.iterdir()}
_UpperCamelCase : Dict = {x.name for x in save_dir.iterdir()}
_UpperCamelCase : Union[str, Any] = save_dir.joinpath('train.source' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(lowerCamelCase__ ) < len(lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1
assert len(packed_examples[0] ) == sum(len(lowerCamelCase__ ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,reason='This test requires fairseq' )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
if not FAIRSEQ_AVAILABLE:
return
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Dict = self._get_dataset(max_len=64 )
_UpperCamelCase : Optional[Any] = 64
_UpperCamelCase : str = ds.make_dynamic_sampler(lowerCamelCase__ ,required_batch_size_multiple=lowerCamelCase__ )
_UpperCamelCase : Tuple = [len(lowerCamelCase__ ) for x in batch_sampler]
assert len(set(lowerCamelCase__ ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(lowerCamelCase__ ) == len(lowerCamelCase__ ) # no dropped or added examples
_UpperCamelCase : str = DataLoader(lowerCamelCase__ ,batch_sampler=lowerCamelCase__ ,collate_fn=ds.collate_fn ,num_workers=2 )
_UpperCamelCase : Any = []
_UpperCamelCase : Tuple = []
for batch in data_loader:
_UpperCamelCase : str = batch['input_ids'].shape
_UpperCamelCase : List[Any] = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
_UpperCamelCase : Dict = np.product(batch['input_ids'].shape )
num_src_per_batch.append(lowerCamelCase__ )
if num_src_tokens > (max_tokens * 1.1):
failures.append(lowerCamelCase__ )
assert num_src_per_batch[0] == max(lowerCamelCase__ )
if failures:
raise AssertionError(F'too many tokens in {len(lowerCamelCase__ )} batches' )
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Optional[int] = self._get_dataset(max_len=512 )
_UpperCamelCase : Dict = 2
_UpperCamelCase : Any = ds.make_sortish_sampler(lowerCamelCase__ ,shuffle=lowerCamelCase__ )
_UpperCamelCase : List[Any] = DataLoader(lowerCamelCase__ ,batch_size=lowerCamelCase__ ,collate_fn=ds.collate_fn ,num_workers=2 )
_UpperCamelCase : List[str] = DataLoader(lowerCamelCase__ ,batch_size=lowerCamelCase__ ,collate_fn=ds.collate_fn ,num_workers=2 ,sampler=lowerCamelCase__ )
_UpperCamelCase : Union[str, Any] = tokenizer.pad_token_id
def count_pad_tokens(lowerCamelCase__ : str ,lowerCamelCase__ : Union[str, Any]="input_ids" ):
return [batch[k].eq(lowerCamelCase__ ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(lowerCamelCase__ ,k='labels' ) ) < sum(count_pad_tokens(lowerCamelCase__ ,k='labels' ) )
assert sum(count_pad_tokens(lowerCamelCase__ ) ) < sum(count_pad_tokens(lowerCamelCase__ ) )
assert len(lowerCamelCase__ ) == len(lowerCamelCase__ )
def UpperCamelCase_ ( self : Optional[int] ,lowerCamelCase__ : Optional[int]=1000 ,lowerCamelCase__ : Union[str, Any]=128 ):
'''simple docstring'''
if os.getenv('USE_REAL_DATA' ,lowerCamelCase__ ):
_UpperCamelCase : List[Any] = 'examples/seq2seq/wmt_en_ro'
_UpperCamelCase : int = max_len * 2 * 64
if not Path(lowerCamelCase__ ).joinpath('train.len' ).exists():
save_len_file(lowerCamelCase__ ,lowerCamelCase__ )
else:
_UpperCamelCase : Any = 'examples/seq2seq/test_data/wmt_en_ro'
_UpperCamelCase : Any = max_len * 4
save_len_file(lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : str = SeqaSeqDataset(
lowerCamelCase__ ,data_dir=lowerCamelCase__ ,type_path='train' ,max_source_length=lowerCamelCase__ ,max_target_length=lowerCamelCase__ ,n_obs=lowerCamelCase__ ,)
return ds, max_tokens, tokenizer
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : List[str] = self._get_dataset()
_UpperCamelCase : Any = set(DistributedSortishSampler(lowerCamelCase__ ,256 ,num_replicas=2 ,rank=0 ,add_extra_examples=lowerCamelCase__ ) )
_UpperCamelCase : List[Any] = set(DistributedSortishSampler(lowerCamelCase__ ,256 ,num_replicas=2 ,rank=1 ,add_extra_examples=lowerCamelCase__ ) )
assert idsa.intersection(lowerCamelCase__ ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] ,)
def UpperCamelCase_ ( self : List[Any] ,lowerCamelCase__ : str ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ ,use_fast=lowerCamelCase__ )
if tok_name == MBART_TINY:
_UpperCamelCase : Union[str, Any] = SeqaSeqDataset(
lowerCamelCase__ ,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) ,type_path='train' ,max_source_length=4 ,max_target_length=8 ,src_lang='EN' ,tgt_lang='FR' ,)
_UpperCamelCase : Tuple = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
_UpperCamelCase : Tuple = SeqaSeqDataset(
lowerCamelCase__ ,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) ,type_path='train' ,max_source_length=4 ,max_target_length=8 ,)
_UpperCamelCase : Any = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(lowerCamelCase__ ) == 1 if tok_name == BART_TINY else len(lowerCamelCase__ ) == 0
| 83 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"vocab_file": "vocab.txt"}
_lowerCamelCase : List[str] = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase : List[Any] = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def a__ ( UpperCAmelCase : List[str] ) -> Any:
with open(UpperCAmelCase , '''r''' ) as f:
UpperCAmelCase : Dict = f.read().splitlines()
return [l.strip() for l in lines]
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, __A : Dict, __A : List[Any]="<unk>", __A : List[str]="<cls>", __A : Any="<pad>", __A : Union[str, Any]="<mask>", __A : int="<eos>", **__A : Tuple, ):
super().__init__(**__A )
UpperCAmelCase : Tuple = load_vocab_file(__A )
UpperCAmelCase : List[Any] = dict(enumerate(self.all_tokens ) )
UpperCAmelCase : str = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase : Union[str, Any] = unk_token
UpperCAmelCase : Optional[Any] = cls_token
UpperCAmelCase : Optional[int] = pad_token
UpperCAmelCase : Optional[int] = mask_token
UpperCAmelCase : List[str] = eos_token
UpperCAmelCase : Optional[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def __magic_name__ ( self : Tuple, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : List[Any], __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : Optional[Any], **__A : Union[str, Any] ):
return text.split()
def __magic_name__ ( self : Optional[int], __A : Dict=False ):
return len(self._id_to_token )
def __magic_name__ ( self : int ):
return {token: i for i, token in enumerate(self.all_tokens )}
def __magic_name__ ( self : Tuple, __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : Union[str, Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Optional[int] = [self.cls_token_id]
UpperCAmelCase : Optional[int] = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def __magic_name__ ( self : Any, __A : List, __A : Optional[List] = None, __A : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase : Dict = [1] + ([0] * len(__A )) + [1]
if token_ids_a is not None:
mask += [0] * len(__A ) + [1]
return mask
def __magic_name__ ( self : Optional[int], __A : List[Any], __A : Dict ):
UpperCAmelCase : Union[str, Any] = os.path.join(__A, (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(__A, '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def __magic_name__ ( self : Dict ):
return self.get_vocab_size(with_added_tokens=__A )
def __magic_name__ ( self : Optional[int], __A : Union[List[str], List[AddedToken]], __A : bool = False ):
return super()._add_tokens(__A, special_tokens=__A )
| 336 | 0 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Tuple=0.999 , lowercase__ : Tuple="cosine" , ) -> Union[str, Any]:
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowercase__ : List[str] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowercase__ : Union[str, Any] ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
lowerCAmelCase_ :Any = []
for i in range(lowercase__ ):
lowerCAmelCase_ :Tuple = i / num_diffusion_timesteps
lowerCAmelCase_ :List[str] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowercase__ ) / alpha_bar_fn(lowercase__ ) , lowercase__ ) )
return torch.tensor(lowercase__ , dtype=torch.floataa )
class _SCREAMING_SNAKE_CASE ( A__ , A__ ):
UpperCAmelCase_ :Optional[int] = [e.name for e in KarrasDiffusionSchedulers]
UpperCAmelCase_ :Optional[Any] = 2
@register_to_config
def __init__( self , __A = 1000 , __A = 0.0_0_0_8_5 , __A = 0.0_1_2 , __A = "linear" , __A = None , __A = "epsilon" , __A = "linspace" , __A = 0 , ) -> Tuple:
if trained_betas is not None:
lowerCAmelCase_ :int = torch.tensor(__A , dtype=torch.floataa )
elif beta_schedule == "linear":
lowerCAmelCase_ :List[Any] = torch.linspace(__A , __A , __A , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowerCAmelCase_ :Dict = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __A , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowerCAmelCase_ :Dict = betas_for_alpha_bar(__A )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
lowerCAmelCase_ :Optional[Any] = 1.0 - self.betas
lowerCAmelCase_ :Tuple = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(__A , __A , __A )
def __lowerCAmelCase ( self , __A , __A=None ) -> List[str]:
if schedule_timesteps is None:
lowerCAmelCase_ :Any = self.timesteps
lowerCAmelCase_ :Tuple = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
lowerCAmelCase_ :int = 1 if len(__A ) > 1 else 0
else:
lowerCAmelCase_ :str = timestep.cpu().item() if torch.is_tensor(__A ) else timestep
lowerCAmelCase_ :Optional[int] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def __lowerCAmelCase ( self ) -> str:
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def __lowerCAmelCase ( self , __A , __A , ) -> torch.FloatTensor:
lowerCAmelCase_ :List[str] = self.index_for_timestep(__A )
if self.state_in_first_order:
lowerCAmelCase_ :str = self.sigmas[step_index]
else:
lowerCAmelCase_ :Any = self.sigmas_interpol[step_index]
lowerCAmelCase_ :int = sample / ((sigma**2 + 1) ** 0.5)
return sample
def __lowerCAmelCase ( self , __A , __A = None , __A = None , ) -> Tuple:
lowerCAmelCase_ :List[str] = num_inference_steps
lowerCAmelCase_ :str = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
lowerCAmelCase_ :Union[str, Any] = np.linspace(0 , num_train_timesteps - 1 , __A , dtype=__A )[::-1].copy()
elif self.config.timestep_spacing == "leading":
lowerCAmelCase_ :Any = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCAmelCase_ :Dict = (np.arange(0 , __A ) * step_ratio).round()[::-1].copy().astype(__A )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
lowerCAmelCase_ :Optional[Any] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCAmelCase_ :int = (np.arange(__A , 0 , -step_ratio )).round().copy().astype(__A )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
lowerCAmelCase_ :List[str] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
lowerCAmelCase_ :Union[str, Any] = torch.from_numpy(np.log(__A ) ).to(__A )
lowerCAmelCase_ :Dict = np.interp(__A , np.arange(0 , len(__A ) ) , __A )
lowerCAmelCase_ :Optional[int] = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
lowerCAmelCase_ :str = torch.from_numpy(__A ).to(device=__A )
# interpolate sigmas
lowerCAmelCase_ :List[Any] = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
lowerCAmelCase_ :str = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
lowerCAmelCase_ :str = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(__A ).startswith("""mps""" ):
# mps does not support float64
lowerCAmelCase_ :Any = torch.from_numpy(__A ).to(__A , dtype=torch.floataa )
else:
lowerCAmelCase_ :List[str] = torch.from_numpy(__A ).to(__A )
# interpolate timesteps
lowerCAmelCase_ :Tuple = self.sigma_to_t(__A ).to(__A , dtype=timesteps.dtype )
lowerCAmelCase_ :Tuple = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
lowerCAmelCase_ :str = torch.cat([timesteps[:1], interleaved_timesteps] )
lowerCAmelCase_ :Dict = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
lowerCAmelCase_ :Optional[Any] = defaultdict(__A )
def __lowerCAmelCase ( self , __A ) -> Union[str, Any]:
# get log sigma
lowerCAmelCase_ :Union[str, Any] = sigma.log()
# get distribution
lowerCAmelCase_ :Any = log_sigma - self.log_sigmas[:, None]
# get sigmas range
lowerCAmelCase_ :List[Any] = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
lowerCAmelCase_ :Tuple = low_idx + 1
lowerCAmelCase_ :str = self.log_sigmas[low_idx]
lowerCAmelCase_ :Optional[Any] = self.log_sigmas[high_idx]
# interpolate sigmas
lowerCAmelCase_ :Union[str, Any] = (low - log_sigma) / (low - high)
lowerCAmelCase_ :Union[str, Any] = w.clamp(0 , 1 )
# transform interpolation to time range
lowerCAmelCase_ :Optional[int] = (1 - w) * low_idx + w * high_idx
lowerCAmelCase_ :int = t.view(sigma.shape )
return t
@property
def __lowerCAmelCase ( self ) -> List[Any]:
return self.sample is None
def __lowerCAmelCase ( self , __A , __A , __A , __A = True , ) -> Union[SchedulerOutput, Tuple]:
lowerCAmelCase_ :Optional[int] = self.index_for_timestep(__A )
# advance index counter by 1
lowerCAmelCase_ :Any = timestep.cpu().item() if torch.is_tensor(__A ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
lowerCAmelCase_ :Optional[Any] = self.sigmas[step_index]
lowerCAmelCase_ :List[str] = self.sigmas_interpol[step_index + 1]
lowerCAmelCase_ :str = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
lowerCAmelCase_ :str = self.sigmas[step_index - 1]
lowerCAmelCase_ :Optional[int] = self.sigmas_interpol[step_index]
lowerCAmelCase_ :Union[str, Any] = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
lowerCAmelCase_ :Dict = 0
lowerCAmelCase_ :List[Any] = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
lowerCAmelCase_ :Tuple = sigma_hat if self.state_in_first_order else sigma_interpol
lowerCAmelCase_ :str = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
lowerCAmelCase_ :Any = sigma_hat if self.state_in_first_order else sigma_interpol
lowerCAmelCase_ :List[Any] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
lowerCAmelCase_ :int = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
lowerCAmelCase_ :List[str] = sigma_interpol - sigma_hat
# store for 2nd order step
lowerCAmelCase_ :Dict = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
lowerCAmelCase_ :List[str] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
lowerCAmelCase_ :Optional[int] = sigma_next - sigma_hat
lowerCAmelCase_ :Optional[int] = self.sample
lowerCAmelCase_ :List[str] = None
lowerCAmelCase_ :Optional[Any] = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def __lowerCAmelCase ( self , __A , __A , __A , ) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
lowerCAmelCase_ :str = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(__A ):
# mps does not support float64
lowerCAmelCase_ :Any = self.timesteps.to(original_samples.device , dtype=torch.floataa )
lowerCAmelCase_ :Optional[int] = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
lowerCAmelCase_ :Tuple = self.timesteps.to(original_samples.device )
lowerCAmelCase_ :int = timesteps.to(original_samples.device )
lowerCAmelCase_ :Dict = [self.index_for_timestep(__A , __A ) for t in timesteps]
lowerCAmelCase_ :Union[str, Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
lowerCAmelCase_ :str = sigma.unsqueeze(-1 )
lowerCAmelCase_ :Any = original_samples + noise * sigma
return noisy_samples
def __len__( self ) -> List[str]:
return self.config.num_train_timesteps
| 84 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__A, '''tf_padding''' ) )
self.parent.assertTrue(hasattr(__A, '''depth_multiplier''' ) )
class __UpperCAmelCase :
def __init__( self : int, __A : List[Any], __A : str=1_3, __A : Dict=3, __A : int=3_2, __A : int=0.2_5, __A : List[str]=8, __A : int=8, __A : Dict=6, __A : str=3_2, __A : Any=True, __A : str=True, __A : int=True, __A : Union[str, Any]="relu6", __A : Any=1_2_8_0, __A : List[Any]=0.1, __A : Optional[Any]=0.0_2, __A : Tuple=True, __A : List[Any]=True, __A : str=1_0, __A : Optional[Any]=None, ):
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : List[str] = batch_size
UpperCAmelCase : List[str] = num_channels
UpperCAmelCase : str = image_size
UpperCAmelCase : Optional[int] = depth_multiplier
UpperCAmelCase : Union[str, Any] = depth_divisible_by
UpperCAmelCase : Optional[Any] = min_depth
UpperCAmelCase : List[str] = expand_ratio
UpperCAmelCase : Dict = tf_padding
UpperCAmelCase : str = output_stride
UpperCAmelCase : Union[str, Any] = first_layer_is_expansion
UpperCAmelCase : List[Any] = finegrained_output
UpperCAmelCase : Optional[Any] = hidden_act
UpperCAmelCase : str = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
UpperCAmelCase : Optional[Any] = classifier_dropout_prob
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : List[str] = is_training
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : Any = scope
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Dict = None
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Dict = ids_tensor([self.batch_size], self.num_labels )
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
UpperCAmelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : Any ):
return MobileNetVaConfig(
num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, depth_divisible_by=self.depth_divisible_by, min_depth=self.min_depth, expand_ratio=self.expand_ratio, output_stride=self.output_stride, first_layer_is_expansion=self.first_layer_is_expansion, finegrained_output=self.finegrained_output, hidden_act=self.hidden_act, tf_padding=self.tf_padding, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def __magic_name__ ( self : List[Any], __A : Dict, __A : Optional[Any], __A : Optional[int], __A : Union[str, Any] ):
UpperCAmelCase : Any = MobileNetVaModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[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,
), )
self.parent.assertEqual(
result.pooler_output.shape, (self.batch_size, self.last_hidden_size), )
def __magic_name__ ( self : str, __A : Union[str, Any], __A : Dict, __A : Optional[Any], __A : str ):
UpperCAmelCase : Optional[int] = self.num_labels
UpperCAmelCase : Any = MobileNetVaForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[int] = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any], __A : Optional[Any], __A : List[str], __A : Dict, __A : Dict ):
UpperCAmelCase : Tuple = self.num_labels
UpperCAmelCase : Dict = MobileNetVaForSemanticSegmentation(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = 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,
), )
UpperCAmelCase : Optional[Any] = 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 __magic_name__ ( self : Tuple ):
UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = config_and_inputs
UpperCAmelCase : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
"""feature-extraction""": MobileNetVaModel,
"""image-classification""": MobileNetVaForImageClassification,
"""image-segmentation""": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = MobileNetVaModelTester(self )
UpperCAmelCase : List[Any] = MobileNetVaConfigTester(self, config_class=__A, has_text_modality=__A )
def __magic_name__ ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''' )
def __magic_name__ ( self : Tuple ):
pass
@unittest.skip(reason='''MobileNetV2 does not output attentions''' )
def __magic_name__ ( self : Any ):
pass
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(__A )
UpperCAmelCase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : int ):
def check_hidden_states_output(__A : Any, __A : Optional[Any], __A : str ):
UpperCAmelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
UpperCAmelCase : Dict = model(**self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Optional[Any] = outputs.hidden_states
UpperCAmelCase : List[Any] = 1_6
self.assertEqual(len(__A ), __A )
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : int ):
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__A )
@slow
def __magic_name__ ( self : Dict ):
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] = MobileNetVaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> int:
UpperCAmelCase : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[Any] ):
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''' ) if is_vision_available() else None
)
@slow
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : List[Any] = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''' ).to(__A )
UpperCAmelCase : Optional[int] = self.default_image_processor
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : Dict = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : str = model(**__A )
# verify the logits
UpperCAmelCase : int = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Tuple = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = model.to(__A )
UpperCAmelCase : Tuple = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = prepare_img()
UpperCAmelCase : int = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Union[str, Any] = model(**__A )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify the logits
UpperCAmelCase : Tuple = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor(
[
[[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
[[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
[[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
], device=__A, )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], __A, atol=1E-4 ) )
| 336 | 0 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def UpperCamelCase_( snake_case : Callable ):
'''simple docstring'''
@wraps(snake_case )
def _inner_fn(*snake_case : Optional[int] , **snake_case : List[Any] ):
warnings.warn(
(f'\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.') , snake_case , )
return fn(*snake_case , **snake_case )
return _inner_fn
| 85 |
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 : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """codegen"""
UpperCamelCase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any, __A : Optional[int]=5_0_4_0_0, __A : Tuple=2_0_4_8, __A : Optional[int]=2_0_4_8, __A : List[str]=4_0_9_6, __A : List[str]=2_8, __A : Union[str, Any]=1_6, __A : Tuple=6_4, __A : Union[str, Any]=None, __A : Union[str, Any]="gelu_new", __A : Any=0.0, __A : Dict=0.0, __A : str=0.0, __A : Optional[int]=1E-5, __A : Any=0.0_2, __A : Any=True, __A : Union[str, Any]=5_0_2_5_6, __A : List[str]=5_0_2_5_6, __A : int=False, **__A : List[Any], ):
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Tuple = n_ctx
UpperCAmelCase : Tuple = n_positions
UpperCAmelCase : Optional[int] = n_embd
UpperCAmelCase : Union[str, Any] = n_layer
UpperCAmelCase : List[str] = n_head
UpperCAmelCase : Tuple = n_inner
UpperCAmelCase : int = rotary_dim
UpperCAmelCase : List[Any] = activation_function
UpperCAmelCase : List[str] = resid_pdrop
UpperCAmelCase : Optional[Any] = embd_pdrop
UpperCAmelCase : str = attn_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : Any = bos_token_id
UpperCAmelCase : List[str] = eos_token_id
super().__init__(
bos_token_id=__A, eos_token_id=__A, tie_word_embeddings=__A, **__A )
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Any, __A : PretrainedConfig, __A : str = "default", __A : List[PatchingSpec] = None, __A : bool = False, ):
super().__init__(__A, task=__A, patching_specs=__A, use_past=__A )
if not getattr(self._config, '''pad_token_id''', __A ):
# TODO: how to do that better?
UpperCAmelCase : Union[str, Any] = 0
@property
def __magic_name__ ( self : str ):
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__A, direction='''inputs''' )
UpperCAmelCase : int = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase : List[Any] = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __magic_name__ ( self : Dict ):
return self._config.n_layer
@property
def __magic_name__ ( self : List[str] ):
return self._config.n_head
def __magic_name__ ( self : str, __A : PreTrainedTokenizer, __A : int = -1, __A : int = -1, __A : bool = False, __A : Optional[TensorType] = None, ):
UpperCAmelCase : Union[str, Any] = super(__A, self ).generate_dummy_inputs(
__A, batch_size=__A, seq_length=__A, is_pair=__A, framework=__A )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase : Union[str, 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
UpperCAmelCase , UpperCAmelCase : str = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase : str = seqlen + 2
UpperCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase : Optional[int] = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
UpperCAmelCase : Union[str, Any] = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase : Optional[Any] = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase : Dict = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__A, __A, dtype=__A )], dim=1 )
return ordered_inputs
@property
def __magic_name__ ( self : Tuple ):
return 1_3
| 336 | 0 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
lowerCamelCase__ = """1"""
lowerCamelCase__ = """0"""
lowerCamelCase__ = """1"""
lowerCamelCase__ = ort.SessionOptions()
lowerCamelCase__ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
lowerCamelCase__ = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
lowerCamelCase__ = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase__ = ort.RunOptions()
lowerCamelCase__ = 128
lowerCamelCase__ = 1
lowerCamelCase__ = np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase__ = np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase__ = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
lowerCamelCase__ = time.time()
lowerCamelCase__ = 2_000
lowerCamelCase__ = {}
for iter in range(max_iters):
lowerCamelCase__ = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1_000 / max_iters)) | 86 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 336 | 0 |
# flake8: noqa
# Lint as: python3
UpperCamelCase = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 87 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCAmelCase :
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
@classmethod
def __magic_name__ ( cls : Any ):
return cls()
@dataclass
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@property
def __magic_name__ ( self : Optional[int] ):
return True
@register_to_config
def __init__( self : Optional[int], __A : float = 0.0_2, __A : float = 1_0_0, __A : float = 1.0_0_7, __A : float = 8_0, __A : float = 0.0_5, __A : float = 5_0, ):
pass
def __magic_name__ ( self : Optional[Any] ):
return KarrasVeSchedulerState.create()
def __magic_name__ ( self : int, __A : KarrasVeSchedulerState, __A : int, __A : Tuple = () ):
UpperCAmelCase : Optional[Any] = jnp.arange(0, __A )[::-1].copy()
UpperCAmelCase : Union[str, Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A, schedule=jnp.array(__A, dtype=jnp.floataa ), timesteps=__A, )
def __magic_name__ ( self : List[Any], __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : random.KeyArray, ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase : int = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1 )
else:
UpperCAmelCase : Optional[int] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase : Union[str, Any] = random.split(__A, num=1 )
UpperCAmelCase : List[str] = self.config.s_noise * random.normal(key=__A, shape=sample.shape )
UpperCAmelCase : Tuple = sigma + gamma * sigma
UpperCAmelCase : List[str] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : int = sample_hat + sigma_hat * model_output
UpperCAmelCase : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : jnp.ndarray, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
UpperCAmelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Optional[Any], __A : KarrasVeSchedulerState, __A : Optional[int], __A : int, __A : Union[str, Any] ):
raise NotImplementedError()
| 336 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
__lowerCAmelCase : Tuple = 'facebook/wmt19-en-de'
__lowerCAmelCase : Union[str, Any] = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase : Dict = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
__lowerCAmelCase : List[str] = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
__lowerCAmelCase : Optional[int] = tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase : List[str] = tiny_model(**batch)
print('test output:', len(outputs.logits[0]))
# Save
__lowerCAmelCase : Any = 'tiny-wmt19-en-de'
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 88 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCAmelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def a__ ( ) -> Dict:
if os.name == "nt":
UpperCAmelCase : List[str] = CursorInfo()
UpperCAmelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def a__ ( ) -> Optional[int]:
if os.name == "nt":
UpperCAmelCase : int = CursorInfo()
UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Any = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def a__ ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 336 | 0 |
'''simple docstring'''
import math
def __lowerCamelCase ( lowerCAmelCase_ ) -> int:
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_a : Tuple = f"""Input value of [number={number}] must be an integer"""
raise TypeError(lowerCAmelCase_ )
if number < 1:
_a : List[Any] = f"""Input value of [number={number}] must be > 0"""
raise ValueError(lowerCAmelCase_ )
elif number == 1:
return 3
elif number == 2:
return 5
else:
_a : List[Any] = int(math.log(number // 3 , 2 ) ) + 2
_a : str = [3, 5]
_a : Optional[int] = 2
_a : Union[str, Any] = 3
for block in range(1 , lowerCAmelCase_ ):
for _ in range(lowerCAmelCase_ ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
__lowerCAmelCase = 0
try:
__lowerCAmelCase = proth(number)
except ValueError:
print(f"""ValueError: there is no {number}th Proth number""")
continue
print(f"""The {number}th Proth number: {value}""")
| 89 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.08_21 * temperature) / (volume) ) )
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.08_21 * temperature) / (pressure) ) )
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
return round(float((pressure * volume) / (0.08_21 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 90 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
"""simple docstring"""
from copy import deepcopy
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : int , lowercase_ : list[int] | None = None , lowercase_ : int | None = None):
'''simple docstring'''
if arr is None and size is not None:
SCREAMING_SNAKE_CASE_ : str = size
SCREAMING_SNAKE_CASE_ : Tuple = [0] * size
elif arr is not None:
self.init(lowercase_)
else:
raise ValueError('''Either arr or size must be specified''')
def _SCREAMING_SNAKE_CASE ( self : int , lowercase_ : list[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = len(lowercase_)
SCREAMING_SNAKE_CASE_ : List[str] = deepcopy(lowercase_)
for i in range(1 , self.size):
SCREAMING_SNAKE_CASE_ : List[Any] = self.next_(lowercase_)
if j < self.size:
self.tree[j] += self.tree[i]
def _SCREAMING_SNAKE_CASE ( self : Any):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = self.tree[:]
for i in range(self.size - 1 , 0 , -1):
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.next_(lowercase_)
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowercase_ : int):
'''simple docstring'''
return index + (index & (-index))
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowercase_ : int):
'''simple docstring'''
return index - (index & (-index))
def _SCREAMING_SNAKE_CASE ( self : Any , lowercase_ : int , lowercase_ : int):
'''simple docstring'''
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
SCREAMING_SNAKE_CASE_ : Dict = self.next_(lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Any , lowercase_ : int , lowercase_ : int):
'''simple docstring'''
self.add(lowercase_ , value - self.get(lowercase_))
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowercase_ : int):
'''simple docstring'''
if right == 0:
return 0
SCREAMING_SNAKE_CASE_ : List[str] = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.prev(lowercase_)
return result
def _SCREAMING_SNAKE_CASE ( self : int , lowercase_ : int , lowercase_ : int):
'''simple docstring'''
return self.prefix(lowercase_) - self.prefix(lowercase_)
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowercase_ : int):
'''simple docstring'''
return self.query(lowercase_ , index + 1)
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowercase_ : int):
'''simple docstring'''
value -= self.tree[0]
if value < 0:
return -1
SCREAMING_SNAKE_CASE_ : Optional[int] = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
SCREAMING_SNAKE_CASE_ : Any = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 91 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( UpperCAmelCase : Dict=None ) -> Optional[int]:
if subparsers is not None:
UpperCAmelCase : Tuple = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase : Dict = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase : Optional[int] = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=UpperCAmelCase , default=UpperCAmelCase , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=UpperCAmelCase , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=UpperCAmelCase , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase : Union[str, Any] = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=UpperCAmelCase , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase )
return parser
def a__ ( UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase : List[str] = defaults.commands
if not args.tpu_name:
UpperCAmelCase : Tuple = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase : int = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase : Tuple = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase : Dict = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , UpperCAmelCase ):
UpperCAmelCase : Optional[int] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , UpperCAmelCase ):
UpperCAmelCase : int = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase : Optional[int] = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
UpperCAmelCase : int = '''; '''.join(UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase : Any = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(UpperCAmelCase )}''' )
return
subprocess.run(UpperCAmelCase )
print('''Successfully setup pod.''' )
def a__ ( ) -> Any:
UpperCAmelCase : Any = tpu_command_parser()
UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(UpperCAmelCase )
| 336 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
"""configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""AlbertTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""AlbertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AlbertForMaskedLM""",
"""AlbertForMultipleChoice""",
"""AlbertForPreTraining""",
"""AlbertForQuestionAnswering""",
"""AlbertForSequenceClassification""",
"""AlbertForTokenClassification""",
"""AlbertModel""",
"""AlbertPreTrainedModel""",
"""load_tf_weights_in_albert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFAlbertForMaskedLM""",
"""TFAlbertForMultipleChoice""",
"""TFAlbertForPreTraining""",
"""TFAlbertForQuestionAnswering""",
"""TFAlbertForSequenceClassification""",
"""TFAlbertForTokenClassification""",
"""TFAlbertMainLayer""",
"""TFAlbertModel""",
"""TFAlbertPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""FlaxAlbertForMaskedLM""",
"""FlaxAlbertForMultipleChoice""",
"""FlaxAlbertForPreTraining""",
"""FlaxAlbertForQuestionAnswering""",
"""FlaxAlbertForSequenceClassification""",
"""FlaxAlbertForTokenClassification""",
"""FlaxAlbertModel""",
"""FlaxAlbertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : str = torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
lowercase_ : Union[str, Any] = get_activation('''gelu''' )
self.assertTrue(torch.allclose(gelu_python(__SCREAMING_SNAKE_CASE ) , torch_builtin(__SCREAMING_SNAKE_CASE ) ) )
self.assertFalse(torch.allclose(gelu_python(__SCREAMING_SNAKE_CASE ) , gelu_new(__SCREAMING_SNAKE_CASE ) ) )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[Any] = torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
lowercase_ : Tuple = get_activation('''gelu''' )
lowercase_ : Any = get_activation('''gelu_10''' )
lowercase_ : List[str] = torch_builtin(__SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = geluaa(__SCREAMING_SNAKE_CASE )
lowercase_ : Dict = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(__SCREAMING_SNAKE_CASE ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def _snake_case ( self ):
"""simple docstring"""
get_activation('''gelu''' )
get_activation('''gelu_10''' )
get_activation('''gelu_fast''' )
get_activation('''gelu_new''' )
get_activation('''gelu_python''' )
get_activation('''gelu_pytorch_tanh''' )
get_activation('''linear''' )
get_activation('''mish''' )
get_activation('''quick_gelu''' )
get_activation('''relu''' )
get_activation('''sigmoid''' )
get_activation('''silu''' )
get_activation('''swish''' )
get_activation('''tanh''' )
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
get_activation('''bogus''' )
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
get_activation(__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : int = get_activation('''gelu''' )
lowercase_ : Any = 1
lowercase_ : str = get_activation('''gelu''' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
lowercase_ : Dict = acta.a
| 93 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __UpperCAmelCase ( lowerCamelCase__ ):
def __get__( self : Tuple, __A : Optional[Any], __A : Optional[int]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
UpperCAmelCase : str = '''__cached_''' + self.fget.__name__
UpperCAmelCase : int = getattr(__A, __A, __A )
if cached is None:
UpperCAmelCase : Any = self.fget(__A )
setattr(__A, __A, __A )
return cached
def a__ ( UpperCAmelCase : Optional[Any] ) -> Any:
UpperCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_torch_fx_proxy(UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(UpperCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : str ) -> Tuple:
return _is_numpy(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
import torch
return isinstance(UpperCAmelCase , torch.Tensor )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
return False if not is_torch_available() else _is_torch(UpperCAmelCase )
def a__ ( UpperCAmelCase : Tuple ) -> List[str]:
import torch
return isinstance(UpperCAmelCase , torch.device )
def a__ ( UpperCAmelCase : Any ) -> Any:
return False if not is_torch_available() else _is_torch_device(UpperCAmelCase )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
import torch
if isinstance(UpperCAmelCase , UpperCAmelCase ):
if hasattr(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
else:
return False
return isinstance(UpperCAmelCase , torch.dtype )
def a__ ( UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> str:
import tensorflow as tf
return isinstance(UpperCAmelCase , tf.Tensor )
def a__ ( UpperCAmelCase : int ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCAmelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(UpperCAmelCase )
return type(UpperCAmelCase ) == tf.Tensor
def a__ ( UpperCAmelCase : int ) -> List[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[Any] ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCAmelCase , jnp.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Optional[int]:
return False if not is_flax_available() else _is_jax(UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Tuple:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return [to_py_obj(UpperCAmelCase ) for o in obj]
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase ).tolist()
elif isinstance(UpperCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( UpperCAmelCase : Any ) -> List[str]:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_numpy(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return np.array(UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase )
else:
return obj
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(__A ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
UpperCAmelCase : int = getattr(self, class_fields[0].name )
UpperCAmelCase : str = all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__A ):
if isinstance(__A, __A ):
UpperCAmelCase : Tuple = first_field.items()
UpperCAmelCase : Any = True
else:
try:
UpperCAmelCase : Optional[Any] = iter(__A )
UpperCAmelCase : Optional[Any] = True
except TypeError:
UpperCAmelCase : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__A ):
if (
not isinstance(__A, (list, tuple) )
or not len(__A ) == 2
or not isinstance(element[0], __A )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
UpperCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
UpperCAmelCase : Union[str, Any] = element[1]
elif first_field is not None:
UpperCAmelCase : Union[str, Any] = first_field
else:
for field in class_fields:
UpperCAmelCase : Optional[Any] = getattr(self, field.name )
if v is not None:
UpperCAmelCase : Optional[int] = v
def __delitem__( self : Union[str, Any], *__A : str, **__A : Tuple ):
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : List[str], *__A : Union[str, Any], **__A : Optional[Any] ):
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Any, *__A : Dict, **__A : str ):
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Dict, *__A : int, **__A : Dict ):
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : List[str], __A : List[str] ):
if isinstance(__A, __A ):
UpperCAmelCase : int = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[Any], __A : Dict, __A : Union[str, Any] ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__A, __A )
super().__setattr__(__A, __A )
def __setitem__( self : Dict, __A : List[Any], __A : Union[str, Any] ):
# Will raise a KeyException if needed
super().__setitem__(__A, __A )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__A, __A )
def __magic_name__ ( self : List[str] ):
return tuple(self[k] for k in self.keys() )
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@classmethod
def __magic_name__ ( cls : List[Any], __A : Tuple ):
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """longest"""
UpperCamelCase = """max_length"""
UpperCamelCase = """do_not_pad"""
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
UpperCamelCase = """np"""
UpperCamelCase = """jax"""
class __UpperCAmelCase :
def __init__( self : Any, __A : List[ContextManager] ):
UpperCAmelCase : Tuple = context_managers
UpperCAmelCase : Tuple = ExitStack()
def __enter__( self : Any ):
for context_manager in self.context_managers:
self.stack.enter_context(__A )
def __exit__( self : List[Any], *__A : Union[str, Any], **__A : Dict ):
self.stack.__exit__(*__A, **__A )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : int = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a__ ( UpperCAmelCase : Dict ) -> Any:
UpperCAmelCase : List[Any] = model_class.__name__
UpperCAmelCase : Union[str, Any] = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a__ ( UpperCAmelCase : MutableMapping , UpperCAmelCase : str = "" , UpperCAmelCase : str = "." ) -> Union[str, Any]:
def _flatten_dict(UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str]="" , UpperCAmelCase : Any="." ):
for k, v in d.items():
UpperCAmelCase : List[str] = str(UpperCAmelCase ) + delimiter + str(UpperCAmelCase ) if parent_key else k
if v and isinstance(UpperCAmelCase , UpperCAmelCase ):
yield from flatten_dict(UpperCAmelCase , UpperCAmelCase , delimiter=UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
@contextmanager
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : bool = False ) -> Optional[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if is_numpy_array(UpperCAmelCase ):
return np.transpose(UpperCAmelCase , axes=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.T if axes is None else array.permute(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(UpperCAmelCase , perm=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.transpose(UpperCAmelCase , axes=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Optional[int] ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.reshape(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.reshape(UpperCAmelCase , UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=None ) -> Any:
if is_numpy_array(UpperCAmelCase ):
return np.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> str:
if is_numpy_array(UpperCAmelCase ):
return np.expand_dims(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.unsqueeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.size(UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.size(UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Dict:
for key, value in auto_map.items():
if isinstance(UpperCAmelCase , (tuple, list) ):
UpperCAmelCase : List[Any] = [f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
UpperCAmelCase : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a__ ( UpperCAmelCase : Tuple ) -> Union[str, Any]:
for base_class in inspect.getmro(UpperCAmelCase ):
UpperCAmelCase : Any = base_class.__module__
UpperCAmelCase : Dict = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 336 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Any = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'vit_msn'
def __init__( self , _lowerCamelCase=768 , _lowerCamelCase=12 , _lowerCamelCase=12 , _lowerCamelCase=3072 , _lowerCamelCase="gelu" , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.02 , _lowerCamelCase=1e-06 , _lowerCamelCase=224 , _lowerCamelCase=16 , _lowerCamelCase=3 , _lowerCamelCase=True , **_lowerCamelCase , ):
super().__init__(**_lowerCamelCase )
a :List[Any] = hidden_size
a :Dict = num_hidden_layers
a :Union[str, Any] = num_attention_heads
a :Tuple = intermediate_size
a :str = hidden_act
a :Dict = hidden_dropout_prob
a :List[Any] = attention_probs_dropout_prob
a :Optional[int] = initializer_range
a :str = layer_norm_eps
a :Union[str, Any] = image_size
a :int = patch_size
a :str = num_channels
a :Optional[Any] = qkv_bias
| 94 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LayoutLMTokenizer
UpperCamelCase = LayoutLMTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
UpperCAmelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : int = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __magic_name__ ( self : Union[str, Any], **__A : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], __A : int ):
UpperCAmelCase : Optional[Any] = '''UNwant\u00E9d,running'''
UpperCAmelCase : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [7, 4, 5, 1_0, 8, 9] )
def __magic_name__ ( self : Optional[int] ):
pass
| 336 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : Tuple = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Union[str, Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
UpperCAmelCase : int = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
UpperCAmelCase : List[Any] = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
UpperCAmelCase : str = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
UpperCAmelCase : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 95 |
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 __UpperCAmelCase :
def __init__( self : Any, __A : str, __A : Dict=1_3, __A : int=3_0, __A : Tuple=2, __A : Union[str, Any]=3, __A : Any=True, __A : str=True, __A : Dict=3_2, __A : List[Any]=2, __A : Optional[Any]=4, __A : Union[str, Any]=3_7, __A : int="gelu", __A : int=0.1, __A : List[Any]=0.1, __A : Tuple=1_0, __A : Tuple=0.0_2, __A : Any=3, __A : List[str]=0.6, __A : Any=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Optional[int] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Tuple = mask_ratio
UpperCAmelCase : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Optional[Any] ):
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=__A, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def __magic_name__ ( self : str, __A : List[Any], __A : Any, __A : Any ):
UpperCAmelCase : Optional[Any] = TFViTMAEModel(config=__A )
UpperCAmelCase : Tuple = model(__A, training=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : str, __A : int, __A : str ):
UpperCAmelCase : Dict = TFViTMAEForPreTraining(__A )
UpperCAmelCase : int = model(__A, training=__A )
# expected sequence length = num_patches
UpperCAmelCase : int = (self.image_size // self.patch_size) ** 2
UpperCAmelCase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase : Tuple = 1
UpperCAmelCase : List[Any] = TFViTMAEForPreTraining(__A )
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = model(__A, training=__A )
UpperCAmelCase : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , 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 __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = TFViTMAEModelTester(self )
UpperCAmelCase : int = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer) )
UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, tf.keras.layers.Layer ) )
def __magic_name__ ( self : str ):
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Any = model_class(__A )
UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : int = [*signature.parameters.keys()]
UpperCAmelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __magic_name__ ( self : int ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : str = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : Dict = model(__A, noise=__A )
UpperCAmelCase : Any = copy.deepcopy(self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Union[str, Any] = model(**__A, noise=__A )
UpperCAmelCase : Dict = outputs_dict[0].numpy()
UpperCAmelCase : Tuple = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ), 1E-6 )
def __magic_name__ ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__A : Union[str, Any] ):
UpperCAmelCase : str = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__A ):
UpperCAmelCase : Tuple = v.numpy()
else:
UpperCAmelCase : str = np.array(__A )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : Any = self._prepare_for_class(__A, __A )
UpperCAmelCase : Optional[int] = prepare_numpy_arrays(__A )
UpperCAmelCase : str = model(__A, noise=__A )
UpperCAmelCase : str = model(**__A, noise=__A )
self.assert_outputs_same(__A, __A )
def __magic_name__ ( self : int, __A : str, __A : Union[str, Any], __A : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase : Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : int = tf.constant(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase : List[Any] = tf_noise
super().check_pt_tf_models(__A, __A, __A )
def __magic_name__ ( self : str ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Union[str, Any] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__A )
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(__A, __A ),)
if isinstance(__A, __A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__A, '''_keras_serializable''', __A )
}
UpperCAmelCase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : str = tf.convert_to_tensor(__A )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCAmelCase : Tuple = main_layer_class(__A )
UpperCAmelCase : int = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCAmelCase : List[Any] = tf.keras.Model(__A, outputs=main_layer(__A ) )
UpperCAmelCase : List[Any] = model(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = os.path.join(__A, '''keras_model.h5''' )
model.save(__A )
UpperCAmelCase : List[str] = tf.keras.models.load_model(
__A, custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__A, tf.keras.Model )
UpperCAmelCase : Tuple = model(__A )
self.assert_outputs_same(__A, __A )
@slow
def __magic_name__ ( self : Dict ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(__A )
UpperCAmelCase : List[str] = self._prepare_for_class(__A, __A )
UpperCAmelCase : Union[str, Any] = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : Optional[int] = outputs.last_hidden_state.numpy()
UpperCAmelCase : Union[str, Any] = 0
else:
UpperCAmelCase : Optional[int] = outputs.logits.numpy()
UpperCAmelCase : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A, saved_model=__A )
UpperCAmelCase : Dict = model_class.from_pretrained(__A )
UpperCAmelCase : str = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : int = after_outputs['''last_hidden_state'''].numpy()
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Any = after_outputs['''logits'''].numpy()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A, 1E-5 )
def __magic_name__ ( self : Optional[Any] ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : List[Any] = model(__A, noise=__A )
UpperCAmelCase : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__A )
UpperCAmelCase : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCAmelCase : str = model_class.from_config(model.config )
UpperCAmelCase : List[str] = new_model(__A ) # Build model
new_model.set_weights(model.get_weights() )
UpperCAmelCase : Tuple = new_model(__A, noise=__A )
self.assert_outputs_same(__A, __A )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def __magic_name__ ( self : Tuple ):
pass
@slow
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(__A )
def a__ ( ) -> Dict:
UpperCAmelCase : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[str] ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase : Tuple = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
UpperCAmelCase : List[str] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : str = image_processor(images=__A, 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)
UpperCAmelCase : Optional[int] = ViTMAEConfig()
UpperCAmelCase : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCAmelCase : Optional[int] = model(**__A, noise=__A )
# verify the logits
UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3], __A, atol=1E-4 )
| 336 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["""NllbTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["""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
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 96 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_lowerCamelCase : List[Any] = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
_lowerCamelCase : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 336 | 0 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__snake_case = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
__snake_case = {
'''facebook/blenderbot_small-90M''': 512,
}
class lowercase ( A__ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = BlenderbotSmallTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_="<|endoftext|>" , UpperCamelCase_=False , UpperCamelCase_=True , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=UpperCamelCase_ , merges=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ , ) , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , **UpperCamelCase_ , )
UpperCamelCase__ :Union[str, Any] = add_prefix_space
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = [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 lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = [self.sep_token_id]
UpperCamelCase__ :Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 97 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] for _ in range(UpperCAmelCase )]
UpperCAmelCase : Union[str, Any] = run_maze(UpperCAmelCase , 0 , 0 , UpperCAmelCase )
if solved:
print('''\n'''.join(str(UpperCAmelCase ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def a__ ( UpperCAmelCase : list[list[int]] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Dict = len(UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase : Dict = 1
return True
UpperCAmelCase : Union[str, Any] = (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 : Any = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase : str = 1
# check for directions
if (
run_maze(UpperCAmelCase , i + 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j + 1 , UpperCAmelCase )
or run_maze(UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j - 1 , UpperCAmelCase )
):
return True
UpperCAmelCase : Any = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
"""simple docstring"""
class snake_case :
"""simple docstring"""
def __init__( self : str ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Tuple ):
UpperCAmelCase__ = name
UpperCAmelCase__ = value
UpperCAmelCase__ = weight
def __repr__( self : List[Any] ):
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def __lowerCAmelCase ( self : Tuple ):
return self.value
def __lowerCAmelCase ( self : Tuple ):
return self.name
def __lowerCAmelCase ( self : Optional[int] ):
return self.weight
def __lowerCAmelCase ( self : int ):
return self.value / self.weight
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = []
for i in range(len(lowerCamelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = sorted(lowerCamelCase , key=lowerCamelCase , reverse=lowerCamelCase )
UpperCAmelCase__ = []
UpperCAmelCase__ , UpperCAmelCase__ = 0.0, 0.0
for i in range(len(lowerCamelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def a_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 98 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __UpperCAmelCase :
def __init__( self : List[Any], __A : List[str], __A : List[str]=1_3, __A : Any=6_4, __A : Optional[Any]=2, __A : str=3, __A : str=True, __A : str=True, __A : Optional[Any]=3_2, __A : List[str]=5, __A : int=4, __A : str=3_7, __A : str="gelu", __A : Dict=0.1, __A : List[Any]=0.1, __A : Dict=1_0, __A : int=0.0_2, __A : Any=[1, 1_6, 4, 4], __A : Optional[int]=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Any = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : List[str] = patch_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase : str = type_sequence_label_size
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : int = scope
UpperCAmelCase : List[str] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
UpperCAmelCase : str = (self.image_size // 3_2) ** 2
UpperCAmelCase : List[str] = num_patches + 1
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : str = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Any ):
UpperCAmelCase : Dict = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 1_6, 3_2],
'''num_groups''': 2,
}
return ViTHybridConfig(
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, backbone_featmap_shape=self.backbone_featmap_shape, backbone_config=__A, )
def __magic_name__ ( self : Optional[int], __A : Optional[int], __A : int, __A : Tuple ):
UpperCAmelCase : int = ViTHybridModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Tuple = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : Dict, __A : str, __A : List[str] ):
UpperCAmelCase : str = self.type_sequence_label_size
UpperCAmelCase : List[Any] = ViTHybridForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : int ):
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = config_and_inputs
UpperCAmelCase : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
UpperCamelCase = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Any = ViTHybridModelTester(self )
UpperCAmelCase : List[Any] = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : int ):
UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, nn.Linear ) )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(__A )
UpperCAmelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = _config_zero_init(__A )
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(config=__A )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
UpperCAmelCase : Union[str, Any] = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@slow
def __magic_name__ ( self : List[str] ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Union[str, Any] = ViTHybridModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> Tuple:
UpperCAmelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : str ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__A )
UpperCAmelCase : Tuple = self.default_image_processor
UpperCAmelCase : int = prepare_img()
UpperCAmelCase : Union[str, Any] = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(**__A )
# verify the logits
UpperCAmelCase : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Optional[Any] = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
@require_accelerate
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''', device_map='''auto''' )
UpperCAmelCase : Tuple = prepare_img()
UpperCAmelCase : Optional[int] = image_processor(images=__A, return_tensors='''pt''' )
UpperCAmelCase : Dict = model(**__A )
UpperCAmelCase : Any = outputs.logits
# model predicts one of the 1000 ImageNet classes
UpperCAmelCase : Dict = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx], '''tabby, tabby cat''' )
| 336 | 0 |
from __future__ import annotations
import queue
class A__ :
"""simple docstring"""
def __init__( self , lowercase) -> Any:
'''simple docstring'''
a__ : List[str] = data
a__ : Tuple = None
a__ : int = None
def A_ ( ) -> TreeNode:
print('\n********Press N to stop entering at any point of time********\n' )
a__ : Tuple = input('Enter the value of the root node: ' ).strip().lower()
a__ : queue.Queue = queue.Queue()
a__ : List[Any] = TreeNode(int(A__ ) )
q.put(A__ )
while not q.empty():
a__ : int = q.get()
a__ : Optional[Any] = F'Enter the left node of {node_found.data}: '
a__ : List[Any] = input(A__ ).strip().lower() or 'n'
if check == "n":
return tree_node
a__ : Tuple = TreeNode(int(A__ ) )
a__ : Union[str, Any] = left_node
q.put(A__ )
a__ : List[str] = F'Enter the right node of {node_found.data}: '
a__ : str = input(A__ ).strip().lower() or 'n'
if check == "n":
return tree_node
a__ : int = TreeNode(int(A__ ) )
a__ : List[Any] = right_node
q.put(A__ )
raise
def A_ ( A__ ) -> None:
if not isinstance(A__ , A__ ) or not node:
return
print(node.data , end=',' )
pre_order(node.left )
pre_order(node.right )
def A_ ( A__ ) -> None:
if not isinstance(A__ , A__ ) or not node:
return
in_order(node.left )
print(node.data , end=',' )
in_order(node.right )
def A_ ( A__ ) -> None:
if not isinstance(A__ , A__ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=',' )
def A_ ( A__ ) -> None:
if not isinstance(A__ , A__ ) or not node:
return
a__ : queue.Queue = queue.Queue()
q.put(A__ )
while not q.empty():
a__ : Optional[Any] = q.get()
print(node_dequeued.data , end=',' )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def A_ ( A__ ) -> None:
if not isinstance(A__ , A__ ) or not node:
return
a__ : queue.Queue = queue.Queue()
q.put(A__ )
while not q.empty():
a__ : Dict = []
while not q.empty():
a__ : Optional[int] = q.get()
print(node_dequeued.data , end=',' )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(A__ )
def A_ ( A__ ) -> None:
if not isinstance(A__ , A__ ) or not node:
return
a__ : list[TreeNode] = []
a__ : Any = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=',' )
stack.append(A__ )
a__ : Union[str, Any] = n.left
# end of while means current node doesn't have left child
a__ : int = stack.pop()
# start to traverse its right child
a__ : Tuple = n.right
def A_ ( A__ ) -> None:
if not isinstance(A__ , A__ ) or not node:
return
a__ : list[TreeNode] = []
a__ : Tuple = node
while n or stack:
while n:
stack.append(A__ )
a__ : Optional[int] = n.left
a__ : Any = stack.pop()
print(n.data , end=',' )
a__ : List[str] = n.right
def A_ ( A__ ) -> None:
if not isinstance(A__ , A__ ) or not node:
return
a__ , a__ : Optional[Any] = [], []
a__ : str = node
stacka.append(A__ )
while stacka: # to find the reversed order of post order, store it in stack2
a__ : List[Any] = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(A__ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=',' )
def A_ ( A__ = "" , A__=50 , A__="*" ) -> str:
if not s:
return "\n" + width * char
a__ , a__ : Dict = divmod(width - len(A__ ) - 2 , 2 )
return F'{left * char} {s} {(left + extra) * char}'
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt("""Binary Tree Traversals"""))
lowercase : TreeNode = build_tree()
print(prompt("""Pre Order Traversal"""))
pre_order(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal"""))
in_order(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal"""))
post_order(node)
print(prompt() + """\n""")
print(prompt("""Level Order Traversal"""))
level_order(node)
print(prompt() + """\n""")
print(prompt("""Actual Level Order Traversal"""))
level_order_actual(node)
print("""*""" * 5_0 + """\n""")
print(prompt("""Pre Order Traversal - Iteration Version"""))
pre_order_iter(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal - Iteration Version"""))
in_order_iter(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal - Iteration Version"""))
post_order_iter(node)
print(prompt())
| 99 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_000 , 5_000 )
return (arr, r)
_lowerCamelCase : Any = make_dataset()
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, ...]:
for triplet in permutations(UpperCAmelCase , 3 ):
if sum(UpperCAmelCase ) == target:
return tuple(sorted(UpperCAmelCase ) )
return (0, 0, 0)
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, int, int]:
arr.sort()
UpperCAmelCase : Tuple = len(UpperCAmelCase )
for i in range(n - 1 ):
UpperCAmelCase , UpperCAmelCase : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a__ ( ) -> tuple[float, float]:
UpperCAmelCase : Union[str, Any] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
UpperCAmelCase : Tuple = '''
triplet_sum1(*dataset)
'''
UpperCAmelCase : List[str] = '''
triplet_sum2(*dataset)
'''
UpperCAmelCase : Tuple = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
UpperCAmelCase : str = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
return (min(UpperCAmelCase ), min(UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCamelCase : int = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 336 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : Union[List[PIL.Image.Image], np.ndarray]
__lowercase : Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version(">=", "0.0.12")
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : np.ndarray
__lowercase : List[bool]
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 100 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def __magic_name__ ( self : int ):
raise NotImplementedError()
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : str, __A : "AutoTokenizer", __A : bool = False, **__A : str ):
UpperCAmelCase : List[str] = tokenizer
UpperCAmelCase : str = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = True
def __magic_name__ ( self : Dict, __A : Optional[int] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
UpperCAmelCase : Union[str, Any] = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Any = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : Optional[Any] = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def __magic_name__ ( self : str ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
UpperCAmelCase : int = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : List[Any] = []
UpperCAmelCase : List[Any] = 0
else:
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : str = True
self.on_finalized_text(__A, stream_end=__A )
def __magic_name__ ( self : List[str], __A : str, __A : bool = False ):
print(__A, flush=__A, end='''''' if not stream_end else None )
def __magic_name__ ( self : List[Any], __A : Optional[int] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : "AutoTokenizer", __A : bool = False, __A : Optional[float] = None, **__A : str ):
super().__init__(__A, __A, **__A )
UpperCAmelCase : Dict = Queue()
UpperCAmelCase : Any = None
UpperCAmelCase : Any = timeout
def __magic_name__ ( self : Dict, __A : str, __A : bool = False ):
self.text_queue.put(__A, timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout )
def __iter__( self : int ):
return self
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[Any] = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 336 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if "model" in orig_key:
lowercase = orig_key.replace('''model.''' , '''''' )
if "norm1" in orig_key:
lowercase = orig_key.replace('''norm1''' , '''attention.output.LayerNorm''' )
if "norm2" in orig_key:
lowercase = orig_key.replace('''norm2''' , '''output.LayerNorm''' )
if "norm" in orig_key:
lowercase = orig_key.replace('''norm''' , '''LayerNorm''' )
if "transformer" in orig_key:
lowercase = orig_key.split('''.''' )[0].split('''_''' )[-1]
lowercase = orig_key.replace(f'transformer_{layer_num}' , f'encoder.layer.{layer_num}' )
if "mha.attn" in orig_key:
lowercase = orig_key.replace('''mha.attn''' , '''attention.self''' )
if "mha" in orig_key:
lowercase = orig_key.replace('''mha''' , '''attention''' )
if "W_q" in orig_key:
lowercase = orig_key.replace('''W_q''' , '''self.query''' )
if "W_k" in orig_key:
lowercase = orig_key.replace('''W_k''' , '''self.key''' )
if "W_v" in orig_key:
lowercase = orig_key.replace('''W_v''' , '''self.value''' )
if "ff1" in orig_key:
lowercase = orig_key.replace('''ff1''' , '''intermediate.dense''' )
if "ff2" in orig_key:
lowercase = orig_key.replace('''ff2''' , '''output.dense''' )
if "ff" in orig_key:
lowercase = orig_key.replace('''ff''' , '''output.dense''' )
if "mlm_class" in orig_key:
lowercase = orig_key.replace('''mlm.mlm_class''' , '''cls.predictions.decoder''' )
if "mlm" in orig_key:
lowercase = orig_key.replace('''mlm''' , '''cls.predictions.transform''' )
if "cls" not in orig_key:
lowercase = '''yoso.''' + orig_key
return orig_key
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
lowercase = orig_state_dict.pop(lowerCAmelCase__ )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
lowercase = val
lowercase = orig_state_dict['''cls.predictions.decoder.bias''']
lowercase = torch.arange(lowerCAmelCase__ ).expand((1, -1) ) + 2
return orig_state_dict
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = torch.load(lowerCAmelCase__ , map_location='''cpu''' )['''model_state_dict''']
lowercase = YosoConfig.from_json_file(lowerCAmelCase__ )
lowercase = YosoForMaskedLM(lowerCAmelCase__ )
lowercase = convert_checkpoint_helper(config.max_position_embeddings , lowerCAmelCase__ )
print(model.load_state_dict(lowerCAmelCase__ ) )
model.eval()
model.save_pretrained(lowerCAmelCase__ )
print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )
if __name__ == "__main__":
lowercase__ :Any = 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."
)
lowercase__ :Any = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 101 |
import numpy
# List of input, output pairs
_lowerCamelCase : Dict = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
_lowerCamelCase : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
_lowerCamelCase : Dict = [2, 4, 1, 5]
_lowerCamelCase : Dict = len(train_data)
_lowerCamelCase : int = 0.0_0_9
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]="train" ) -> Dict:
return calculate_hypothesis_value(UpperCAmelCase , UpperCAmelCase ) - output(
UpperCAmelCase , UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Any:
UpperCAmelCase : str = 0
for i in range(len(UpperCAmelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> Optional[int]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def a__ ( UpperCAmelCase : int , UpperCAmelCase : Optional[Any] ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : str=m ) -> Dict:
UpperCAmelCase : Optional[int] = 0
for i in range(UpperCAmelCase ):
if index == -1:
summation_value += _error(UpperCAmelCase )
else:
summation_value += _error(UpperCAmelCase ) * train_data[i][0][index]
return summation_value
def a__ ( UpperCAmelCase : Dict ) -> Dict:
UpperCAmelCase : Dict = summation_of_cost_derivative(UpperCAmelCase , UpperCAmelCase ) / m
return cost_derivative_value
def a__ ( ) -> List[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
UpperCAmelCase : List[str] = 0.000002
UpperCAmelCase : Any = 0
UpperCAmelCase : Dict = 0
while True:
j += 1
UpperCAmelCase : List[Any] = [0, 0, 0, 0]
for i in range(0 , len(UpperCAmelCase ) ):
UpperCAmelCase : List[str] = get_cost_derivative(i - 1 )
UpperCAmelCase : Tuple = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
UpperCAmelCase , UpperCAmelCase , atol=UpperCAmelCase , rtol=UpperCAmelCase , ):
break
UpperCAmelCase : int = temp_parameter_vector
print(('''Number of iterations:''', j) )
def a__ ( ) -> List[Any]:
for i in range(len(UpperCAmelCase ) ):
print(('''Actual output value:''', output(UpperCAmelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCAmelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 336 | 0 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
SCREAMING_SNAKE_CASE : List[Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Model type selected in the list: ' + ', '.join(__snake_case )} )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
lowerCamelCase__ =field(
default=128, metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
}, )
lowerCamelCase__ =field(
default=128, metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}, )
lowerCamelCase__ =field(
default=64, metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
}, )
lowerCamelCase__ =field(
default=30, metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Overwrite the cached training and evaluation sets'} )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
lowerCamelCase__ =field(
default=0.0, metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
lowerCamelCase__ =field(
default=20, metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
lowerCamelCase__ =field(
default=0, metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
}, )
lowerCamelCase__ =field(default=1, metadata={'help': 'multiple threads for converting example to features'} )
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ ='train'
lowerCamelCase__ ='dev'
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ =42
lowerCamelCase__ =42
lowerCamelCase__ =42
lowerCamelCase__ =42
def __init__(self , a_ , a_ , a_ = None , a_ = Split.train , a_ = False , a_ = None , a_ = "pt" , ):
'''simple docstring'''
__snake_case : Optional[Any] = args
__snake_case : Optional[Any] = is_language_sensitive
__snake_case : Dict = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(a_ , a_ ):
try:
__snake_case : Any = Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
__snake_case : Optional[int] = mode
# Load data features from cache or dataset file
__snake_case : Tuple = '''v2''' if args.version_2_with_negative else '''v1'''
__snake_case : Any = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}""" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__snake_case : Any = cached_features_file + '''.lock'''
with FileLock(a_ ):
if os.path.exists(a_ ) and not args.overwrite_cache:
__snake_case : int = time.time()
__snake_case : Optional[Any] = torch.load(a_ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__snake_case : Tuple = self.old_features['''features''']
__snake_case : Union[str, Any] = self.old_features.get('''dataset''' , a_ )
__snake_case : Optional[Any] = self.old_features.get('''examples''' , a_ )
logger.info(
f"""Loading features from cached file {cached_features_file} [took %.3f s]""" , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
''' future run''' )
else:
if mode == Split.dev:
__snake_case : str = self.processor.get_dev_examples(args.data_dir )
else:
__snake_case : int = self.processor.get_train_examples(args.data_dir )
__snake_case , __snake_case : int = squad_convert_examples_to_features(
examples=self.examples , tokenizer=a_ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=a_ , )
__snake_case : Union[str, Any] = time.time()
torch.save(
{'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples} , a_ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )
def __len__(self ):
'''simple docstring'''
return len(self.features )
def __getitem__(self , a_ ):
'''simple docstring'''
__snake_case : Tuple = self.features[i]
__snake_case : Any = torch.tensor(feature.input_ids , dtype=torch.long )
__snake_case : Optional[Any] = torch.tensor(feature.attention_mask , dtype=torch.long )
__snake_case : str = torch.tensor(feature.token_type_ids , dtype=torch.long )
__snake_case : Optional[Any] = torch.tensor(feature.cls_index , dtype=torch.long )
__snake_case : Optional[Any] = torch.tensor(feature.p_mask , dtype=torch.float )
__snake_case : str = torch.tensor(feature.is_impossible , dtype=torch.float )
__snake_case : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': attention_mask,
'''token_type_ids''': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'''is_impossible''': is_impossible} )
if self.is_language_sensitive:
inputs.update({'''langs''': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__snake_case : Dict = torch.tensor(feature.start_position , dtype=torch.long )
__snake_case : str = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions} )
return inputs
| 102 |
def a__ ( UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] ) -> Optional[Any]:
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[Any] = len(UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
UpperCAmelCase : Optional[Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
UpperCAmelCase : Any = left
UpperCAmelCase : List[str] = point
elif point > right:
UpperCAmelCase : Any = right
UpperCAmelCase : List[str] = point
else:
if item < current_item:
UpperCAmelCase : Optional[int] = point - 1
else:
UpperCAmelCase : str = point + 1
return None
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : List[str] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , point + 1 , UpperCAmelCase )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> int:
if collection != sorted(UpperCAmelCase ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
_lowerCamelCase : Optional[int] = 0
if debug == 1:
_lowerCamelCase : Dict = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
_lowerCamelCase : List[Any] = 6_7
_lowerCamelCase : Optional[Any] = interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print("Not found")
| 336 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A__ : str = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = ['''DeiTFeatureExtractor''']
A__ : int = ['''DeiTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DeiTForImageClassification''',
'''DeiTForImageClassificationWithTeacher''',
'''DeiTForMaskedImageModeling''',
'''DeiTModel''',
'''DeiTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any = [
'''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDeiTForImageClassification''',
'''TFDeiTForImageClassificationWithTeacher''',
'''TFDeiTForMaskedImageModeling''',
'''TFDeiTModel''',
'''TFDeiTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
A__ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 103 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Any = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : List[str]=False ) -> Any:
UpperCAmelCase : Optional[int] = '''backbone.''' if is_semantic else ''''''
UpperCAmelCase : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
(f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
(f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
(f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
(f'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : str=False , UpperCAmelCase : Dict=False ) -> Any:
for i in range(config.num_hidden_layers ):
UpperCAmelCase : Tuple = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias''' )
UpperCAmelCase : List[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias''' )
UpperCAmelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase : str = q_bias
UpperCAmelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
UpperCAmelCase : int = state_dict.pop(f'''{prefix}blocks.{i}.gamma_1''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.gamma_2''' )
UpperCAmelCase : str = gamma_a
UpperCAmelCase : Dict = gamma_a
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : str = val
def a__ ( ) -> Optional[int]:
UpperCAmelCase : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase : Union[str, Any] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any]=False ) -> Union[str, Any]:
UpperCAmelCase : Dict = False if '''rvlcdip''' in checkpoint_url else True
UpperCAmelCase : Any = BeitConfig(use_absolute_position_embeddings=UpperCAmelCase , use_mask_token=UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
UpperCAmelCase : List[Any] = 1_024
UpperCAmelCase : Optional[Any] = 4_096
UpperCAmelCase : Any = 24
UpperCAmelCase : Union[str, Any] = 16
# labels
if "rvlcdip" in checkpoint_url:
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : List[Any] = '''huggingface/label-files'''
UpperCAmelCase : Any = '''rvlcdip-id2label.json'''
UpperCAmelCase : List[str] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Dict = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : Union[str, Any] = idalabel
UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )['''model''']
UpperCAmelCase : List[str] = create_rename_keys(UpperCAmelCase , has_lm_head=UpperCAmelCase )
for src, dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
read_in_q_k_v(UpperCAmelCase , UpperCAmelCase , has_lm_head=UpperCAmelCase )
# load HuggingFace model
UpperCAmelCase : Tuple = BeitForMaskedImageModeling(UpperCAmelCase ) if has_lm_head else BeitForImageClassification(UpperCAmelCase )
model.eval()
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image
UpperCAmelCase : Dict = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCAmelCase )
UpperCAmelCase : List[str] = prepare_img()
UpperCAmelCase : Optional[Any] = image_processor(images=UpperCAmelCase , return_tensors='''pt''' )
UpperCAmelCase : str = encoding['''pixel_values''']
UpperCAmelCase : Any = model(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify logits
UpperCAmelCase : List[Any] = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(UpperCAmelCase ), "Shape of logits not as expected"
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
if has_lm_head:
UpperCAmelCase : List[Any] = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
UpperCAmelCase : Any = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=UpperCAmelCase , )
if __name__ == "__main__":
_lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 336 | 0 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase_ :
"""simple docstring"""
def __init__( self : str ,lowercase__ : Tuple ,lowercase__ : Dict=1_3 ,lowercase__ : List[str]=3_0 ,lowercase__ : Tuple=2 ,lowercase__ : Optional[int]=3 ,lowercase__ : List[str]=True ,lowercase__ : Tuple=True ,lowercase__ : int=3_2 ,lowercase__ : List[str]=5 ,lowercase__ : Tuple=4 ,lowercase__ : Any=3_7 ,lowercase__ : Any="gelu" ,lowercase__ : Union[str, Any]=0.1 ,lowercase__ : Optional[int]=0.1 ,lowercase__ : str=1_0 ,lowercase__ : Optional[int]=0.0_2 ,lowercase__ : Union[str, Any]=3 ,lowercase__ : Optional[int]=0.6 ,lowercase__ : List[Any]=None ,):
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = mask_ratio
__lowercase = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
__lowercase = (image_size // patch_size) ** 2
__lowercase = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
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 ,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 ,mask_ratio=self.mask_ratio ,)
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : str ,lowercase__ : Optional[int] ,lowercase__ : List[str] ):
__lowercase = ViTMAEModel(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : List[Any] ,lowercase__ : int ,lowercase__ : Optional[Any] ):
__lowercase = ViTMAEForPreTraining(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
__lowercase = (self.image_size // self.patch_size) ** 2
__lowercase = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
__lowercase = 1
__lowercase = ViTMAEForPreTraining(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase = model(lowercase__ )
__lowercase = self.patch_size**2
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ (lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
SCREAMING_SNAKE_CASE : Dict = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
SCREAMING_SNAKE_CASE : Any = False
SCREAMING_SNAKE_CASE : Optional[int] = False
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CASE : Union[str, Any] = False
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = ViTMAEModelTester(self )
__lowercase = ConfigTester(self ,config_class=lowercase__ ,has_text_modality=lowercase__ ,hidden_size=3_7 )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase__ ,nn.Linear ) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : Optional[int] ,lowercase__ : List[str] ,lowercase__ : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
__lowercase = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
__lowercase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
__lowercase = torch.from_numpy(lowercase__ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
__lowercase = pt_noise
super().check_pt_tf_models(lowercase__ ,lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
model.to(lowercase__ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
__lowercase = outputs[0].cpu().numpy()
__lowercase = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase__ )
__lowercase = model_class.from_pretrained(lowercase__ )
model.to(lowercase__ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
# Make sure we don't have nans
__lowercase = after_outputs[0].cpu().numpy()
__lowercase = 0
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase__ ,1e-5 )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : int ):
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ViTMAEModel.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
def _A ( ):
"""simple docstring"""
__lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE ( self : str ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
__lowercase = ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' ).to(lowercase__ )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=lowercase__ ,return_tensors='''pt''' ).to(lowercase__ )
# 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)
__lowercase = ViTMAEConfig()
__lowercase = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
__lowercase = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
__lowercase = model(**lowercase__ ,noise=torch.from_numpy(lowercase__ ).to(device=lowercase__ ) )
# verify the logits
__lowercase = torch.Size((1, 1_9_6, 7_6_8) )
self.assertEqual(outputs.logits.shape ,lowercase__ )
__lowercase = torch.tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,expected_slice.to(lowercase__ ) ,atol=1e-4 ) )
| 104 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[int], __A : Optional[int], __A : Any=1_3, __A : str=7, __A : Optional[int]=True, __A : Tuple=True, __A : Union[str, Any]=True, __A : Any=True, __A : Optional[int]=9_9, __A : Tuple=3_2, __A : str=5, __A : Union[str, Any]=4, __A : List[str]=3_7, __A : Tuple="gelu", __A : Optional[int]=0.1, __A : int=0.1, __A : Optional[Any]=5_1_2, __A : int=1_6, __A : Optional[Any]=2, __A : Union[str, Any]=0.0_2, __A : Optional[int]=4, ):
UpperCAmelCase : Any = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Any = seq_length
UpperCAmelCase : Tuple = is_training
UpperCAmelCase : str = use_attention_mask
UpperCAmelCase : List[str] = use_token_type_ids
UpperCAmelCase : int = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Any = num_choices
def __magic_name__ ( self : str ):
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase : List[Any] = None
if self.use_attention_mask:
UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase : Union[str, Any] = RobertaConfig(
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, )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self : int ):
UpperCAmelCase : Any = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = config_and_inputs
UpperCAmelCase : Any = True
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = FlaxRobertaModelTester(self )
@slow
def __magic_name__ ( self : Any ):
for model_class_name in self.all_model_classes:
UpperCAmelCase : Dict = model_class_name.from_pretrained('''roberta-base''', from_pt=__A )
UpperCAmelCase : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
| 336 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def _SCREAMING_SNAKE_CASE ( _lowercase : list ) ->int:
'''simple docstring'''
if not postfix_notation:
return 0
a : List[str] = {"+", "-", "*", "/"}
a : list[Any] = []
for token in postfix_notation:
if token in operations:
a, a : int = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(_lowercase ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 105 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"vocab_file": "vocab.txt"}
_lowerCamelCase : List[str] = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase : List[Any] = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def a__ ( UpperCAmelCase : List[str] ) -> Any:
with open(UpperCAmelCase , '''r''' ) as f:
UpperCAmelCase : Dict = f.read().splitlines()
return [l.strip() for l in lines]
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, __A : Dict, __A : List[Any]="<unk>", __A : List[str]="<cls>", __A : Any="<pad>", __A : Union[str, Any]="<mask>", __A : int="<eos>", **__A : Tuple, ):
super().__init__(**__A )
UpperCAmelCase : Tuple = load_vocab_file(__A )
UpperCAmelCase : List[Any] = dict(enumerate(self.all_tokens ) )
UpperCAmelCase : str = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase : Union[str, Any] = unk_token
UpperCAmelCase : Optional[Any] = cls_token
UpperCAmelCase : Optional[int] = pad_token
UpperCAmelCase : Optional[int] = mask_token
UpperCAmelCase : List[str] = eos_token
UpperCAmelCase : Optional[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def __magic_name__ ( self : Tuple, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : List[Any], __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : Optional[Any], **__A : Union[str, Any] ):
return text.split()
def __magic_name__ ( self : Optional[int], __A : Dict=False ):
return len(self._id_to_token )
def __magic_name__ ( self : int ):
return {token: i for i, token in enumerate(self.all_tokens )}
def __magic_name__ ( self : Tuple, __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : Union[str, Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Optional[int] = [self.cls_token_id]
UpperCAmelCase : Optional[int] = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def __magic_name__ ( self : Any, __A : List, __A : Optional[List] = None, __A : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase : Dict = [1] + ([0] * len(__A )) + [1]
if token_ids_a is not None:
mask += [0] * len(__A ) + [1]
return mask
def __magic_name__ ( self : Optional[int], __A : List[Any], __A : Dict ):
UpperCAmelCase : Union[str, Any] = os.path.join(__A, (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(__A, '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def __magic_name__ ( self : Dict ):
return self.get_vocab_size(with_added_tokens=__A )
def __magic_name__ ( self : Optional[int], __A : Union[List[str], List[AddedToken]], __A : bool = False ):
return super()._add_tokens(__A, special_tokens=__A )
| 336 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = torch.device('''cpu''')
def __SCREAMING_SNAKE_CASE ( ):
lowerCAmelCase__ : List[str] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCAmelCase__ : Any = Image.open(requests.get(A_ , stream=A_ ).raw )
return im
def __SCREAMING_SNAKE_CASE ( A_ ):
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2] )
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ : Union[str, Any] = dct.pop(A_ )
lowerCAmelCase__ : Optional[Any] = val
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Optional[Any] = []
for k in state_dict.keys():
lowerCAmelCase__ : int = k
if ".pwconv" in k:
lowerCAmelCase__ : Dict = k_new.replace('''.pwconv''' , '''.point_wise_conv''' )
if ".dwconv" in k:
lowerCAmelCase__ : Dict = k_new.replace('''.dwconv''' , '''.depth_wise_conv''' )
if ".Proj." in k:
lowerCAmelCase__ : Any = k_new.replace('''.Proj.''' , '''.proj.''' )
if "patch_embed" in k_new:
lowerCAmelCase__ : Union[str, Any] = k_new.replace('''patch_embed''' , '''swiftformer.patch_embed.patch_embedding''' )
if "network" in k_new:
lowerCAmelCase__ : Dict = k_new.split('''.''' )
if ls[2].isdigit():
lowerCAmelCase__ : List[str] = '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:] )
else:
lowerCAmelCase__ : List[str] = k_new.replace('''network''' , '''swiftformer.encoder.network''' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ : int = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
lowerCAmelCase__ : Any = 10_00
lowerCAmelCase__ : Union[str, Any] = '''huggingface/label-files'''
lowerCAmelCase__ : int = '''imagenet-1k-id2label.json'''
lowerCAmelCase__ : int = json.load(open(hf_hub_download(A_ , A_ , repo_type='''dataset''' ) , '''r''' ) )
lowerCAmelCase__ : Optional[int] = {int(A_ ): v for k, v in idalabel.items()}
lowerCAmelCase__ : int = idalabel
lowerCAmelCase__ : int = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
lowerCAmelCase__ : Optional[int] = [3, 3, 6, 4]
lowerCAmelCase__ : Dict = [48, 56, 1_12, 2_20]
elif swiftformer_name == "swiftformer_s":
lowerCAmelCase__ : Any = [3, 3, 9, 6]
lowerCAmelCase__ : Optional[int] = [48, 64, 1_68, 2_24]
elif swiftformer_name == "swiftformer_l1":
lowerCAmelCase__ : Any = [4, 3, 10, 5]
lowerCAmelCase__ : List[Any] = [48, 96, 1_92, 3_84]
elif swiftformer_name == "swiftformer_l3":
lowerCAmelCase__ : str = [4, 4, 12, 6]
lowerCAmelCase__ : List[str] = [64, 1_28, 3_20, 5_12]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('''https''' ):
lowerCAmelCase__ : Tuple = torch.hub.load_state_dict_from_url(A_ , map_location='''cpu''' , check_hash=A_ )
else:
lowerCAmelCase__ : str = torch.load(A_ , map_location='''cpu''' )
lowerCAmelCase__ : Optional[int] = checkpoint
lowerCAmelCase__ : Dict = create_rename_keys(A_ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(A_ , A_ , A_ )
# load HuggingFace model
lowerCAmelCase__ : Tuple = SwiftFormerForImageClassification(A_ ).eval()
hf_model.load_state_dict(A_ )
# prepare test inputs
lowerCAmelCase__ : Optional[int] = prepare_img()
lowerCAmelCase__ : List[Any] = ViTImageProcessor.from_pretrained('''preprocessor_config''' )
lowerCAmelCase__ : int = processor(images=A_ , return_tensors='''pt''' )
# compare outputs from both models
lowerCAmelCase__ : str = get_expected_output(A_ )
lowerCAmelCase__ : int = hf_model(inputs['''pixel_values'''] ).logits
assert hf_logits.shape == torch.Size([1, 10_00] )
assert torch.allclose(hf_logits[0, 0:5] , A_ , atol=1e-3 )
Path(A_ ).mkdir(exist_ok=A_ )
print(f'Saving model {swiftformer_name} to {pytorch_dump_folder_path}' )
hf_model.save_pretrained(A_ )
if __name__ == "__main__":
__UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swiftformer_name''',
default='''swiftformer_xs''',
choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
type=str,
help='''Name of the SwiftFormer model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''./converted_outputs/''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')
__UpperCamelCase : str = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 106 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__A, '''tf_padding''' ) )
self.parent.assertTrue(hasattr(__A, '''depth_multiplier''' ) )
class __UpperCAmelCase :
def __init__( self : int, __A : List[Any], __A : str=1_3, __A : Dict=3, __A : int=3_2, __A : int=0.2_5, __A : List[str]=8, __A : int=8, __A : Dict=6, __A : str=3_2, __A : Any=True, __A : str=True, __A : int=True, __A : Union[str, Any]="relu6", __A : Any=1_2_8_0, __A : List[Any]=0.1, __A : Optional[Any]=0.0_2, __A : Tuple=True, __A : List[Any]=True, __A : str=1_0, __A : Optional[Any]=None, ):
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : List[str] = batch_size
UpperCAmelCase : List[str] = num_channels
UpperCAmelCase : str = image_size
UpperCAmelCase : Optional[int] = depth_multiplier
UpperCAmelCase : Union[str, Any] = depth_divisible_by
UpperCAmelCase : Optional[Any] = min_depth
UpperCAmelCase : List[str] = expand_ratio
UpperCAmelCase : Dict = tf_padding
UpperCAmelCase : str = output_stride
UpperCAmelCase : Union[str, Any] = first_layer_is_expansion
UpperCAmelCase : List[Any] = finegrained_output
UpperCAmelCase : Optional[Any] = hidden_act
UpperCAmelCase : str = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
UpperCAmelCase : Optional[Any] = classifier_dropout_prob
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : List[str] = is_training
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : Any = scope
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Dict = None
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Dict = ids_tensor([self.batch_size], self.num_labels )
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
UpperCAmelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : Any ):
return MobileNetVaConfig(
num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, depth_divisible_by=self.depth_divisible_by, min_depth=self.min_depth, expand_ratio=self.expand_ratio, output_stride=self.output_stride, first_layer_is_expansion=self.first_layer_is_expansion, finegrained_output=self.finegrained_output, hidden_act=self.hidden_act, tf_padding=self.tf_padding, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def __magic_name__ ( self : List[Any], __A : Dict, __A : Optional[Any], __A : Optional[int], __A : Union[str, Any] ):
UpperCAmelCase : Any = MobileNetVaModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[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,
), )
self.parent.assertEqual(
result.pooler_output.shape, (self.batch_size, self.last_hidden_size), )
def __magic_name__ ( self : str, __A : Union[str, Any], __A : Dict, __A : Optional[Any], __A : str ):
UpperCAmelCase : Optional[int] = self.num_labels
UpperCAmelCase : Any = MobileNetVaForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[int] = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any], __A : Optional[Any], __A : List[str], __A : Dict, __A : Dict ):
UpperCAmelCase : Tuple = self.num_labels
UpperCAmelCase : Dict = MobileNetVaForSemanticSegmentation(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = 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,
), )
UpperCAmelCase : Optional[Any] = 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 __magic_name__ ( self : Tuple ):
UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = config_and_inputs
UpperCAmelCase : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
"""feature-extraction""": MobileNetVaModel,
"""image-classification""": MobileNetVaForImageClassification,
"""image-segmentation""": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = MobileNetVaModelTester(self )
UpperCAmelCase : List[Any] = MobileNetVaConfigTester(self, config_class=__A, has_text_modality=__A )
def __magic_name__ ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''' )
def __magic_name__ ( self : Tuple ):
pass
@unittest.skip(reason='''MobileNetV2 does not output attentions''' )
def __magic_name__ ( self : Any ):
pass
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(__A )
UpperCAmelCase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : int ):
def check_hidden_states_output(__A : Any, __A : Optional[Any], __A : str ):
UpperCAmelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
UpperCAmelCase : Dict = model(**self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Optional[Any] = outputs.hidden_states
UpperCAmelCase : List[Any] = 1_6
self.assertEqual(len(__A ), __A )
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : int ):
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__A )
@slow
def __magic_name__ ( self : Dict ):
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] = MobileNetVaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> int:
UpperCAmelCase : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[Any] ):
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''' ) if is_vision_available() else None
)
@slow
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : List[Any] = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''' ).to(__A )
UpperCAmelCase : Optional[int] = self.default_image_processor
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : Dict = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : str = model(**__A )
# verify the logits
UpperCAmelCase : int = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Tuple = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = model.to(__A )
UpperCAmelCase : Tuple = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = prepare_img()
UpperCAmelCase : int = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Union[str, Any] = model(**__A )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify the logits
UpperCAmelCase : Tuple = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor(
[
[[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
[[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
[[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
], device=__A, )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], __A, atol=1E-4 ) )
| 336 | 0 |
import argparse
import copy
def __magic_name__ ( A : int ):
'''simple docstring'''
a = {}
with open(A ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
a = []
_list.append([line.split()[1], line.split()[2]] )
a = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
a = []
_list.append([line.split()[0], line.split()[2]] )
a = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def __magic_name__ ( A : Optional[int], A : Dict ):
'''simple docstring'''
with open(A ) as f:
a = f.read(1 )
a = start_node
a = []
a = start_node
a = 0
while visiting not in first_solution:
a = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(A ) and k[0] not in first_solution:
a = k[1]
a = k[0]
first_solution.append(A )
a = distance_of_first_solution + int(A )
a = best_node
first_solution.append(A )
a = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
a = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def __magic_name__ ( A : Tuple, A : List[str] ):
'''simple docstring'''
a = []
for n in solution[1:-1]:
a = solution.index(A )
for kn in solution[1:-1]:
a = solution.index(A )
if n == kn:
continue
a = copy.deepcopy(A )
a = kn
a = n
a = 0
for k in _tmp[:-1]:
a = _tmp[_tmp.index(A ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
a = distance + int(i[1] )
_tmp.append(A )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
a = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda A : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def __magic_name__ ( A : Any, A : List[str], A : Optional[int], A : Dict, A : List[Any] ):
'''simple docstring'''
a = 1
a = first_solution
a = []
a = distance_of_first_solution
a = solution
while count <= iters:
a = find_neighborhood(A, A )
a = 0
a = neighborhood[index_of_best_solution]
a = len(A ) - 1
a = False
while not found:
a = 0
while i < len(A ):
if best_solution[i] != solution[i]:
a = best_solution[i]
a = solution[i]
break
a = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
a = True
a = best_solution[:-1]
a = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
a = cost
a = solution
else:
a = index_of_best_solution + 1
a = neighborhood[index_of_best_solution]
if len(A ) >= size:
tabu_list.pop(0 )
a = count + 1
return best_solution_ever, best_cost
def __magic_name__ ( A : Optional[Any]=None ):
'''simple docstring'''
a = generate_neighbours(args.File )
a , a = generate_first_solution(
args.File, A )
a , a = tabu_search(
A, A, A, args.Iterations, args.Size, )
print(F"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
__lowerCAmelCase : Any = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 107 |
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 : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """codegen"""
UpperCamelCase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any, __A : Optional[int]=5_0_4_0_0, __A : Tuple=2_0_4_8, __A : Optional[int]=2_0_4_8, __A : List[str]=4_0_9_6, __A : List[str]=2_8, __A : Union[str, Any]=1_6, __A : Tuple=6_4, __A : Union[str, Any]=None, __A : Union[str, Any]="gelu_new", __A : Any=0.0, __A : Dict=0.0, __A : str=0.0, __A : Optional[int]=1E-5, __A : Any=0.0_2, __A : Any=True, __A : Union[str, Any]=5_0_2_5_6, __A : List[str]=5_0_2_5_6, __A : int=False, **__A : List[Any], ):
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Tuple = n_ctx
UpperCAmelCase : Tuple = n_positions
UpperCAmelCase : Optional[int] = n_embd
UpperCAmelCase : Union[str, Any] = n_layer
UpperCAmelCase : List[str] = n_head
UpperCAmelCase : Tuple = n_inner
UpperCAmelCase : int = rotary_dim
UpperCAmelCase : List[Any] = activation_function
UpperCAmelCase : List[str] = resid_pdrop
UpperCAmelCase : Optional[Any] = embd_pdrop
UpperCAmelCase : str = attn_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : Any = bos_token_id
UpperCAmelCase : List[str] = eos_token_id
super().__init__(
bos_token_id=__A, eos_token_id=__A, tie_word_embeddings=__A, **__A )
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Any, __A : PretrainedConfig, __A : str = "default", __A : List[PatchingSpec] = None, __A : bool = False, ):
super().__init__(__A, task=__A, patching_specs=__A, use_past=__A )
if not getattr(self._config, '''pad_token_id''', __A ):
# TODO: how to do that better?
UpperCAmelCase : Union[str, Any] = 0
@property
def __magic_name__ ( self : str ):
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__A, direction='''inputs''' )
UpperCAmelCase : int = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase : List[Any] = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __magic_name__ ( self : Dict ):
return self._config.n_layer
@property
def __magic_name__ ( self : List[str] ):
return self._config.n_head
def __magic_name__ ( self : str, __A : PreTrainedTokenizer, __A : int = -1, __A : int = -1, __A : bool = False, __A : Optional[TensorType] = None, ):
UpperCAmelCase : Union[str, Any] = super(__A, self ).generate_dummy_inputs(
__A, batch_size=__A, seq_length=__A, is_pair=__A, framework=__A )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase : Union[str, 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
UpperCAmelCase , UpperCAmelCase : str = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase : str = seqlen + 2
UpperCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase : Optional[int] = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
UpperCAmelCase : Union[str, Any] = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase : Optional[Any] = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase : Dict = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__A, __A, dtype=__A )], dim=1 )
return ordered_inputs
@property
def __magic_name__ ( self : Tuple ):
return 1_3
| 336 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=lowercase ):
"""simple docstring"""
a : str =["transformers", "torch", "note_seq"]
def __init__( self , *snake_case__ , **snake_case__ ):
"""simple docstring"""
requires_backends(self , ["transformers", "torch", "note_seq"] )
@classmethod
def lowercase__ ( cls , *snake_case__ , **snake_case__ ):
"""simple docstring"""
requires_backends(cls , ["transformers", "torch", "note_seq"] )
@classmethod
def lowercase__ ( cls , *snake_case__ , **snake_case__ ):
"""simple docstring"""
requires_backends(cls , ["transformers", "torch", "note_seq"] )
| 108 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 336 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A: List[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : List[str] = ['pixel_values']
def __init__( self , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = 1 / 255 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> None:
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Dict = size if size is not None else {"""shortest_edge""": 256}
UpperCAmelCase : int = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : int = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
UpperCAmelCase : List[str] = get_size_dict(_SCREAMING_SNAKE_CASE , param_name="""crop_size""" )
UpperCAmelCase : Optional[int] = do_resize
UpperCAmelCase : int = size
UpperCAmelCase : Any = resample
UpperCAmelCase : Dict = do_center_crop
UpperCAmelCase : Dict = crop_size
UpperCAmelCase : Tuple = do_rescale
UpperCAmelCase : Optional[Any] = rescale_factor
UpperCAmelCase : Any = do_normalize
UpperCAmelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase : int = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
if "shortest_edge" not in size:
raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
UpperCAmelCase : Tuple = get_resize_output_image_size(_SCREAMING_SNAKE_CASE , size=size["""shortest_edge"""] , default_to_square=_SCREAMING_SNAKE_CASE )
return resize(_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase : List[str] = get_size_dict(_SCREAMING_SNAKE_CASE )
if "height" not in size or "width" not in size:
raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(_SCREAMING_SNAKE_CASE , size=(size["""height"""], size["""width"""]) , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE ) -> np.ndarray:
'''simple docstring'''
return rescale(_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> np.ndarray:
'''simple docstring'''
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : str = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase : str = size if size is not None else self.size
UpperCAmelCase : Union[str, Any] = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Any = resample if resample is not None else self.resample
UpperCAmelCase : int = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase : Dict = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase : List[Any] = get_size_dict(_SCREAMING_SNAKE_CASE , param_name="""crop_size""" )
UpperCAmelCase : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase : str = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase : int = image_std if image_std is not None else self.image_std
UpperCAmelCase : List[Any] = make_list_of_images(_SCREAMING_SNAKE_CASE )
if not valid_images(_SCREAMING_SNAKE_CASE ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCAmelCase : Optional[Any] = [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]
if do_resize:
UpperCAmelCase : Any = [self.resize(image=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE ) for image in images]
if do_center_crop:
UpperCAmelCase : Dict = [self.center_crop(image=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE ) for image in images]
if do_rescale:
UpperCAmelCase : List[Any] = [self.rescale(image=_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE ) for image in images]
if do_normalize:
UpperCAmelCase : str = [self.normalize(image=_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE ) for image in images]
UpperCAmelCase : Optional[Any] = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for image in images]
UpperCAmelCase : str = {"""pixel_values""": images}
return BatchFeature(data=_SCREAMING_SNAKE_CASE , tensor_type=_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase : Any = target_sizes.numpy()
UpperCAmelCase : Tuple = []
for idx in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCAmelCase : Dict = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Tuple = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase : List[Any] = logits.argmax(dim=1 )
UpperCAmelCase : List[str] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 109 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCAmelCase :
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
@classmethod
def __magic_name__ ( cls : Any ):
return cls()
@dataclass
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@property
def __magic_name__ ( self : Optional[int] ):
return True
@register_to_config
def __init__( self : Optional[int], __A : float = 0.0_2, __A : float = 1_0_0, __A : float = 1.0_0_7, __A : float = 8_0, __A : float = 0.0_5, __A : float = 5_0, ):
pass
def __magic_name__ ( self : Optional[Any] ):
return KarrasVeSchedulerState.create()
def __magic_name__ ( self : int, __A : KarrasVeSchedulerState, __A : int, __A : Tuple = () ):
UpperCAmelCase : Optional[Any] = jnp.arange(0, __A )[::-1].copy()
UpperCAmelCase : Union[str, Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A, schedule=jnp.array(__A, dtype=jnp.floataa ), timesteps=__A, )
def __magic_name__ ( self : List[Any], __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : random.KeyArray, ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase : int = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1 )
else:
UpperCAmelCase : Optional[int] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase : Union[str, Any] = random.split(__A, num=1 )
UpperCAmelCase : List[str] = self.config.s_noise * random.normal(key=__A, shape=sample.shape )
UpperCAmelCase : Tuple = sigma + gamma * sigma
UpperCAmelCase : List[str] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : int = sample_hat + sigma_hat * model_output
UpperCAmelCase : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : jnp.ndarray, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
UpperCAmelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Optional[Any], __A : KarrasVeSchedulerState, __A : Optional[int], __A : int, __A : Union[str, Any] ):
raise NotImplementedError()
| 336 | 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
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , UpperCamelCase : Any , UpperCamelCase : Optional[Any]=7 , UpperCamelCase : Optional[Any]=3 , UpperCamelCase : Optional[Any]=18 , UpperCamelCase : Tuple=30 , UpperCamelCase : Optional[Any]=400 , UpperCamelCase : Any=True , UpperCamelCase : List[Any]=None , UpperCamelCase : Tuple=True , UpperCamelCase : Tuple=None , UpperCamelCase : Optional[int]=True , UpperCamelCase : Any=[0.48145466, 0.4578275, 0.40821073] , UpperCamelCase : int=[0.26862954, 0.26130258, 0.27577711] , UpperCamelCase : List[Any]=True , ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = size if size is not None else {'''height''': 224, '''width''': 224}
__UpperCAmelCase : Any = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
__UpperCAmelCase : int = parent
__UpperCAmelCase : Union[str, Any] = batch_size
__UpperCAmelCase : List[Any] = num_channels
__UpperCAmelCase : Optional[Any] = image_size
__UpperCAmelCase : str = min_resolution
__UpperCAmelCase : str = max_resolution
__UpperCAmelCase : List[str] = do_resize
__UpperCAmelCase : Union[str, Any] = size
__UpperCAmelCase : Dict = do_center_crop
__UpperCAmelCase : Tuple = crop_size
__UpperCAmelCase : Dict = do_normalize
__UpperCAmelCase : List[Any] = image_mean
__UpperCAmelCase : Optional[Any] = image_std
__UpperCAmelCase : List[Any] = do_convert_rgb
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def lowerCamelCase__ ( self : List[Any] , UpperCamelCase : Dict=False , UpperCamelCase : Optional[Any]=False , UpperCamelCase : List[str]=False ):
'''simple docstring'''
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
__UpperCAmelCase : Any = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
__UpperCAmelCase : List[str] = []
for i in range(self.batch_size ):
__UpperCAmelCase : int = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
__UpperCAmelCase : Tuple = [Image.fromarray(np.moveaxis(__A , 0 , -1 ) ) for x in image_inputs]
if torchify:
__UpperCAmelCase : Optional[Any] = [torch.from_numpy(__A ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class lowerCamelCase__ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__a = ChineseCLIPImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self : int ):
'''simple docstring'''
__UpperCAmelCase : str = ChineseCLIPImageProcessingTester(self , do_center_crop=__A )
@property
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , """do_resize""" ) )
self.assertTrue(hasattr(__A , """size""" ) )
self.assertTrue(hasattr(__A , """do_center_crop""" ) )
self.assertTrue(hasattr(__A , """center_crop""" ) )
self.assertTrue(hasattr(__A , """do_normalize""" ) )
self.assertTrue(hasattr(__A , """image_mean""" ) )
self.assertTrue(hasattr(__A , """image_std""" ) )
self.assertTrue(hasattr(__A , """do_convert_rgb""" ) )
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 224, """width""": 224} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
__UpperCAmelCase : str = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self : Optional[Any] ):
'''simple docstring'''
__UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCAmelCase : Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input
__UpperCAmelCase : List[str] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
__UpperCAmelCase : List[Any] = image_processing(__A , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__UpperCAmelCase : int = self.image_processor_tester.prepare_inputs(equal_resolution=__A , numpify=__A )
for image in image_inputs:
self.assertIsInstance(__A , np.ndarray )
# Test not batched input
__UpperCAmelCase : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
__UpperCAmelCase : Union[str, Any] = image_processing(__A , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__UpperCAmelCase : List[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=__A , torchify=__A )
for image in image_inputs:
self.assertIsInstance(__A , torch.Tensor )
# Test not batched input
__UpperCAmelCase : List[str] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
__UpperCAmelCase : str = image_processing(__A , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
@require_torch
@require_vision
class lowerCamelCase__ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__a = ChineseCLIPImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Any = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=__A )
__UpperCAmelCase : List[str] = 3
@property
def lowerCamelCase__ ( self : int ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , """do_resize""" ) )
self.assertTrue(hasattr(__A , """size""" ) )
self.assertTrue(hasattr(__A , """do_center_crop""" ) )
self.assertTrue(hasattr(__A , """center_crop""" ) )
self.assertTrue(hasattr(__A , """do_normalize""" ) )
self.assertTrue(hasattr(__A , """image_mean""" ) )
self.assertTrue(hasattr(__A , """image_std""" ) )
self.assertTrue(hasattr(__A , """do_convert_rgb""" ) )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCAmelCase : Tuple = self.image_processor_tester.prepare_inputs(equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input
__UpperCAmelCase : Tuple = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
__UpperCAmelCase : Dict = image_processing(__A , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 115 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCAmelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def a__ ( ) -> Dict:
if os.name == "nt":
UpperCAmelCase : List[str] = CursorInfo()
UpperCAmelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def a__ ( ) -> Optional[int]:
if os.name == "nt":
UpperCAmelCase : int = CursorInfo()
UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Any = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def a__ ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 336 | 0 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
"stable diffusion controlnet",
"0.22.0",
"Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.",
standard_warn=False,
stacklevel=3,
)
| 100 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 | 0 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCamelCase = 25_6047
UpperCamelCase = 25_6145
@require_sentencepiece
@require_tokenizers
class snake_case_ ( lowerCamelCase__ ,unittest.TestCase ):
__A : int = NllbTokenizer
__A : Optional[Any] = NllbTokenizerFast
__A : Any = True
__A : Union[str, Any] = True
__A : Optional[int] = {}
def __UpperCamelCase ( self : Any ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__ : Optional[Any] = NllbTokenizer(__A , keep_accents=__A )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCamelCase ( self : Optional[Any] ) -> int:
lowercase__ : Dict = NllbTokenizer(__A , keep_accents=__A )
lowercase__ : List[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(__A , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__A ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
lowercase__ : List[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
__A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
lowercase__ : Tuple = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(
__A , [
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]
] , )
lowercase__ : Any = tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(
__A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def __UpperCamelCase ( self : str ) -> Tuple:
lowercase__ : List[str] = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ : List[Any] = self.rust_tokenizer_class.from_pretrained(__A , **__A )
lowercase__ : int = self.tokenizer_class.from_pretrained(__A , **__A )
lowercase__ : str = tempfile.mkdtemp()
lowercase__ : Optional[Any] = tokenizer_r.save_pretrained(__A )
lowercase__ : Dict = tokenizer_p.save_pretrained(__A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
lowercase__ : List[Any] = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(__A , __A )
# Checks everything loads correctly in the same way
lowercase__ : Optional[Any] = tokenizer_r.from_pretrained(__A )
lowercase__ : int = tokenizer_p.from_pretrained(__A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__A , __A ) )
shutil.rmtree(__A )
# Save tokenizer rust, legacy_format=True
lowercase__ : int = tempfile.mkdtemp()
lowercase__ : List[Any] = tokenizer_r.save_pretrained(__A , legacy_format=__A )
lowercase__ : Union[str, Any] = tokenizer_p.save_pretrained(__A )
# Checks it save with the same files
self.assertSequenceEqual(__A , __A )
# Checks everything loads correctly in the same way
lowercase__ : Optional[int] = tokenizer_r.from_pretrained(__A )
lowercase__ : List[Any] = tokenizer_p.from_pretrained(__A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__A , __A ) )
shutil.rmtree(__A )
# Save tokenizer rust, legacy_format=False
lowercase__ : Optional[Any] = tempfile.mkdtemp()
lowercase__ : Tuple = tokenizer_r.save_pretrained(__A , legacy_format=__A )
lowercase__ : str = tokenizer_p.save_pretrained(__A )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowercase__ : Any = tokenizer_r.from_pretrained(__A )
lowercase__ : Dict = tokenizer_p.from_pretrained(__A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__A , __A ) )
shutil.rmtree(__A )
@require_torch
def __UpperCamelCase ( self : Optional[int] ) -> Optional[int]:
if not self.test_seqaseq:
return
lowercase__ : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
# Longer text that will definitely require truncation.
lowercase__ : Dict = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
''' will only worsen the violence and misery for millions of people.''',
]
lowercase__ : List[str] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
try:
lowercase__ : int = tokenizer.prepare_seqaseq_batch(
src_texts=__A , tgt_texts=__A , max_length=3 , max_target_length=10 , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="ron_Latn" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
lowercase__ : Tuple = tokenizer.prepare_seqaseq_batch(
__A , tgt_texts=__A , max_length=3 , return_tensors="pt" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
lowercase__ : Union[str, Any] = tokenizer.prepare_seqaseq_batch(
src_texts=__A , max_length=3 , max_target_length=10 , return_tensors="pt" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("decoder_input_ids" , __A )
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece." )
def __UpperCamelCase ( self : Dict ) -> Optional[Any]:
pass
def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ : Any = [AddedToken("<special>" , lstrip=__A )]
lowercase__ : Optional[int] = self.rust_tokenizer_class.from_pretrained(
__A , additional_special_tokens=__A , **__A )
lowercase__ : Dict = tokenizer_r.encode("Hey this is a <special> token" )
lowercase__ : Any = tokenizer_r.encode("<special>" , add_special_tokens=__A )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
lowercase__ : Dict = self.rust_tokenizer_class.from_pretrained(
__A , additional_special_tokens=__A , **__A , )
lowercase__ : List[str] = self.tokenizer_class.from_pretrained(
__A , additional_special_tokens=__A , **__A )
lowercase__ : Union[str, Any] = tokenizer_p.encode("Hey this is a <special> token" )
lowercase__ : Union[str, Any] = tokenizer_cr.encode("Hey this is a <special> token" )
self.assertEqual(__A , __A )
self.assertEqual(__A , __A )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case_ ( unittest.TestCase ):
__A : Tuple = "facebook/nllb-200-distilled-600M"
__A : Union[str, Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
__A : Union[str, Any] = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
__A : Union[str, Any] = [
25_6047,
1_6297,
13_4408,
8165,
24_8066,
1_4734,
950,
1135,
10_5721,
3573,
83,
2_7352,
108,
4_9486,
2,
]
@classmethod
def __UpperCamelCase ( cls : Optional[int] ) -> int:
lowercase__ : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="eng_Latn" , tgt_lang="ron_Latn" )
lowercase__ : Tuple = 1
return cls
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] , 25_60_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] , 25_60_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] , 25_60_57 )
def __UpperCamelCase ( self : List[Any] ) -> Dict:
lowercase__ : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __A )
def __UpperCamelCase ( self : List[str] ) -> Optional[Any]:
self.assertIn(__A , self.tokenizer.all_special_ids )
# fmt: off
lowercase__ : Any = [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
# fmt: on
lowercase__ : Union[str, Any] = self.tokenizer.decode(__A , skip_special_tokens=__A )
lowercase__ : Dict = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__A )
self.assertEqual(__A , __A )
self.assertNotIn(self.tokenizer.eos_token , __A )
def __UpperCamelCase ( self : Tuple ) -> Any:
lowercase__ : Optional[int] = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , __A )
lowercase__ : Optional[Any] = 10
lowercase__ : int = self.tokenizer(__A , max_length=__A , truncation=__A ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , __A )
self.assertEqual(len(__A ) , __A )
def __UpperCamelCase ( self : List[str] ) -> List[str]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [25_62_03, 3] )
def __UpperCamelCase ( self : Dict ) -> str:
lowercase__ : Dict = tempfile.mkdtemp()
lowercase__ : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__A )
lowercase__ : Any = NllbTokenizer.from_pretrained(__A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __A )
@require_torch
def __UpperCamelCase ( self : Any ) -> Any:
lowercase__ : Dict = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__A , truncation=__A , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
lowercase__ : Union[str, Any] = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["ron_Latn"] )
self.assertIsInstance(__A , __A )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
lowercase__ : List[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __A )
self.assertEqual(__A , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
lowercase__ : Optional[Any] = self.tokenizer(self.src_text , padding=__A , truncation=__A , max_length=3 , return_tensors="pt" )
lowercase__ : Union[str, Any] = self.tokenizer(
text_target=self.tgt_text , padding=__A , truncation=__A , max_length=10 , return_tensors="pt" )
lowercase__ : Tuple = targets['''input_ids''']
lowercase__ : List[Any] = shift_tokens_right(
__A , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __UpperCamelCase ( self : Optional[int] ) -> Any:
lowercase__ : Dict = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
nested_simplify(__A ) , {
# A, test, EOS, en_XX
"input_ids": [[25_60_47, 70, 73_56, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 25_60_57,
} , )
@require_torch
def __UpperCamelCase ( self : Tuple ) -> Tuple:
lowercase__ : Tuple = True
lowercase__ : int = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47] )
lowercase__ : int = False
lowercase__ : Optional[int] = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2] )
| 87 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : Tuple = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = ["DeiTFeatureExtractor"]
UpperCAmelCase_ : List[Any] = ["DeiTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = [
"DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DeiTForImageClassification",
"DeiTForImageClassificationWithTeacher",
"DeiTForMaskedImageModeling",
"DeiTModel",
"DeiTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Any = [
"TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDeiTForImageClassification",
"TFDeiTForImageClassificationWithTeacher",
"TFDeiTForMaskedImageModeling",
"TFDeiTModel",
"TFDeiTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 200 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( UpperCAmelCase : Dict=None ) -> Optional[int]:
if subparsers is not None:
UpperCAmelCase : Tuple = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase : Dict = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase : Optional[int] = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=UpperCAmelCase , default=UpperCAmelCase , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=UpperCAmelCase , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=UpperCAmelCase , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase : Union[str, Any] = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=UpperCAmelCase , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase )
return parser
def a__ ( UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase : List[str] = defaults.commands
if not args.tpu_name:
UpperCAmelCase : Tuple = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase : int = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase : Tuple = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase : Dict = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , UpperCAmelCase ):
UpperCAmelCase : Optional[int] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , UpperCAmelCase ):
UpperCAmelCase : int = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase : Optional[int] = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
UpperCAmelCase : int = '''; '''.join(UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase : Any = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(UpperCAmelCase )}''' )
return
subprocess.run(UpperCAmelCase )
print('''Successfully setup pod.''' )
def a__ ( ) -> Any:
UpperCAmelCase : Any = tpu_command_parser()
UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(UpperCAmelCase )
| 336 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __snake_case ( lowerCamelCase__ ):
@staticmethod
@abstractmethod
def lowerCamelCase_ ( lowercase) -> Any:
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
raise NotImplementedError()
| 290 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 0 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
_A = ""
_A = ""
_A = ""
_A = ""
def a__ ( lowerCAmelCase ) -> None:
# authorize twitter, initialize tweepy
UpperCAmelCase__ : List[str] = tweepy.OAuthHandler(lowerCAmelCase , lowerCAmelCase )
auth.set_access_token(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[str] = tweepy.API(lowerCAmelCase )
# initialize a list to hold all the tweepy Tweets
UpperCAmelCase__ : int = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCAmelCase__ : int = api.user_timeline(screen_name=lowerCAmelCase , count=2_00 )
# save most recent tweets
alltweets.extend(lowerCAmelCase )
# save the id of the oldest tweet less one
UpperCAmelCase__ : List[Any] = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(lowerCAmelCase ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
UpperCAmelCase__ : Any = api.user_timeline(
screen_name=lowerCAmelCase , count=2_00 , max_id=lowerCAmelCase )
# save most recent tweets
alltweets.extend(lowerCAmelCase )
# update the id of the oldest tweet less one
UpperCAmelCase__ : Tuple = alltweets[-1].id - 1
print(F"""...{len(lowerCAmelCase )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCAmelCase__ : Optional[int] = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , """w""" ) as f:
UpperCAmelCase__ : int = csv.writer(lowerCAmelCase )
writer.writerow(["""id""", """created_at""", """text"""] )
writer.writerows(lowerCAmelCase )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets("""FirePing32""")
| 171 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __UpperCAmelCase ( lowerCamelCase__ ):
def __get__( self : Tuple, __A : Optional[Any], __A : Optional[int]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
UpperCAmelCase : str = '''__cached_''' + self.fget.__name__
UpperCAmelCase : int = getattr(__A, __A, __A )
if cached is None:
UpperCAmelCase : Any = self.fget(__A )
setattr(__A, __A, __A )
return cached
def a__ ( UpperCAmelCase : Optional[Any] ) -> Any:
UpperCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_torch_fx_proxy(UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(UpperCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : str ) -> Tuple:
return _is_numpy(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
import torch
return isinstance(UpperCAmelCase , torch.Tensor )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
return False if not is_torch_available() else _is_torch(UpperCAmelCase )
def a__ ( UpperCAmelCase : Tuple ) -> List[str]:
import torch
return isinstance(UpperCAmelCase , torch.device )
def a__ ( UpperCAmelCase : Any ) -> Any:
return False if not is_torch_available() else _is_torch_device(UpperCAmelCase )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
import torch
if isinstance(UpperCAmelCase , UpperCAmelCase ):
if hasattr(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
else:
return False
return isinstance(UpperCAmelCase , torch.dtype )
def a__ ( UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> str:
import tensorflow as tf
return isinstance(UpperCAmelCase , tf.Tensor )
def a__ ( UpperCAmelCase : int ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCAmelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(UpperCAmelCase )
return type(UpperCAmelCase ) == tf.Tensor
def a__ ( UpperCAmelCase : int ) -> List[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[Any] ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCAmelCase , jnp.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Optional[int]:
return False if not is_flax_available() else _is_jax(UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Tuple:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return [to_py_obj(UpperCAmelCase ) for o in obj]
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase ).tolist()
elif isinstance(UpperCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( UpperCAmelCase : Any ) -> List[str]:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_numpy(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return np.array(UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase )
else:
return obj
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(__A ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
UpperCAmelCase : int = getattr(self, class_fields[0].name )
UpperCAmelCase : str = all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__A ):
if isinstance(__A, __A ):
UpperCAmelCase : Tuple = first_field.items()
UpperCAmelCase : Any = True
else:
try:
UpperCAmelCase : Optional[Any] = iter(__A )
UpperCAmelCase : Optional[Any] = True
except TypeError:
UpperCAmelCase : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__A ):
if (
not isinstance(__A, (list, tuple) )
or not len(__A ) == 2
or not isinstance(element[0], __A )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
UpperCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
UpperCAmelCase : Union[str, Any] = element[1]
elif first_field is not None:
UpperCAmelCase : Union[str, Any] = first_field
else:
for field in class_fields:
UpperCAmelCase : Optional[Any] = getattr(self, field.name )
if v is not None:
UpperCAmelCase : Optional[int] = v
def __delitem__( self : Union[str, Any], *__A : str, **__A : Tuple ):
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : List[str], *__A : Union[str, Any], **__A : Optional[Any] ):
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Any, *__A : Dict, **__A : str ):
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Dict, *__A : int, **__A : Dict ):
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : List[str], __A : List[str] ):
if isinstance(__A, __A ):
UpperCAmelCase : int = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[Any], __A : Dict, __A : Union[str, Any] ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__A, __A )
super().__setattr__(__A, __A )
def __setitem__( self : Dict, __A : List[Any], __A : Union[str, Any] ):
# Will raise a KeyException if needed
super().__setitem__(__A, __A )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__A, __A )
def __magic_name__ ( self : List[str] ):
return tuple(self[k] for k in self.keys() )
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@classmethod
def __magic_name__ ( cls : List[Any], __A : Tuple ):
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """longest"""
UpperCamelCase = """max_length"""
UpperCamelCase = """do_not_pad"""
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
UpperCamelCase = """np"""
UpperCamelCase = """jax"""
class __UpperCAmelCase :
def __init__( self : Any, __A : List[ContextManager] ):
UpperCAmelCase : Tuple = context_managers
UpperCAmelCase : Tuple = ExitStack()
def __enter__( self : Any ):
for context_manager in self.context_managers:
self.stack.enter_context(__A )
def __exit__( self : List[Any], *__A : Union[str, Any], **__A : Dict ):
self.stack.__exit__(*__A, **__A )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : int = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a__ ( UpperCAmelCase : Dict ) -> Any:
UpperCAmelCase : List[Any] = model_class.__name__
UpperCAmelCase : Union[str, Any] = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a__ ( UpperCAmelCase : MutableMapping , UpperCAmelCase : str = "" , UpperCAmelCase : str = "." ) -> Union[str, Any]:
def _flatten_dict(UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str]="" , UpperCAmelCase : Any="." ):
for k, v in d.items():
UpperCAmelCase : List[str] = str(UpperCAmelCase ) + delimiter + str(UpperCAmelCase ) if parent_key else k
if v and isinstance(UpperCAmelCase , UpperCAmelCase ):
yield from flatten_dict(UpperCAmelCase , UpperCAmelCase , delimiter=UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
@contextmanager
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : bool = False ) -> Optional[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if is_numpy_array(UpperCAmelCase ):
return np.transpose(UpperCAmelCase , axes=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.T if axes is None else array.permute(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(UpperCAmelCase , perm=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.transpose(UpperCAmelCase , axes=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Optional[int] ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.reshape(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.reshape(UpperCAmelCase , UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=None ) -> Any:
if is_numpy_array(UpperCAmelCase ):
return np.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> str:
if is_numpy_array(UpperCAmelCase ):
return np.expand_dims(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.unsqueeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.size(UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.size(UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Dict:
for key, value in auto_map.items():
if isinstance(UpperCAmelCase , (tuple, list) ):
UpperCAmelCase : List[Any] = [f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
UpperCAmelCase : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a__ ( UpperCAmelCase : Tuple ) -> Union[str, Any]:
for base_class in inspect.getmro(UpperCAmelCase ):
UpperCAmelCase : Any = base_class.__module__
UpperCAmelCase : Dict = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 336 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_A = logging.getLogger(__name__)
_A = "pytorch_model.bin"
@dataclasses.dataclass
class A :
__snake_case = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
__snake_case = dataclasses.field(
default=lowerCamelCase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class A :
__snake_case = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
__snake_case = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
__snake_case = dataclasses.field(
default=lowerCamelCase__ , metadata={'help': 'A csv or a json file containing the validation data.'} )
__snake_case = dataclasses.field(
default=lowerCamelCase__ , metadata={'help': 'The name of the task to train on.'} , )
__snake_case = dataclasses.field(
default=lowerCamelCase__ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class A :
__snake_case = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
__snake_case = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
__snake_case = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]'
} , )
__snake_case = dataclasses.field(
default=10 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
__snake_case = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
__snake_case = dataclasses.field(
default=lowerCamelCase__ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
__snake_case = dataclasses.field(
default=lowerCamelCase__ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
__snake_case = dataclasses.field(
default=lowerCamelCase__ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
__snake_case = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
__snake_case = dataclasses.field(
default=100 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
__snake_case = dataclasses.field(
default=lowerCamelCase__ , metadata={'help': 'Random seed for initialization.'} , )
def __UpperCamelCase ( _A , _A , _A , _A , _A , _A ):
lowerCAmelCase_ = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
lowerCAmelCase_ = dataset.filter(lambda _A : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
lowerCAmelCase_ = int(eval_result * len(_A ) )
print(_A )
lowerCAmelCase_ = dataset.sort('''probability''' , reverse=_A )
lowerCAmelCase_ = dataset.select(range(_A ) )
lowerCAmelCase_ = dataset.remove_columns(['''label''', '''probability'''] )
lowerCAmelCase_ = dataset.rename_column('''prediction''' , '''label''' )
lowerCAmelCase_ = dataset.map(lambda _A : {"label": idalabel[example["label"]]} )
lowerCAmelCase_ = dataset.shuffle(seed=args.seed )
lowerCAmelCase_ = os.path.join(_A , f"train_pseudo.{args.data_file_extension}" )
if args.data_file_extension == "csv":
dataset.to_csv(_A , index=_A )
else:
dataset.to_json(_A )
def __UpperCamelCase ( _A , _A , _A , _A , **_A ):
lowerCAmelCase_ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
lowerCAmelCase_ = STModelArguments(model_name_or_path=_A )
lowerCAmelCase_ = STDataArguments(train_file=_A , infer_file=_A )
lowerCAmelCase_ = STTrainingArguments(output_dir=_A )
lowerCAmelCase_ = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(_A ).items():
setattr(_A , _A , _A )
for key, value in kwargs.items():
if hasattr(_A , _A ):
setattr(_A , _A , _A )
# Sanity checks
lowerCAmelCase_ = {}
lowerCAmelCase_ = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
lowerCAmelCase_ = args.train_file
lowerCAmelCase_ = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
lowerCAmelCase_ = args.eval_file
for key in data_files:
lowerCAmelCase_ = data_files[key].split('''.''' )[-1]
assert extension in ["csv", "json"], f"`{key}_file` should be a csv or a json file."
if args.data_file_extension is None:
lowerCAmelCase_ = extension
else:
assert extension == args.data_file_extension, f"`{key}_file` should be a {args.data_file_extension} file`."
assert (
args.eval_metric in datasets.list_metrics()
), f"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('''Creating the initial data directory for self-training...''' )
lowerCAmelCase_ = f"{args.output_dir}/self-train_iter-{{}}".format
lowerCAmelCase_ = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=_A )
os.makedirs(_A , exist_ok=_A )
accelerator.wait_for_everyone()
lowerCAmelCase_ = None
lowerCAmelCase_ = None
lowerCAmelCase_ = 0
lowerCAmelCase_ = False
# Show the progress bar
lowerCAmelCase_ = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
lowerCAmelCase_ = data_dir_format(_A )
assert os.path.exists(_A )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
lowerCAmelCase_ = os.path.join(_A , '''stage-1''' )
lowerCAmelCase_ = {
'''accelerator''': accelerator,
'''model_name_or_path''': args.model_name_or_path,
'''cache_dir''': args.cache_dir,
'''do_train''': True,
'''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
'''do_eval''': True if args.eval_file is not None else False,
'''eval_file''': data_files['''eval'''],
'''do_predict''': True,
'''infer_file''': data_files['''infer'''],
'''task_name''': args.task_name,
'''label_list''': args.label_list,
'''output_dir''': current_output_dir,
'''eval_metric''': args.eval_metric,
'''evaluation_strategy''': args.evaluation_strategy,
'''early_stopping_patience''': args.early_stopping_patience,
'''early_stopping_threshold''': args.early_stopping_threshold,
'''seed''': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(_A , _A ):
arguments_dict.update({key: value} )
lowerCAmelCase_ = os.path.join(_A , '''best-checkpoint''' , _A )
if os.path.exists(_A ):
logger.info(
'''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.''' , _A , _A , )
else:
logger.info('''***** Running self-training: iteration: %d, stage: 1 *****''' , _A )
finetune(**_A )
accelerator.wait_for_everyone()
assert os.path.exists(_A )
logger.info('''Self-training job completed: iteration: %d, stage: 1.''' , _A )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
lowerCAmelCase_ = os.path.join(_A , '''best-checkpoint''' )
lowerCAmelCase_ = os.path.join(_A , '''stage-2''' )
# Update arguments_dict
lowerCAmelCase_ = model_path
lowerCAmelCase_ = data_files['''train''']
lowerCAmelCase_ = current_output_dir
lowerCAmelCase_ = os.path.join(_A , '''best-checkpoint''' , _A )
if os.path.exists(_A ):
logger.info(
'''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.''' , _A , _A , )
else:
logger.info('''***** Running self-training: iteration: %d, stage: 2 *****''' , _A )
finetune(**_A )
accelerator.wait_for_everyone()
assert os.path.exists(_A )
logger.info('''Self-training job completed: iteration: %d, stage: 2.''' , _A )
lowerCAmelCase_ = iteration
lowerCAmelCase_ = data_dir_format(iteration + 1 )
lowerCAmelCase_ = AutoConfig.from_pretrained(os.path.join(_A , '''best-checkpoint''' ) )
lowerCAmelCase_ = config.idalabel
lowerCAmelCase_ = os.path.join(_A , '''eval_results_best-checkpoint.json''' )
lowerCAmelCase_ = os.path.join(_A , '''test_results_best-checkpoint.json''' )
assert os.path.exists(_A )
with open(_A , '''r''' ) as f:
lowerCAmelCase_ = float(json.load(_A )[args.eval_metric] )
lowerCAmelCase_ = os.path.join(_A , '''infer_output_best-checkpoint.csv''' )
assert os.path.exists(_A )
# Loading the dataset from local csv or json files.
lowerCAmelCase_ = load_dataset(args.data_file_extension , data_files={'''data''': data_files['''infer''']} )['''data''']
lowerCAmelCase_ = load_dataset('''csv''' , data_files={'''data''': infer_output_file} )['''data''']
if accelerator.is_main_process:
os.makedirs(_A , exist_ok=_A )
shutil.copy(_A , os.path.join(_A , f"eval_results_iter-{iteration}.json" ) )
if os.path.exists(_A ):
shutil.copy(_A , os.path.join(_A , f"test_results_iter-{iteration}.json" ) )
create_pseudo_labeled_data(_A , _A , _A , _A , _A , _A )
accelerator.wait_for_everyone()
lowerCAmelCase_ = os.path.join(_A , f"train_pseudo.{args.data_file_extension}" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
lowerCAmelCase_ = eval_result
if best_iteration is None:
lowerCAmelCase_ = new_iteration
lowerCAmelCase_ = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
lowerCAmelCase_ = new_iteration
lowerCAmelCase_ = new_eval_result
lowerCAmelCase_ = 0
else:
if new_eval_result == best_eval_result:
lowerCAmelCase_ = new_iteration
lowerCAmelCase_ = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
lowerCAmelCase_ = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('''Best iteration: %d''' , _A )
logger.info('''Best evaluation result: %s = %f''' , args.eval_metric , _A )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_A , f"eval_results_iter-{iteration}.json" ) , os.path.join(_A , '''eval_results_best-iteration.json''' ) , )
else:
# Assume that the last iteration is the best
logger.info('''Best iteration: %d''' , args.max_selftrain_iterations - 1 )
logger.info('''Best evaluation result: %s = %f''' , args.eval_metric , _A )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(_A , f"eval_results_iter-{args.max_selftrain_iterations - 1}.json" ) , os.path.join(_A , '''eval_results_best-iteration.json''' ) , )
| 278 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LayoutLMTokenizer
UpperCamelCase = LayoutLMTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
UpperCAmelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : int = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __magic_name__ ( self : Union[str, Any], **__A : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], __A : int ):
UpperCAmelCase : Optional[Any] = '''UNwant\u00E9d,running'''
UpperCAmelCase : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [7, 4, 5, 1_0, 8, 9] )
def __magic_name__ ( self : Optional[int] ):
pass
| 336 | 0 |
from math import factorial
__snake_case = {str(digit): factorial(digit) for digit in range(1_0)}
def _A ( _lowercase ) -> int:
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
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(_lowercase ) )
def _A ( _lowercase = 60 , _lowercase = 1_00_00_00 ) -> int:
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ) or not isinstance(_lowercase , _lowercase ):
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
__UpperCamelCase = 0
# the cached sizes of the previous chains
__UpperCamelCase = {}
for start_chain_element in range(1 , _lowercase ):
# The temporary set will contain the elements of the chain
__UpperCamelCase = set()
__UpperCamelCase = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__UpperCamelCase = 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(_lowercase )
chain_set_length += 1
__UpperCamelCase = digit_factorial_sum(_lowercase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__UpperCamelCase = 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()}""")
| 310 |
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 __UpperCAmelCase :
def __init__( self : Any, __A : str, __A : Dict=1_3, __A : int=3_0, __A : Tuple=2, __A : Union[str, Any]=3, __A : Any=True, __A : str=True, __A : Dict=3_2, __A : List[Any]=2, __A : Optional[Any]=4, __A : Union[str, Any]=3_7, __A : int="gelu", __A : int=0.1, __A : List[Any]=0.1, __A : Tuple=1_0, __A : Tuple=0.0_2, __A : Any=3, __A : List[str]=0.6, __A : Any=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Optional[int] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Tuple = mask_ratio
UpperCAmelCase : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Optional[Any] ):
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=__A, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def __magic_name__ ( self : str, __A : List[Any], __A : Any, __A : Any ):
UpperCAmelCase : Optional[Any] = TFViTMAEModel(config=__A )
UpperCAmelCase : Tuple = model(__A, training=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : str, __A : int, __A : str ):
UpperCAmelCase : Dict = TFViTMAEForPreTraining(__A )
UpperCAmelCase : int = model(__A, training=__A )
# expected sequence length = num_patches
UpperCAmelCase : int = (self.image_size // self.patch_size) ** 2
UpperCAmelCase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase : Tuple = 1
UpperCAmelCase : List[Any] = TFViTMAEForPreTraining(__A )
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = model(__A, training=__A )
UpperCAmelCase : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , 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 __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = TFViTMAEModelTester(self )
UpperCAmelCase : int = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer) )
UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, tf.keras.layers.Layer ) )
def __magic_name__ ( self : str ):
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Any = model_class(__A )
UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : int = [*signature.parameters.keys()]
UpperCAmelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __magic_name__ ( self : int ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : str = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : Dict = model(__A, noise=__A )
UpperCAmelCase : Any = copy.deepcopy(self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Union[str, Any] = model(**__A, noise=__A )
UpperCAmelCase : Dict = outputs_dict[0].numpy()
UpperCAmelCase : Tuple = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ), 1E-6 )
def __magic_name__ ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__A : Union[str, Any] ):
UpperCAmelCase : str = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__A ):
UpperCAmelCase : Tuple = v.numpy()
else:
UpperCAmelCase : str = np.array(__A )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : Any = self._prepare_for_class(__A, __A )
UpperCAmelCase : Optional[int] = prepare_numpy_arrays(__A )
UpperCAmelCase : str = model(__A, noise=__A )
UpperCAmelCase : str = model(**__A, noise=__A )
self.assert_outputs_same(__A, __A )
def __magic_name__ ( self : int, __A : str, __A : Union[str, Any], __A : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase : Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : int = tf.constant(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase : List[Any] = tf_noise
super().check_pt_tf_models(__A, __A, __A )
def __magic_name__ ( self : str ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Union[str, Any] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__A )
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(__A, __A ),)
if isinstance(__A, __A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__A, '''_keras_serializable''', __A )
}
UpperCAmelCase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : str = tf.convert_to_tensor(__A )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCAmelCase : Tuple = main_layer_class(__A )
UpperCAmelCase : int = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCAmelCase : List[Any] = tf.keras.Model(__A, outputs=main_layer(__A ) )
UpperCAmelCase : List[Any] = model(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = os.path.join(__A, '''keras_model.h5''' )
model.save(__A )
UpperCAmelCase : List[str] = tf.keras.models.load_model(
__A, custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__A, tf.keras.Model )
UpperCAmelCase : Tuple = model(__A )
self.assert_outputs_same(__A, __A )
@slow
def __magic_name__ ( self : Dict ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(__A )
UpperCAmelCase : List[str] = self._prepare_for_class(__A, __A )
UpperCAmelCase : Union[str, Any] = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : Optional[int] = outputs.last_hidden_state.numpy()
UpperCAmelCase : Union[str, Any] = 0
else:
UpperCAmelCase : Optional[int] = outputs.logits.numpy()
UpperCAmelCase : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A, saved_model=__A )
UpperCAmelCase : Dict = model_class.from_pretrained(__A )
UpperCAmelCase : str = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : int = after_outputs['''last_hidden_state'''].numpy()
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Any = after_outputs['''logits'''].numpy()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A, 1E-5 )
def __magic_name__ ( self : Optional[Any] ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : List[Any] = model(__A, noise=__A )
UpperCAmelCase : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__A )
UpperCAmelCase : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCAmelCase : str = model_class.from_config(model.config )
UpperCAmelCase : List[str] = new_model(__A ) # Build model
new_model.set_weights(model.get_weights() )
UpperCAmelCase : Tuple = new_model(__A, noise=__A )
self.assert_outputs_same(__A, __A )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def __magic_name__ ( self : Tuple ):
pass
@slow
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(__A )
def a__ ( ) -> Dict:
UpperCAmelCase : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[str] ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase : Tuple = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
UpperCAmelCase : List[str] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : str = image_processor(images=__A, 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)
UpperCAmelCase : Optional[int] = ViTMAEConfig()
UpperCAmelCase : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCAmelCase : Optional[int] = model(**__A, noise=__A )
# verify the logits
UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3], __A, atol=1E-4 )
| 336 | 0 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[int] = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 'mask2former'
_SCREAMING_SNAKE_CASE = ['swin']
_SCREAMING_SNAKE_CASE = {'hidden_size': 'hidden_dim'}
def __init__( self : Optional[Any] , _lowerCAmelCase : Optional[Dict] = None , _lowerCAmelCase : int = 2_5_6 , _lowerCAmelCase : int = 2_5_6 , _lowerCAmelCase : int = 2_5_6 , _lowerCAmelCase : int = 1_0_2_4 , _lowerCAmelCase : str = "relu" , _lowerCAmelCase : int = 6 , _lowerCAmelCase : int = 1_0 , _lowerCAmelCase : int = 8 , _lowerCAmelCase : float = 0.0 , _lowerCAmelCase : int = 2_0_4_8 , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase : int = 4 , _lowerCAmelCase : int = 2_5_5 , _lowerCAmelCase : int = 1_0_0 , _lowerCAmelCase : float = 0.1 , _lowerCAmelCase : float = 2.0 , _lowerCAmelCase : float = 5.0 , _lowerCAmelCase : float = 5.0 , _lowerCAmelCase : int = 1_2_5_4_4 , _lowerCAmelCase : float = 3.0 , _lowerCAmelCase : float = 0.75 , _lowerCAmelCase : float = 0.02 , _lowerCAmelCase : float = 1.0 , _lowerCAmelCase : bool = True , _lowerCAmelCase : List[int] = [4, 8, 1_6, 3_2] , _lowerCAmelCase : bool = None , **_lowerCAmelCase : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
snake_case_ = CONFIG_MAPPING['''swin'''](
image_size=2_2_4 , in_channels=3 , patch_size=4 , embed_dim=9_6 , depths=[2, 2, 1_8, 2] , num_heads=[3, 6, 1_2, 2_4] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=__A , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(__A , __A ):
snake_case_ = backbone_config.pop("model_type" )
snake_case_ = CONFIG_MAPPING[backbone_model_type]
snake_case_ = config_class.from_dict(__A )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
F'''Supported model types: {','.join(self.backbones_supported )}''' )
snake_case_ = backbone_config
snake_case_ = feature_size
snake_case_ = mask_feature_size
snake_case_ = hidden_dim
snake_case_ = encoder_feedforward_dim
snake_case_ = activation_function
snake_case_ = encoder_layers
snake_case_ = decoder_layers
snake_case_ = num_attention_heads
snake_case_ = dropout
snake_case_ = dim_feedforward
snake_case_ = pre_norm
snake_case_ = enforce_input_projection
snake_case_ = common_stride
snake_case_ = ignore_value
snake_case_ = num_queries
snake_case_ = no_object_weight
snake_case_ = class_weight
snake_case_ = mask_weight
snake_case_ = dice_weight
snake_case_ = train_num_points
snake_case_ = oversample_ratio
snake_case_ = importance_sample_ratio
snake_case_ = init_std
snake_case_ = init_xavier_std
snake_case_ = use_auxiliary_loss
snake_case_ = feature_strides
snake_case_ = output_auxiliary_logits
snake_case_ = decoder_layers
super().__init__(**__A )
@classmethod
def lowerCAmelCase__ ( cls : str , _lowerCAmelCase : PretrainedConfig , **_lowerCAmelCase : Any ) -> List[str]:
"""simple docstring"""
return cls(
backbone_config=__A , **__A , )
def lowerCAmelCase__ ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
snake_case_ = copy.deepcopy(self.__dict__ )
snake_case_ = self.backbone_config.to_dict()
snake_case_ = self.__class__.model_type
return output
| 159 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_lowerCamelCase : List[Any] = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
_lowerCamelCase : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 336 | 0 |
"""simple docstring"""
from ....utils import logging
_a = logging.get_logger(__name__)
class _lowerCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Tuple, UpperCAmelCase__ : List[str], UpperCAmelCase__ : Any=None, UpperCAmelCase__ : int=2_0_4_8 ):
__lowercase = config.__dict__
__lowercase = modal_hidden_size
if num_labels:
__lowercase = num_labels
| 17 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] for _ in range(UpperCAmelCase )]
UpperCAmelCase : Union[str, Any] = run_maze(UpperCAmelCase , 0 , 0 , UpperCAmelCase )
if solved:
print('''\n'''.join(str(UpperCAmelCase ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def a__ ( UpperCAmelCase : list[list[int]] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Dict = len(UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
UpperCAmelCase : Dict = 1
return True
UpperCAmelCase : Union[str, Any] = (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 : Any = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCAmelCase : str = 1
# check for directions
if (
run_maze(UpperCAmelCase , i + 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j + 1 , UpperCAmelCase )
or run_maze(UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j - 1 , UpperCAmelCase )
):
return True
UpperCAmelCase : Any = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A : list[int] = [ord(letter) for letter in string.ascii_lowercase]
A : set[int] = {ord(char) for char in VALID_CHARS}
A : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def lowercase_ ( _A : list[int] , _A : tuple[int, ...] ):
"""simple docstring"""
lowerCamelCase__ : str = ""
lowerCamelCase__ : int
lowerCamelCase__ : int
lowerCamelCase__ : int
for keychar, cipherchar in zip(cycle(_A ) , _A ):
lowerCamelCase__ : Dict = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(_A )
return decoded
def lowercase_ ( _A : list[int] ):
"""simple docstring"""
lowerCamelCase__ : list[str] = []
for key in product(_A , repeat=3 ):
lowerCamelCase__ : int = try_key(_A , _A )
if encoded is not None:
possibles.append(_A )
return possibles
def lowercase_ ( _A : list[str] , _A : str ):
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def lowercase_ ( _A : str = "p059_cipher.txt" ):
"""simple docstring"""
lowerCamelCase__ : list[int]
lowerCamelCase__ : list[str]
lowerCamelCase__ : str
lowerCamelCase__ : str
lowerCamelCase__ : str = Path(_A ).parent.joinpath(_A ).read_text(encoding="utf-8" )
lowerCamelCase__ : int = [int(_A ) for number in data.strip().split("," )]
lowerCamelCase__ : Dict = filter_valid_chars(_A )
for common_word in COMMON_WORDS:
lowerCamelCase__ : Tuple = filter_common_word(_A , _A )
if len(_A ) == 1:
break
lowerCamelCase__ : List[Any] = possibles[0]
return sum(ord(_A ) for char in decoded_text )
if __name__ == "__main__":
print(f'{solution() = }')
| 184 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __UpperCAmelCase :
def __init__( self : List[Any], __A : List[str], __A : List[str]=1_3, __A : Any=6_4, __A : Optional[Any]=2, __A : str=3, __A : str=True, __A : str=True, __A : Optional[Any]=3_2, __A : List[str]=5, __A : int=4, __A : str=3_7, __A : str="gelu", __A : Dict=0.1, __A : List[Any]=0.1, __A : Dict=1_0, __A : int=0.0_2, __A : Any=[1, 1_6, 4, 4], __A : Optional[int]=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Any = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : List[str] = patch_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : List[Any] = is_training
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : Any = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase : str = type_sequence_label_size
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : int = scope
UpperCAmelCase : List[str] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
UpperCAmelCase : str = (self.image_size // 3_2) ** 2
UpperCAmelCase : List[str] = num_patches + 1
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : str = None
if self.use_labels:
UpperCAmelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Any ):
UpperCAmelCase : Dict = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 1_6, 3_2],
'''num_groups''': 2,
}
return ViTHybridConfig(
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, backbone_featmap_shape=self.backbone_featmap_shape, backbone_config=__A, )
def __magic_name__ ( self : Optional[int], __A : Optional[int], __A : int, __A : Tuple ):
UpperCAmelCase : int = ViTHybridModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Tuple = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : Dict, __A : str, __A : List[str] ):
UpperCAmelCase : str = self.type_sequence_label_size
UpperCAmelCase : List[Any] = ViTHybridForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : int ):
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = config_and_inputs
UpperCAmelCase : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
UpperCamelCase = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Any = ViTHybridModelTester(self )
UpperCAmelCase : List[Any] = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : int ):
UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, nn.Linear ) )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(__A )
UpperCAmelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = _config_zero_init(__A )
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(config=__A )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
UpperCAmelCase : Union[str, Any] = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@slow
def __magic_name__ ( self : List[str] ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Union[str, Any] = ViTHybridModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> Tuple:
UpperCAmelCase : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : str ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__A )
UpperCAmelCase : Tuple = self.default_image_processor
UpperCAmelCase : int = prepare_img()
UpperCAmelCase : Union[str, Any] = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(**__A )
# verify the logits
UpperCAmelCase : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Optional[Any] = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
@require_accelerate
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Union[str, Any] = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
UpperCAmelCase : int = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''', device_map='''auto''' )
UpperCAmelCase : Tuple = prepare_img()
UpperCAmelCase : Optional[int] = image_processor(images=__A, return_tensors='''pt''' )
UpperCAmelCase : Dict = model(**__A )
UpperCAmelCase : Any = outputs.logits
# model predicts one of the 1000 ImageNet classes
UpperCAmelCase : Dict = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx], '''tabby, tabby cat''' )
| 336 | 0 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
__UpperCAmelCase : Any = 1
__UpperCAmelCase : Dict = True
for v in tree[start]:
if v not in visited:
ret += dfs(_UpperCamelCase )
if ret % 2 == 0:
cuts.append(_UpperCamelCase )
return ret
def lowerCamelCase ( ) -> Tuple:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
UpperCAmelCase : Dict = 10, 9
UpperCAmelCase : Union[str, Any] = defaultdict(list)
UpperCAmelCase : dict[int, bool] = {}
UpperCAmelCase : list[int] = []
UpperCAmelCase : Dict = 0
UpperCAmelCase : Dict = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 115 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_000 , 5_000 )
return (arr, r)
_lowerCamelCase : Any = make_dataset()
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, ...]:
for triplet in permutations(UpperCAmelCase , 3 ):
if sum(UpperCAmelCase ) == target:
return tuple(sorted(UpperCAmelCase ) )
return (0, 0, 0)
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> tuple[int, int, int]:
arr.sort()
UpperCAmelCase : Tuple = len(UpperCAmelCase )
for i in range(n - 1 ):
UpperCAmelCase , UpperCAmelCase : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a__ ( ) -> tuple[float, float]:
UpperCAmelCase : Union[str, Any] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
UpperCAmelCase : Tuple = '''
triplet_sum1(*dataset)
'''
UpperCAmelCase : List[str] = '''
triplet_sum2(*dataset)
'''
UpperCAmelCase : Tuple = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
UpperCAmelCase : str = repeat(setup=UpperCAmelCase , stmt=UpperCAmelCase , repeat=5 , number=10_000 )
return (min(UpperCAmelCase ), min(UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCamelCase : int = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 336 | 0 |
"""simple docstring"""
from math import ceil
def _lowerCAmelCase ( UpperCamelCase_ = 1001 ):
__SCREAMING_SNAKE_CASE = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__SCREAMING_SNAKE_CASE = 2 * i + 1
__SCREAMING_SNAKE_CASE = 2 * i
__SCREAMING_SNAKE_CASE = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
__magic_name__ = int(sys.argv[1])
print(solution(n))
except ValueError:
print("Invalid entry - please enter a number")
| 100 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCAmelCase :
def __magic_name__ ( self : int, __A : Dict ):
raise NotImplementedError()
def __magic_name__ ( self : int ):
raise NotImplementedError()
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : str, __A : "AutoTokenizer", __A : bool = False, **__A : str ):
UpperCAmelCase : List[str] = tokenizer
UpperCAmelCase : str = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = True
def __magic_name__ ( self : Dict, __A : Optional[int] ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
UpperCAmelCase : Union[str, Any] = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Any = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
UpperCAmelCase : int = []
UpperCAmelCase : int = 0
# If the last token is a CJK character, we print the characters.
elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : Union[str, Any] = text[self.print_len :]
self.print_len += len(__A )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : Optional[Any] = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(__A )
self.on_finalized_text(__A )
def __magic_name__ ( self : str ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
UpperCAmelCase : int = self.tokenizer.decode(self.token_cache, **self.decode_kwargs )
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : List[Any] = []
UpperCAmelCase : List[Any] = 0
else:
UpperCAmelCase : Dict = ''''''
UpperCAmelCase : str = True
self.on_finalized_text(__A, stream_end=__A )
def __magic_name__ ( self : List[str], __A : str, __A : bool = False ):
print(__A, flush=__A, end='''''' if not stream_end else None )
def __magic_name__ ( self : List[Any], __A : Optional[int] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Dict, __A : "AutoTokenizer", __A : bool = False, __A : Optional[float] = None, **__A : str ):
super().__init__(__A, __A, **__A )
UpperCAmelCase : Dict = Queue()
UpperCAmelCase : Any = None
UpperCAmelCase : Any = timeout
def __magic_name__ ( self : Dict, __A : str, __A : bool = False ):
self.text_queue.put(__A, timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout )
def __iter__( self : int ):
return self
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[Any] = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 336 | 0 |
def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : int):
return [sentence[i : i + ngram_size] for i in range(len(_lowerCamelCase) - ngram_size + 1)]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 87 |
import numpy
# List of input, output pairs
_lowerCamelCase : Dict = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
_lowerCamelCase : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
_lowerCamelCase : Dict = [2, 4, 1, 5]
_lowerCamelCase : Dict = len(train_data)
_lowerCamelCase : int = 0.0_0_9
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]="train" ) -> Dict:
return calculate_hypothesis_value(UpperCAmelCase , UpperCAmelCase ) - output(
UpperCAmelCase , UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Any:
UpperCAmelCase : str = 0
for i in range(len(UpperCAmelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> Optional[int]:
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def a__ ( UpperCAmelCase : int , UpperCAmelCase : Optional[Any] ) -> List[str]:
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : str=m ) -> Dict:
UpperCAmelCase : Optional[int] = 0
for i in range(UpperCAmelCase ):
if index == -1:
summation_value += _error(UpperCAmelCase )
else:
summation_value += _error(UpperCAmelCase ) * train_data[i][0][index]
return summation_value
def a__ ( UpperCAmelCase : Dict ) -> Dict:
UpperCAmelCase : Dict = summation_of_cost_derivative(UpperCAmelCase , UpperCAmelCase ) / m
return cost_derivative_value
def a__ ( ) -> List[Any]:
global parameter_vector
# Tune these values to set a tolerance value for predicted output
UpperCAmelCase : List[str] = 0.000002
UpperCAmelCase : Any = 0
UpperCAmelCase : Dict = 0
while True:
j += 1
UpperCAmelCase : List[Any] = [0, 0, 0, 0]
for i in range(0 , len(UpperCAmelCase ) ):
UpperCAmelCase : List[str] = get_cost_derivative(i - 1 )
UpperCAmelCase : Tuple = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
UpperCAmelCase , UpperCAmelCase , atol=UpperCAmelCase , rtol=UpperCAmelCase , ):
break
UpperCAmelCase : int = temp_parameter_vector
print(('''Number of iterations:''', j) )
def a__ ( ) -> List[Any]:
for i in range(len(UpperCAmelCase ) ):
print(('''Actual output value:''', output(UpperCAmelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCAmelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 336 | 0 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = [0] * len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , len(SCREAMING_SNAKE_CASE__ ) ):
# use last results for better performance - dynamic programming
_SCREAMING_SNAKE_CASE : List[Any] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
_SCREAMING_SNAKE_CASE : Any = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
_SCREAMING_SNAKE_CASE : List[Any] = j
return prefix_result
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
return max(prefix_function(SCREAMING_SNAKE_CASE__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 200 |
def a__ ( UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] ) -> Optional[Any]:
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[Any] = len(UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
UpperCAmelCase : Optional[Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
UpperCAmelCase : Any = left
UpperCAmelCase : List[str] = point
elif point > right:
UpperCAmelCase : Any = right
UpperCAmelCase : List[str] = point
else:
if item < current_item:
UpperCAmelCase : Optional[int] = point - 1
else:
UpperCAmelCase : str = point + 1
return None
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] ) -> Dict:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
UpperCAmelCase : List[str] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
UpperCAmelCase , UpperCAmelCase , point + 1 , UpperCAmelCase )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> int:
if collection != sorted(UpperCAmelCase ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
_lowerCamelCase : Optional[int] = 0
if debug == 1:
_lowerCamelCase : Dict = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
_lowerCamelCase : List[Any] = 6_7
_lowerCamelCase : Optional[Any] = interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print("Not found")
| 336 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class __snake_case :
def __init__( self , lowercase , ) -> Any:
'''simple docstring'''
a__: Optional[Any] = parent
a__: Optional[int] = 13
a__: Dict = 7
a__: Union[str, Any] = 30
a__: Any = self.seq_length + self.mem_len
a__: List[Any] = 15
a__: Any = True
a__: int = True
a__: List[str] = 99
a__: Optional[Any] = [10, 50, 80]
a__: Optional[int] = 32
a__: Optional[int] = 32
a__: List[Any] = 4
a__: Optional[Any] = 8
a__: Tuple = 1_28
a__: Dict = 2
a__: List[str] = 2
a__: Tuple = None
a__: Dict = 1
a__: List[Any] = 0
a__: List[Any] = 3
a__: List[Any] = self.vocab_size - 1
a__: Dict = 0.01
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__: Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__: List[Any] = None
if self.use_labels:
a__: Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__: Optional[Any] = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
random.seed(self.seed)
tf.random.set_seed(self.seed)
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase) -> Any:
'''simple docstring'''
a__: Tuple = TFTransfoXLModel(__A)
a__: Optional[int] = model(__A).to_tuple()
a__: List[str] = {'''input_ids''': input_ids_a, '''mems''': mems_a}
a__: Optional[int] = model(__A).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__: List[str] = TFTransfoXLLMHeadModel(__A)
a__: int = model(__A).to_tuple()
a__: int = {'''input_ids''': input_ids_a, '''labels''': lm_labels}
a__: Optional[int] = model(__A).to_tuple()
a__: List[Any] = model([input_ids_a, mems_a]).to_tuple()
a__: Optional[Any] = {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
a__: Any = model(__A).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
a__: List[str] = TFTransfoXLForSequenceClassification(__A)
a__: List[str] = model(__A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Union[str, Any] = self.prepare_config_and_inputs()
(a__): List[Any] = config_and_inputs
a__: Union[str, Any] = {'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
a__ = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
a__ = () if is_tf_available() else ()
a__ = (
{
"""feature-extraction""": TFTransfoXLModel,
"""text-classification""": TFTransfoXLForSequenceClassification,
"""text-generation""": TFTransfoXLLMHeadModel,
"""zero-shot""": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
a__ = False
a__ = False
a__ = False
a__ = False
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase) -> Any:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Any = TFTransfoXLModelTester(self)
a__: Dict = ConfigTester(self , config_class=__A , d_embed=37)
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
self.model_tester.set_seed()
a__: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*__A)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
self.model_tester.set_seed()
a__: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*__A)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__A)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
a__: Dict = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
a__: Optional[Any] = model_class(__A)
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer)
if model_class in list_other_models_with_output_ebd:
a__: Optional[int] = model.get_output_embeddings()
assert isinstance(__A , tf.keras.layers.Layer)
a__: Dict = model.get_bias()
assert name is None
else:
a__: Tuple = model.get_output_embeddings()
assert x is None
a__: str = model.get_bias()
assert name is None
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
pass
@slow
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__: Tuple = TFTransfoXLModel.from_pretrained(__A)
self.assertIsNotNone(__A)
@unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.')
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
pass
@require_tf
class __snake_case ( unittest.TestCase ):
@unittest.skip('Skip test until #12651 is resolved.')
@slow
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
a__: str = TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
# fmt: off
a__: Dict = tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]] , dtype=tf.intaa) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
a__: Dict = [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
a__: Optional[Any] = model.generate(__A , max_length=2_00 , do_sample=__A)
self.assertListEqual(output_ids[0].numpy().tolist() , __A)
| 290 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Any = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any]=False , UpperCAmelCase : List[str]=False ) -> Any:
UpperCAmelCase : Optional[int] = '''backbone.''' if is_semantic else ''''''
UpperCAmelCase : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
(f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
(f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
(f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
(f'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : str=False , UpperCAmelCase : Dict=False ) -> Any:
for i in range(config.num_hidden_layers ):
UpperCAmelCase : Tuple = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias''' )
UpperCAmelCase : List[Any] = state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias''' )
UpperCAmelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase : str = q_bias
UpperCAmelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase : int = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
UpperCAmelCase : int = state_dict.pop(f'''{prefix}blocks.{i}.gamma_1''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(f'''{prefix}blocks.{i}.gamma_2''' )
UpperCAmelCase : str = gamma_a
UpperCAmelCase : Dict = gamma_a
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : str = val
def a__ ( ) -> Optional[int]:
UpperCAmelCase : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase : Union[str, Any] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any]=False ) -> Union[str, Any]:
UpperCAmelCase : Dict = False if '''rvlcdip''' in checkpoint_url else True
UpperCAmelCase : Any = BeitConfig(use_absolute_position_embeddings=UpperCAmelCase , use_mask_token=UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
UpperCAmelCase : List[Any] = 1_024
UpperCAmelCase : Optional[Any] = 4_096
UpperCAmelCase : Any = 24
UpperCAmelCase : Union[str, Any] = 16
# labels
if "rvlcdip" in checkpoint_url:
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : List[Any] = '''huggingface/label-files'''
UpperCAmelCase : Any = '''rvlcdip-id2label.json'''
UpperCAmelCase : List[str] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Dict = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : Union[str, Any] = idalabel
UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
UpperCAmelCase : Tuple = torch.hub.load_state_dict_from_url(UpperCAmelCase , map_location='''cpu''' )['''model''']
UpperCAmelCase : List[str] = create_rename_keys(UpperCAmelCase , has_lm_head=UpperCAmelCase )
for src, dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
read_in_q_k_v(UpperCAmelCase , UpperCAmelCase , has_lm_head=UpperCAmelCase )
# load HuggingFace model
UpperCAmelCase : Tuple = BeitForMaskedImageModeling(UpperCAmelCase ) if has_lm_head else BeitForImageClassification(UpperCAmelCase )
model.eval()
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image
UpperCAmelCase : Dict = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCAmelCase )
UpperCAmelCase : List[str] = prepare_img()
UpperCAmelCase : Optional[Any] = image_processor(images=UpperCAmelCase , return_tensors='''pt''' )
UpperCAmelCase : str = encoding['''pixel_values''']
UpperCAmelCase : Any = model(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify logits
UpperCAmelCase : List[Any] = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(UpperCAmelCase ), "Shape of logits not as expected"
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
if has_lm_head:
UpperCAmelCase : List[Any] = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
UpperCAmelCase : Any = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase , UpperCAmelCase ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=UpperCAmelCase , )
if __name__ == "__main__":
_lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 336 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(lowerCAmelCase , 2 ) - pow(lowerCAmelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(lowerCAmelCase , 2 ) - pow(lowerCAmelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(lowerCAmelCase , 2 ) + pow(lowerCAmelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 171 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[int], __A : Optional[int], __A : Any=1_3, __A : str=7, __A : Optional[int]=True, __A : Tuple=True, __A : Union[str, Any]=True, __A : Any=True, __A : Optional[int]=9_9, __A : Tuple=3_2, __A : str=5, __A : Union[str, Any]=4, __A : List[str]=3_7, __A : Tuple="gelu", __A : Optional[int]=0.1, __A : int=0.1, __A : Optional[Any]=5_1_2, __A : int=1_6, __A : Optional[Any]=2, __A : Union[str, Any]=0.0_2, __A : Optional[int]=4, ):
UpperCAmelCase : Any = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Any = seq_length
UpperCAmelCase : Tuple = is_training
UpperCAmelCase : str = use_attention_mask
UpperCAmelCase : List[str] = use_token_type_ids
UpperCAmelCase : int = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Any = num_choices
def __magic_name__ ( self : str ):
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase : List[Any] = None
if self.use_attention_mask:
UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Any = None
if self.use_token_type_ids:
UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase : Union[str, Any] = RobertaConfig(
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, )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self : int ):
UpperCAmelCase : Any = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = config_and_inputs
UpperCAmelCase : Any = True
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = FlaxRobertaModelTester(self )
@slow
def __magic_name__ ( self : Any ):
for model_class_name in self.all_model_classes:
UpperCAmelCase : Dict = model_class_name.from_pretrained('''roberta-base''', from_pt=__A )
UpperCAmelCase : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
| 336 | 0 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [[0 for _ in range(_A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCAmelCase_ = 1
for n in range(m + 1 ):
for k in range(1 , _A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_A = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
_A = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 278 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"vocab_file": "vocab.txt"}
_lowerCamelCase : List[str] = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase : List[Any] = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def a__ ( UpperCAmelCase : List[str] ) -> Any:
with open(UpperCAmelCase , '''r''' ) as f:
UpperCAmelCase : Dict = f.read().splitlines()
return [l.strip() for l in lines]
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, __A : Dict, __A : List[Any]="<unk>", __A : List[str]="<cls>", __A : Any="<pad>", __A : Union[str, Any]="<mask>", __A : int="<eos>", **__A : Tuple, ):
super().__init__(**__A )
UpperCAmelCase : Tuple = load_vocab_file(__A )
UpperCAmelCase : List[Any] = dict(enumerate(self.all_tokens ) )
UpperCAmelCase : str = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase : Union[str, Any] = unk_token
UpperCAmelCase : Optional[Any] = cls_token
UpperCAmelCase : Optional[int] = pad_token
UpperCAmelCase : Optional[int] = mask_token
UpperCAmelCase : List[str] = eos_token
UpperCAmelCase : Optional[Any] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def __magic_name__ ( self : Tuple, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : List[Any], __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : Optional[Any], **__A : Union[str, Any] ):
return text.split()
def __magic_name__ ( self : Optional[int], __A : Dict=False ):
return len(self._id_to_token )
def __magic_name__ ( self : int ):
return {token: i for i, token in enumerate(self.all_tokens )}
def __magic_name__ ( self : Tuple, __A : str ):
return self._token_to_id.get(__A, self._token_to_id.get(self.unk_token ) )
def __magic_name__ ( self : Any, __A : int ):
return self._id_to_token.get(__A, self.unk_token )
def __magic_name__ ( self : Union[str, Any], __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Optional[int] = [self.cls_token_id]
UpperCAmelCase : Optional[int] = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def __magic_name__ ( self : Any, __A : List, __A : Optional[List] = None, __A : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase : Dict = [1] + ([0] * len(__A )) + [1]
if token_ids_a is not None:
mask += [0] * len(__A ) + [1]
return mask
def __magic_name__ ( self : Optional[int], __A : List[Any], __A : Dict ):
UpperCAmelCase : Union[str, Any] = os.path.join(__A, (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
with open(__A, '''w''' ) as f:
f.write('''\n'''.join(self.all_tokens ) )
return (vocab_file,)
@property
def __magic_name__ ( self : Dict ):
return self.get_vocab_size(with_added_tokens=__A )
def __magic_name__ ( self : Optional[int], __A : Union[List[str], List[AddedToken]], __A : bool = False ):
return super()._add_tokens(__A, special_tokens=__A )
| 336 | 0 |
def _A ( _lowercase ) -> list[list[float]]:
"""simple docstring"""
__UpperCamelCase = []
for data in source_data:
for i, el in enumerate(_lowercase ):
if len(_lowercase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(_lowercase ) )
return data_lists
def _A ( _lowercase , _lowercase ) -> list[list[float]]:
"""simple docstring"""
__UpperCamelCase = []
for dlist, weight in zip(_lowercase , _lowercase ):
__UpperCamelCase = min(_lowercase )
__UpperCamelCase = max(_lowercase )
__UpperCamelCase = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
__UpperCamelCase = f'''Invalid weight of {weight:f} provided'''
raise ValueError(_lowercase )
score_lists.append(_lowercase )
return score_lists
def _A ( _lowercase ) -> list[float]:
"""simple docstring"""
__UpperCamelCase = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(_lowercase ):
__UpperCamelCase = final_scores[j] + ele
return final_scores
def _A ( _lowercase , _lowercase ) -> list[list[float]]:
"""simple docstring"""
__UpperCamelCase = get_data(_lowercase )
__UpperCamelCase = calculate_each_score(_lowercase , _lowercase )
__UpperCamelCase = generate_final_scores(_lowercase )
# append scores to source data
for i, ele in enumerate(_lowercase ):
source_data[i].append(_lowercase )
return source_data
| 310 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__A, '''tf_padding''' ) )
self.parent.assertTrue(hasattr(__A, '''depth_multiplier''' ) )
class __UpperCAmelCase :
def __init__( self : int, __A : List[Any], __A : str=1_3, __A : Dict=3, __A : int=3_2, __A : int=0.2_5, __A : List[str]=8, __A : int=8, __A : Dict=6, __A : str=3_2, __A : Any=True, __A : str=True, __A : int=True, __A : Union[str, Any]="relu6", __A : Any=1_2_8_0, __A : List[Any]=0.1, __A : Optional[Any]=0.0_2, __A : Tuple=True, __A : List[Any]=True, __A : str=1_0, __A : Optional[Any]=None, ):
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : List[str] = batch_size
UpperCAmelCase : List[str] = num_channels
UpperCAmelCase : str = image_size
UpperCAmelCase : Optional[int] = depth_multiplier
UpperCAmelCase : Union[str, Any] = depth_divisible_by
UpperCAmelCase : Optional[Any] = min_depth
UpperCAmelCase : List[str] = expand_ratio
UpperCAmelCase : Dict = tf_padding
UpperCAmelCase : str = output_stride
UpperCAmelCase : Union[str, Any] = first_layer_is_expansion
UpperCAmelCase : List[Any] = finegrained_output
UpperCAmelCase : Optional[Any] = hidden_act
UpperCAmelCase : str = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
UpperCAmelCase : Optional[Any] = classifier_dropout_prob
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : List[str] = is_training
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : Union[str, Any] = initializer_range
UpperCAmelCase : Any = scope
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Dict = None
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Dict = ids_tensor([self.batch_size], self.num_labels )
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
UpperCAmelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : Any ):
return MobileNetVaConfig(
num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, depth_divisible_by=self.depth_divisible_by, min_depth=self.min_depth, expand_ratio=self.expand_ratio, output_stride=self.output_stride, first_layer_is_expansion=self.first_layer_is_expansion, finegrained_output=self.finegrained_output, hidden_act=self.hidden_act, tf_padding=self.tf_padding, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def __magic_name__ ( self : List[Any], __A : Dict, __A : Optional[Any], __A : Optional[int], __A : Union[str, Any] ):
UpperCAmelCase : Any = MobileNetVaModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[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,
), )
self.parent.assertEqual(
result.pooler_output.shape, (self.batch_size, self.last_hidden_size), )
def __magic_name__ ( self : str, __A : Union[str, Any], __A : Dict, __A : Optional[Any], __A : str ):
UpperCAmelCase : Optional[int] = self.num_labels
UpperCAmelCase : Any = MobileNetVaForImageClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[int] = model(__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any], __A : Optional[Any], __A : List[str], __A : Dict, __A : Dict ):
UpperCAmelCase : Tuple = self.num_labels
UpperCAmelCase : Dict = MobileNetVaForSemanticSegmentation(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = 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,
), )
UpperCAmelCase : Optional[Any] = 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 __magic_name__ ( self : Tuple ):
UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = config_and_inputs
UpperCAmelCase : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
"""feature-extraction""": MobileNetVaModel,
"""image-classification""": MobileNetVaForImageClassification,
"""image-segmentation""": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = MobileNetVaModelTester(self )
UpperCAmelCase : List[Any] = MobileNetVaConfigTester(self, config_class=__A, has_text_modality=__A )
def __magic_name__ ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''' )
def __magic_name__ ( self : Tuple ):
pass
@unittest.skip(reason='''MobileNetV2 does not output attentions''' )
def __magic_name__ ( self : Any ):
pass
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class(__A )
UpperCAmelCase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : int ):
def check_hidden_states_output(__A : Any, __A : Optional[Any], __A : str ):
UpperCAmelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
UpperCAmelCase : Dict = model(**self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Optional[Any] = outputs.hidden_states
UpperCAmelCase : List[Any] = 1_6
self.assertEqual(len(__A ), __A )
UpperCAmelCase , UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : Tuple = True
check_hidden_states_output(__A, __A, __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def __magic_name__ ( self : int ):
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__A )
@slow
def __magic_name__ ( self : Dict ):
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] = MobileNetVaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def a__ ( ) -> int:
UpperCAmelCase : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[Any] ):
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''' ) if is_vision_available() else None
)
@slow
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : List[Any] = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''' ).to(__A )
UpperCAmelCase : Optional[int] = self.default_image_processor
UpperCAmelCase : Optional[Any] = prepare_img()
UpperCAmelCase : Dict = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : str = model(**__A )
# verify the logits
UpperCAmelCase : int = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3], __A, atol=1E-4 ) )
@slow
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Tuple = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = model.to(__A )
UpperCAmelCase : Tuple = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
UpperCAmelCase : List[Any] = prepare_img()
UpperCAmelCase : int = image_processor(images=__A, return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
UpperCAmelCase : Union[str, Any] = model(**__A )
UpperCAmelCase : Optional[Any] = outputs.logits
# verify the logits
UpperCAmelCase : Tuple = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape, __A )
UpperCAmelCase : Tuple = torch.tensor(
[
[[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
[[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
[[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
], device=__A, )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], __A, atol=1E-4 ) )
| 336 | 0 |
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 __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self : Dict ) -> List[Any]:
"""simple docstring"""
snake_case_ = tempfile.mkdtemp()
# fmt: off
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
snake_case_ = dict(zip(__A , range(len(__A ) ) ) )
snake_case_ = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
snake_case_ = {'''unk_token''': '''<unk>'''}
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
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(__A ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__A ) )
snake_case_ = {
'''do_resize''': True,
'''size''': 2_0,
'''do_center_crop''': True,
'''crop_size''': 1_8,
'''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],
}
snake_case_ = os.path.join(self.tmpdirname , __A )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(__A , __A )
def lowerCAmelCase__ ( self : Optional[int] , **_lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **__A )
def lowerCAmelCase__ ( self : Optional[int] , **_lowerCAmelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__A )
def lowerCAmelCase__ ( self : Optional[Any] , **_lowerCAmelCase : List[Any] ) -> str:
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__A )
def lowerCAmelCase__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
snake_case_ = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
snake_case_ = [Image.fromarray(np.moveaxis(__A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_rust_tokenizer()
snake_case_ = self.get_image_processor()
snake_case_ = CLIPSegProcessor(tokenizer=__A , image_processor=__A )
processor_slow.save_pretrained(self.tmpdirname )
snake_case_ = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=__A )
snake_case_ = CLIPSegProcessor(tokenizer=__A , image_processor=__A )
processor_fast.save_pretrained(self.tmpdirname )
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 , __A )
self.assertIsInstance(processor_fast.tokenizer , __A )
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 , __A )
self.assertIsInstance(processor_fast.image_processor , __A )
def lowerCAmelCase__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
snake_case_ = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
snake_case_ = self.get_image_processor(do_normalize=__A , padding_value=1.0 )
snake_case_ = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __A )
def lowerCAmelCase__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = CLIPSegProcessor(tokenizer=__A , image_processor=__A )
snake_case_ = self.prepare_image_inputs()
snake_case_ = image_processor(__A , return_tensors="np" )
snake_case_ = processor(images=__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 lowerCAmelCase__ ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = CLIPSegProcessor(tokenizer=__A , image_processor=__A )
snake_case_ = '''lower newer'''
snake_case_ = processor(text=__A )
snake_case_ = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase__ ( self : Dict ) -> List[Any]:
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = CLIPSegProcessor(tokenizer=__A , image_processor=__A )
snake_case_ = '''lower newer'''
snake_case_ = self.prepare_image_inputs()
snake_case_ = processor(text=__A , images=__A )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(__A ):
processor()
def lowerCAmelCase__ ( self : List[Any] ) -> str:
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = CLIPSegProcessor(tokenizer=__A , image_processor=__A )
snake_case_ = self.prepare_image_inputs()
snake_case_ = self.prepare_image_inputs()
snake_case_ = processor(images=__A , visual_prompt=__A )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "conditional_pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(__A ):
processor()
def lowerCAmelCase__ ( self : Optional[int] ) -> Any:
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = CLIPSegProcessor(tokenizer=__A , image_processor=__A )
snake_case_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case_ = processor.batch_decode(__A )
snake_case_ = tokenizer.batch_decode(__A )
self.assertListEqual(__A , __A )
| 159 |
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 : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """codegen"""
UpperCamelCase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any, __A : Optional[int]=5_0_4_0_0, __A : Tuple=2_0_4_8, __A : Optional[int]=2_0_4_8, __A : List[str]=4_0_9_6, __A : List[str]=2_8, __A : Union[str, Any]=1_6, __A : Tuple=6_4, __A : Union[str, Any]=None, __A : Union[str, Any]="gelu_new", __A : Any=0.0, __A : Dict=0.0, __A : str=0.0, __A : Optional[int]=1E-5, __A : Any=0.0_2, __A : Any=True, __A : Union[str, Any]=5_0_2_5_6, __A : List[str]=5_0_2_5_6, __A : int=False, **__A : List[Any], ):
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Tuple = n_ctx
UpperCAmelCase : Tuple = n_positions
UpperCAmelCase : Optional[int] = n_embd
UpperCAmelCase : Union[str, Any] = n_layer
UpperCAmelCase : List[str] = n_head
UpperCAmelCase : Tuple = n_inner
UpperCAmelCase : int = rotary_dim
UpperCAmelCase : List[Any] = activation_function
UpperCAmelCase : List[str] = resid_pdrop
UpperCAmelCase : Optional[Any] = embd_pdrop
UpperCAmelCase : str = attn_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : Any = bos_token_id
UpperCAmelCase : List[str] = eos_token_id
super().__init__(
bos_token_id=__A, eos_token_id=__A, tie_word_embeddings=__A, **__A )
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Any, __A : PretrainedConfig, __A : str = "default", __A : List[PatchingSpec] = None, __A : bool = False, ):
super().__init__(__A, task=__A, patching_specs=__A, use_past=__A )
if not getattr(self._config, '''pad_token_id''', __A ):
# TODO: how to do that better?
UpperCAmelCase : Union[str, Any] = 0
@property
def __magic_name__ ( self : str ):
UpperCAmelCase : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__A, direction='''inputs''' )
UpperCAmelCase : int = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
UpperCAmelCase : List[Any] = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __magic_name__ ( self : Dict ):
return self._config.n_layer
@property
def __magic_name__ ( self : List[str] ):
return self._config.n_head
def __magic_name__ ( self : str, __A : PreTrainedTokenizer, __A : int = -1, __A : int = -1, __A : bool = False, __A : Optional[TensorType] = None, ):
UpperCAmelCase : Union[str, Any] = super(__A, self ).generate_dummy_inputs(
__A, batch_size=__A, seq_length=__A, is_pair=__A, framework=__A )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase : Union[str, 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
UpperCAmelCase , UpperCAmelCase : str = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
UpperCAmelCase : str = seqlen + 2
UpperCAmelCase : Optional[int] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase : Optional[int] = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
UpperCAmelCase : Union[str, Any] = common_inputs['''attention_mask''']
if self.use_past:
UpperCAmelCase : Optional[Any] = ordered_inputs['''attention_mask'''].dtype
UpperCAmelCase : Dict = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__A, __A, dtype=__A )], dim=1 )
return ordered_inputs
@property
def __magic_name__ ( self : Tuple ):
return 1_3
| 336 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _lowerCAmelCase ( lowerCamelCase__ ,unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = CpmAntTokenizer
__UpperCAmelCase : Tuple = False
def _lowercase ( self : Dict ):
super().setUp()
__lowercase = [
'''<d>''',
'''</d>''',
'''<s>''',
'''</s>''',
'''</_>''',
'''<unk>''',
'''<pad>''',
'''</n>''',
'''我''',
'''是''',
'''C''',
'''P''',
'''M''',
'''A''',
'''n''',
'''t''',
]
__lowercase = 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] ) )
@tooslow
def _lowercase ( self : Any ):
__lowercase = CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b" )
__lowercase = '''今天天气真好！'''
__lowercase = ['''今天''', '''天气''', '''真''', '''好''', '''！''']
__lowercase = tokenizer.tokenize(__A )
self.assertListEqual(__A, __A )
__lowercase = '''今天天气真好！'''
__lowercase = [tokenizer.bos_token] + tokens
__lowercase = [6, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), __A )
__lowercase = tokenizer.decode(__A )
self.assertEqual(__A, __A )
| 17 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 336 | 0 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class _lowercase ( lowerCamelCase__):
"""simple docstring"""
def __init__( self : str , __lowerCamelCase : str = "▁" , __lowerCamelCase : bool = True , __lowerCamelCase : Union[str, AddedToken] = "<unk>" , __lowerCamelCase : Union[str, AddedToken] = "</s>" , __lowerCamelCase : Union[str, AddedToken] = "<pad>" , ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
lowerCamelCase__ : int = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
lowerCamelCase__ : Optional[Any] = token_dict['''token''']
lowerCamelCase__ : Any = Tokenizer(Unigram() )
lowerCamelCase__ : Dict = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}" ) , " " ),
normalizers.Lowercase(),
] )
lowerCamelCase__ : Dict = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__A , add_prefix_space=__A ),
pre_tokenizers.Digits(individual_digits=__A ),
pre_tokenizers.Punctuation(),
] )
lowerCamelCase__ : Optional[Any] = decoders.Metaspace(replacement=__A , add_prefix_space=__A )
lowerCamelCase__ : Dict = TemplateProcessing(
single=f"$A {self.special_tokens['eos']['token']}" , special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])] , )
lowerCamelCase__ : Optional[int] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__A , __A )
def lowerCAmelCase ( self : Any , __lowerCamelCase : Union[str, List[str]] , __lowerCamelCase : int = 8000 , __lowerCamelCase : bool = True , ):
'''simple docstring'''
lowerCamelCase__ : Any = trainers.UnigramTrainer(
vocab_size=__A , special_tokens=self.special_tokens_list , show_progress=__A , )
if isinstance(__A , __A ):
lowerCamelCase__ : str = [files]
self._tokenizer.train(__A , trainer=__A )
self.add_unk_id()
def lowerCAmelCase ( self : Any , __lowerCamelCase : Union[Iterator[str], Iterator[Iterator[str]]] , __lowerCamelCase : int = 8000 , __lowerCamelCase : bool = True , ):
'''simple docstring'''
lowerCamelCase__ : str = trainers.UnigramTrainer(
vocab_size=__A , special_tokens=self.special_tokens_list , show_progress=__A , )
self._tokenizer.train_from_iterator(__A , trainer=__A )
self.add_unk_id()
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = json.loads(self._tokenizer.to_str() )
lowerCamelCase__ : int = self.special_tokens['''unk''']['''id''']
lowerCamelCase__ : Any = Tokenizer.from_str(json.dumps(__A ) )
| 184 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class __UpperCAmelCase :
# setable values
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None # sigma(t_i)
@classmethod
def __magic_name__ ( cls : Any ):
return cls()
@dataclass
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@property
def __magic_name__ ( self : Optional[int] ):
return True
@register_to_config
def __init__( self : Optional[int], __A : float = 0.0_2, __A : float = 1_0_0, __A : float = 1.0_0_7, __A : float = 8_0, __A : float = 0.0_5, __A : float = 5_0, ):
pass
def __magic_name__ ( self : Optional[Any] ):
return KarrasVeSchedulerState.create()
def __magic_name__ ( self : int, __A : KarrasVeSchedulerState, __A : int, __A : Tuple = () ):
UpperCAmelCase : Optional[Any] = jnp.arange(0, __A )[::-1].copy()
UpperCAmelCase : Union[str, Any] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__A, schedule=jnp.array(__A, dtype=jnp.floataa ), timesteps=__A, )
def __magic_name__ ( self : List[Any], __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : random.KeyArray, ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase : int = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1 )
else:
UpperCAmelCase : Optional[int] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase : Union[str, Any] = random.split(__A, num=1 )
UpperCAmelCase : List[str] = self.config.s_noise * random.normal(key=__A, shape=sample.shape )
UpperCAmelCase : Tuple = sigma + gamma * sigma
UpperCAmelCase : List[str] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : int = sample_hat + sigma_hat * model_output
UpperCAmelCase : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase : int = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Tuple, __A : KarrasVeSchedulerState, __A : jnp.ndarray, __A : float, __A : float, __A : jnp.ndarray, __A : jnp.ndarray, __A : jnp.ndarray, __A : bool = True, ):
UpperCAmelCase : Tuple = sample_prev + sigma_prev * model_output
UpperCAmelCase : List[str] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__A, derivative=__A, state=__A )
def __magic_name__ ( self : Optional[Any], __A : KarrasVeSchedulerState, __A : Optional[int], __A : int, __A : Union[str, Any] ):
raise NotImplementedError()
| 336 | 0 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase ( ) -> tuple[list[int], int]:
'''simple docstring'''
__UpperCAmelCase : str = [randint(-1_0_0_0 , 1_0_0_0 ) for i in range(1_0 )]
__UpperCAmelCase : Any = randint(-5_0_0_0 , 5_0_0_0 )
return (arr, r)
UpperCAmelCase : Any = make_dataset()
def lowerCamelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : int ) -> tuple[int, ...]:
'''simple docstring'''
for triplet in permutations(_UpperCamelCase , 3 ):
if sum(_UpperCamelCase ) == target:
return tuple(sorted(_UpperCamelCase ) )
return (0, 0, 0)
def lowerCamelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : int ) -> tuple[int, int, int]:
'''simple docstring'''
arr.sort()
__UpperCAmelCase : Tuple = len(_UpperCamelCase )
for i in range(n - 1 ):
__UpperCAmelCase : int = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def lowerCamelCase ( ) -> tuple[float, float]:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
__UpperCAmelCase : Tuple = '''
triplet_sum1(*dataset)
'''
__UpperCAmelCase : List[str] = '''
triplet_sum2(*dataset)
'''
__UpperCAmelCase : Tuple = repeat(setup=_UpperCamelCase , stmt=_UpperCamelCase , repeat=5 , number=1_0_0_0_0 )
__UpperCAmelCase : str = repeat(setup=_UpperCamelCase , stmt=_UpperCamelCase , repeat=5 , number=1_0_0_0_0 )
return (min(_UpperCamelCase ), min(_UpperCamelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase : int = solution_times()
print(F"The time for naive implementation is {times[0]}.")
print(F"The time for optimized implementation is {times[1]}.")
| 115 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __UpperCAmelCase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def a__ ( ) -> Dict:
if os.name == "nt":
UpperCAmelCase : List[str] = CursorInfo()
UpperCAmelCase : List[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Dict = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def a__ ( ) -> Optional[int]:
if os.name == "nt":
UpperCAmelCase : int = CursorInfo()
UpperCAmelCase : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
UpperCAmelCase : Any = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase , ctypes.byref(UpperCAmelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def a__ ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 336 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__magic_name__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__):
warnings.warn(
"""The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DeiTImageProcessor instead.""" , __A , )
super().__init__(*__A , **__A)
| 100 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 336 | 0 |
def lowercase_ ( ):
return [list(range(1000 - i , -1000 - i , -1)) for i in range(1000)]
UpperCamelCase = generate_large_matrix()
UpperCamelCase = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def lowercase_ ( _lowerCamelCase : list[list[int]]):
assert all(row == sorted(_lowerCamelCase , reverse=_lowerCamelCase) for row in grid)
assert all(list(_lowerCamelCase) == sorted(_lowerCamelCase , reverse=_lowerCamelCase) for col in zip(*_lowerCamelCase))
def lowercase_ ( _lowerCamelCase : list[int]):
lowercase__ : Union[str, Any] = 0
lowercase__ : Union[str, Any] = len(_lowerCamelCase) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowercase__ : Any = (left + right) // 2
lowercase__ : List[str] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowercase__ : List[Any] = mid + 1
else:
lowercase__ : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(_lowerCamelCase)
def lowercase_ ( _lowerCamelCase : list[list[int]]):
lowercase__ : Dict = 0
lowercase__ : List[Any] = len(grid[0])
for i in range(len(_lowerCamelCase)):
lowercase__ : Any = find_negative_index(grid[i][:bound])
total += bound
return (len(_lowerCamelCase) * len(grid[0])) - total
def lowercase_ ( _lowerCamelCase : list[list[int]]):
return len([number for row in grid for number in row if number < 0])
def lowercase_ ( _lowerCamelCase : list[list[int]]):
lowercase__ : Tuple = 0
for row in grid:
for i, number in enumerate(_lowerCamelCase):
if number < 0:
total += len(_lowerCamelCase) - i
break
return total
def lowercase_ ( ):
from timeit import timeit
print("Running benchmarks")
lowercase__ : Union[str, Any] = (
'''from __main__ import count_negatives_binary_search, '''
'''count_negatives_brute_force, count_negatives_brute_force_with_break, grid'''
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowercase__ : Any = timeit(f'''{func}(grid=grid)''' , setup=_lowerCamelCase , number=500)
print(f'''{func}() took {time:0.4f} seconds''')
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 87 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
UpperCAmelCase : str = number_of_bytes // partitions
UpperCAmelCase : Dict = []
for i in range(UpperCAmelCase ):
UpperCAmelCase : int = i * bytes_per_partition + 1
UpperCAmelCase : Optional[int] = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 200 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( UpperCAmelCase : Dict=None ) -> Optional[int]:
if subparsers is not None:
UpperCAmelCase : Tuple = subparsers.add_parser('''tpu-config''' , description=_description )
else:
UpperCAmelCase : Dict = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
UpperCAmelCase : Optional[int] = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=UpperCAmelCase , default=UpperCAmelCase , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=UpperCAmelCase , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=UpperCAmelCase , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
UpperCAmelCase : Union[str, Any] = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=UpperCAmelCase , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase )
return parser
def a__ ( UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
UpperCAmelCase : List[str] = defaults.commands
if not args.tpu_name:
UpperCAmelCase : Tuple = defaults.tpu_name
if not args.tpu_zone:
UpperCAmelCase : int = defaults.tpu_zone
if args.accelerate_version == "dev":
UpperCAmelCase : Tuple = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
UpperCAmelCase : Dict = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , UpperCAmelCase ):
UpperCAmelCase : Optional[int] = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
UpperCAmelCase : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , UpperCAmelCase ):
UpperCAmelCase : int = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
UpperCAmelCase : Optional[int] = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
UpperCAmelCase : int = '''; '''.join(UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
UpperCAmelCase : Any = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {" ".join(UpperCAmelCase )}''' )
return
subprocess.run(UpperCAmelCase )
print('''Successfully setup pod.''' )
def a__ ( ) -> Any:
UpperCAmelCase : Any = tpu_command_parser()
UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(UpperCAmelCase )
| 336 | 0 |
"""simple docstring"""
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
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class __snake_case ( lowerCamelCase__ ):
a__ = """instructblip_vision_model"""
def __init__( self , lowercase=14_08 , lowercase=61_44 , lowercase=39 , lowercase=16 , lowercase=2_24 , lowercase=14 , lowercase="gelu" , lowercase=1e-6 , lowercase=0.0 , lowercase=1e-10 , lowercase=True , **lowercase , ) -> List[str]:
'''simple docstring'''
super().__init__(**__A)
a__: Union[str, Any] = hidden_size
a__: Optional[Any] = intermediate_size
a__: Any = num_hidden_layers
a__: Any = num_attention_heads
a__: str = patch_size
a__: str = image_size
a__: List[Any] = initializer_range
a__: str = attention_dropout
a__: List[Any] = layer_norm_eps
a__: Any = hidden_act
a__: List[str] = qkv_bias
@classmethod
def lowerCamelCase_ ( cls , lowercase , **lowercase) -> Dict:
'''simple docstring'''
cls._set_token_in_kwargs(__A)
a__: Optional[int] = cls.get_config_dict(__A , **__A)
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type') == "instructblip":
a__: int = 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 __snake_case ( lowerCamelCase__ ):
a__ = """instructblip_qformer"""
def __init__( self , lowercase=3_05_22 , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_12 , lowercase=0.02 , lowercase=1e-12 , lowercase=0 , lowercase="absolute" , lowercase=2 , lowercase=14_08 , **lowercase , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=__A , **__A)
a__: List[Any] = vocab_size
a__: int = hidden_size
a__: List[str] = num_hidden_layers
a__: Optional[Any] = num_attention_heads
a__: Optional[int] = hidden_act
a__: Dict = intermediate_size
a__: Any = hidden_dropout_prob
a__: Dict = attention_probs_dropout_prob
a__: Union[str, Any] = max_position_embeddings
a__: Optional[int] = initializer_range
a__: List[Any] = layer_norm_eps
a__: Any = position_embedding_type
a__: Tuple = cross_attention_frequency
a__: List[str] = encoder_hidden_size
@classmethod
def lowerCamelCase_ ( cls , lowercase , **lowercase) -> List[str]:
'''simple docstring'''
cls._set_token_in_kwargs(__A)
a__: Optional[int] = cls.get_config_dict(__A , **__A)
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type') == "instructblip":
a__: Any = 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 __snake_case ( lowerCamelCase__ ):
a__ = """instructblip"""
a__ = True
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase=32 , **lowercase) -> Tuple:
'''simple docstring'''
super().__init__(**__A)
if vision_config is None:
a__: Any = {}
logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.')
if qformer_config is None:
a__: Any = {}
logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.')
if text_config is None:
a__: List[str] = {}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).')
a__: Dict = InstructBlipVisionConfig(**__A)
a__: List[str] = InstructBlipQFormerConfig(**__A)
a__: List[str] = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
a__: List[str] = CONFIG_MAPPING[text_model_type](**__A)
a__: Tuple = self.text_config.tie_word_embeddings
a__: Tuple = self.text_config.is_encoder_decoder
a__: List[Any] = num_query_tokens
a__: Any = self.vision_config.hidden_size
a__: Dict = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
a__: Any = 1.0
a__: List[Any] = 0.02
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase , lowercase , **lowercase , ) -> Optional[Any]:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__A , )
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Optional[int] = copy.deepcopy(self.__dict__)
a__: Tuple = self.vision_config.to_dict()
a__: str = self.qformer_config.to_dict()
a__: Union[str, Any] = self.text_config.to_dict()
a__: Any = self.__class__.model_type
return output
| 290 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 0 |
"""simple docstring"""
from __future__ import annotations
_A = 1.6_021E-19 # units = C
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 171 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __UpperCAmelCase ( lowerCamelCase__ ):
def __get__( self : Tuple, __A : Optional[Any], __A : Optional[int]=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
UpperCAmelCase : str = '''__cached_''' + self.fget.__name__
UpperCAmelCase : int = getattr(__A, __A, __A )
if cached is None:
UpperCAmelCase : Any = self.fget(__A )
setattr(__A, __A, __A )
return cached
def a__ ( UpperCAmelCase : Optional[Any] ) -> Any:
UpperCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_torch_fx_proxy(UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(UpperCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return isinstance(UpperCAmelCase , np.ndarray )
def a__ ( UpperCAmelCase : str ) -> Tuple:
return _is_numpy(UpperCAmelCase )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
import torch
return isinstance(UpperCAmelCase , torch.Tensor )
def a__ ( UpperCAmelCase : str ) -> List[Any]:
return False if not is_torch_available() else _is_torch(UpperCAmelCase )
def a__ ( UpperCAmelCase : Tuple ) -> List[str]:
import torch
return isinstance(UpperCAmelCase , torch.device )
def a__ ( UpperCAmelCase : Any ) -> Any:
return False if not is_torch_available() else _is_torch_device(UpperCAmelCase )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
import torch
if isinstance(UpperCAmelCase , UpperCAmelCase ):
if hasattr(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
else:
return False
return isinstance(UpperCAmelCase , torch.dtype )
def a__ ( UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase )
def a__ ( UpperCAmelCase : Any ) -> str:
import tensorflow as tf
return isinstance(UpperCAmelCase , tf.Tensor )
def a__ ( UpperCAmelCase : int ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[str] ) -> Tuple:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCAmelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(UpperCAmelCase )
return type(UpperCAmelCase ) == tf.Tensor
def a__ ( UpperCAmelCase : int ) -> List[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase )
def a__ ( UpperCAmelCase : List[Any] ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCAmelCase , jnp.ndarray )
def a__ ( UpperCAmelCase : List[Any] ) -> Optional[int]:
return False if not is_flax_available() else _is_jax(UpperCAmelCase )
def a__ ( UpperCAmelCase : int ) -> Tuple:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return [to_py_obj(UpperCAmelCase ) for o in obj]
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase ).tolist()
elif isinstance(UpperCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( UpperCAmelCase : Any ) -> List[str]:
if isinstance(UpperCAmelCase , (dict, UserDict) ):
return {k: to_numpy(UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(UpperCAmelCase , (list, tuple) ):
return np.array(UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCAmelCase ):
return np.asarray(UpperCAmelCase )
else:
return obj
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(__A ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
UpperCAmelCase : int = getattr(self, class_fields[0].name )
UpperCAmelCase : str = all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__A ):
if isinstance(__A, __A ):
UpperCAmelCase : Tuple = first_field.items()
UpperCAmelCase : Any = True
else:
try:
UpperCAmelCase : Optional[Any] = iter(__A )
UpperCAmelCase : Optional[Any] = True
except TypeError:
UpperCAmelCase : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__A ):
if (
not isinstance(__A, (list, tuple) )
or not len(__A ) == 2
or not isinstance(element[0], __A )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
UpperCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
UpperCAmelCase : Union[str, Any] = element[1]
elif first_field is not None:
UpperCAmelCase : Union[str, Any] = first_field
else:
for field in class_fields:
UpperCAmelCase : Optional[Any] = getattr(self, field.name )
if v is not None:
UpperCAmelCase : Optional[int] = v
def __delitem__( self : Union[str, Any], *__A : str, **__A : Tuple ):
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : List[str], *__A : Union[str, Any], **__A : Optional[Any] ):
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Any, *__A : Dict, **__A : str ):
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def __magic_name__ ( self : Dict, *__A : int, **__A : Dict ):
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : List[str], __A : List[str] ):
if isinstance(__A, __A ):
UpperCAmelCase : int = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[Any], __A : Dict, __A : Union[str, Any] ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__A, __A )
super().__setattr__(__A, __A )
def __setitem__( self : Dict, __A : List[Any], __A : Union[str, Any] ):
# Will raise a KeyException if needed
super().__setitem__(__A, __A )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__A, __A )
def __magic_name__ ( self : List[str] ):
return tuple(self[k] for k in self.keys() )
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@classmethod
def __magic_name__ ( cls : List[Any], __A : Tuple ):
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """longest"""
UpperCamelCase = """max_length"""
UpperCamelCase = """do_not_pad"""
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
UpperCamelCase = """np"""
UpperCamelCase = """jax"""
class __UpperCAmelCase :
def __init__( self : Any, __A : List[ContextManager] ):
UpperCAmelCase : Tuple = context_managers
UpperCAmelCase : Tuple = ExitStack()
def __enter__( self : Any ):
for context_manager in self.context_managers:
self.stack.enter_context(__A )
def __exit__( self : List[Any], *__A : Union[str, Any], **__A : Dict ):
self.stack.__exit__(*__A, **__A )
def a__ ( UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : int = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : List[str] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a__ ( UpperCAmelCase : Dict ) -> Any:
UpperCAmelCase : List[Any] = model_class.__name__
UpperCAmelCase : Union[str, Any] = infer_framework(UpperCAmelCase )
if framework == "tf":
UpperCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a__ ( UpperCAmelCase : MutableMapping , UpperCAmelCase : str = "" , UpperCAmelCase : str = "." ) -> Union[str, Any]:
def _flatten_dict(UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str]="" , UpperCAmelCase : Any="." ):
for k, v in d.items():
UpperCAmelCase : List[str] = str(UpperCAmelCase ) + delimiter + str(UpperCAmelCase ) if parent_key else k
if v and isinstance(UpperCAmelCase , UpperCAmelCase ):
yield from flatten_dict(UpperCAmelCase , UpperCAmelCase , delimiter=UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
@contextmanager
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : bool = False ) -> Optional[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str]=None ) -> Optional[Any]:
if is_numpy_array(UpperCAmelCase ):
return np.transpose(UpperCAmelCase , axes=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.T if axes is None else array.permute(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(UpperCAmelCase , perm=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.transpose(UpperCAmelCase , axes=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : Optional[int] ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.reshape(*UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(UpperCAmelCase , UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.reshape(UpperCAmelCase , UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int]=None ) -> Any:
if is_numpy_array(UpperCAmelCase ):
return np.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.squeeze(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : str , UpperCAmelCase : int ) -> str:
if is_numpy_array(UpperCAmelCase ):
return np.expand_dims(UpperCAmelCase , UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.unsqueeze(dim=UpperCAmelCase )
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return jnp.expand_dims(UpperCAmelCase , axis=UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : Dict ) -> List[str]:
if is_numpy_array(UpperCAmelCase ):
return np.size(UpperCAmelCase )
elif is_torch_tensor(UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(UpperCAmelCase ):
import tensorflow as tf
return tf.size(UpperCAmelCase )
elif is_jax_tensor(UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(UpperCAmelCase )}.''' )
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Dict:
for key, value in auto_map.items():
if isinstance(UpperCAmelCase , (tuple, list) ):
UpperCAmelCase : List[Any] = [f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
UpperCAmelCase : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a__ ( UpperCAmelCase : Tuple ) -> Union[str, Any]:
for base_class in inspect.getmro(UpperCAmelCase ):
UpperCAmelCase : Any = base_class.__module__
UpperCAmelCase : Dict = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 336 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_A = {"processing_layoutxlm": ["LayoutXLMProcessor"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["LayoutXLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["LayoutXLMTokenizerFast"]
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
_A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 278 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LayoutLMTokenizer
UpperCamelCase = LayoutLMTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def __magic_name__ ( self : Any ):
super().setUp()
UpperCAmelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
UpperCAmelCase : int = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __magic_name__ ( self : Union[str, Any], **__A : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **__A )
def __magic_name__ ( self : Optional[int], __A : int ):
UpperCAmelCase : Optional[Any] = '''UNwant\u00E9d,running'''
UpperCAmelCase : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__A, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ), [7, 4, 5, 1_0, 8, 9] )
def __magic_name__ ( self : Optional[int] ):
pass
| 336 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> str:
"""simple docstring"""
with open(_lowercase ) as metadata_file:
__UpperCamelCase = json.load(_lowercase )
__UpperCamelCase = LukeConfig(use_entity_aware_attention=_lowercase , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
__UpperCamelCase = torch.load(_lowercase , map_location='cpu' )['''module''']
# Load the entity vocab file
__UpperCamelCase = load_original_entity_vocab(_lowercase )
# add an entry for [MASK2]
__UpperCamelCase = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
__UpperCamelCase = XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
__UpperCamelCase = AddedToken('<ent>' , lstrip=_lowercase , rstrip=_lowercase )
__UpperCamelCase = AddedToken('<ent2>' , lstrip=_lowercase , rstrip=_lowercase )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'''Saving tokenizer to {pytorch_dump_folder_path}''' )
tokenizer.save_pretrained(_lowercase )
with open(os.path.join(_lowercase , 'tokenizer_config.json' ) , 'r' ) as f:
__UpperCamelCase = json.load(_lowercase )
__UpperCamelCase = '''MLukeTokenizer'''
with open(os.path.join(_lowercase , 'tokenizer_config.json' ) , 'w' ) as f:
json.dump(_lowercase , _lowercase )
with open(os.path.join(_lowercase , MLukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(_lowercase , _lowercase )
__UpperCamelCase = MLukeTokenizer.from_pretrained(_lowercase )
# Initialize the embeddings of the special tokens
__UpperCamelCase = tokenizer.convert_tokens_to_ids(['@'] )[0]
__UpperCamelCase = tokenizer.convert_tokens_to_ids(['#'] )[0]
__UpperCamelCase = state_dict['''embeddings.word_embeddings.weight''']
__UpperCamelCase = word_emb[ent_init_index].unsqueeze(0 )
__UpperCamelCase = word_emb[enta_init_index].unsqueeze(0 )
__UpperCamelCase = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
__UpperCamelCase = state_dict[bias_name]
__UpperCamelCase = decoder_bias[ent_init_index].unsqueeze(0 )
__UpperCamelCase = decoder_bias[enta_init_index].unsqueeze(0 )
__UpperCamelCase = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__UpperCamelCase = f'''encoder.layer.{layer_index}.attention.self.'''
__UpperCamelCase = state_dict[prefix + matrix_name]
__UpperCamelCase = state_dict[prefix + matrix_name]
__UpperCamelCase = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__UpperCamelCase = state_dict['''entity_embeddings.entity_embeddings.weight''']
__UpperCamelCase = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
__UpperCamelCase = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
__UpperCamelCase = state_dict['''entity_predictions.bias''']
__UpperCamelCase = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
__UpperCamelCase = torch.cat([entity_prediction_bias, entity_mask_bias] )
__UpperCamelCase = LukeForMaskedLM(config=_lowercase ).eval()
state_dict.pop('entity_predictions.decoder.weight' )
state_dict.pop('lm_head.decoder.weight' )
state_dict.pop('lm_head.decoder.bias' )
__UpperCamelCase = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('lm_head' ) or key.startswith('entity_predictions' )):
__UpperCamelCase = state_dict[key]
else:
__UpperCamelCase = state_dict[key]
__UpperCamelCase = model.load_state_dict(_lowercase , strict=_lowercase )
if set(_lowercase ) != {"luke.embeddings.position_ids"}:
raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}''' )
if set(_lowercase ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f'''Unexpected missing_keys: {missing_keys}''' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
__UpperCamelCase = MLukeTokenizer.from_pretrained(_lowercase , task='entity_classification' )
__UpperCamelCase = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
__UpperCamelCase = (0, 9)
__UpperCamelCase = tokenizer(_lowercase , entity_spans=[span] , return_tensors='pt' )
__UpperCamelCase = model(**_lowercase )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
__UpperCamelCase = torch.Size((1, 33, 7_68) )
__UpperCamelCase = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , _lowercase , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
__UpperCamelCase = torch.Size((1, 1, 7_68) )
__UpperCamelCase = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
f''' {expected_shape}''' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , _lowercase , atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
__UpperCamelCase = MLukeTokenizer.from_pretrained(_lowercase )
__UpperCamelCase = '''Tokyo is the capital of <mask>.'''
__UpperCamelCase = (24, 30)
__UpperCamelCase = tokenizer(_lowercase , entity_spans=[span] , return_tensors='pt' )
__UpperCamelCase = model(**_lowercase )
__UpperCamelCase = encoding['''input_ids'''][0].tolist()
__UpperCamelCase = input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
__UpperCamelCase = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(_lowercase )
__UpperCamelCase = outputs.entity_logits[0][0].argmax().item()
__UpperCamelCase = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('en:' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(_lowercase ) )
model.save_pretrained(_lowercase )
def _A ( _lowercase ) -> str:
"""simple docstring"""
__UpperCamelCase = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
__UpperCamelCase = [json.loads(_lowercase ) for line in open(_lowercase )]
__UpperCamelCase = {}
for entry in data:
__UpperCamelCase = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
__UpperCamelCase = entity_id
break
__UpperCamelCase = f'''{language}:{entity_name}'''
__UpperCamelCase = entity_id
return new_mapping
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
__snake_case = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 310 |
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 __UpperCAmelCase :
def __init__( self : Any, __A : str, __A : Dict=1_3, __A : int=3_0, __A : Tuple=2, __A : Union[str, Any]=3, __A : Any=True, __A : str=True, __A : Dict=3_2, __A : List[Any]=2, __A : Optional[Any]=4, __A : Union[str, Any]=3_7, __A : int="gelu", __A : int=0.1, __A : List[Any]=0.1, __A : Tuple=1_0, __A : Tuple=0.0_2, __A : Any=3, __A : List[str]=0.6, __A : Any=None, ):
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : List[str] = image_size
UpperCAmelCase : Dict = patch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : Union[str, Any] = use_labels
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Optional[int] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Tuple = mask_ratio
UpperCAmelCase : Any = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : Optional[Any] ):
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=__A, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def __magic_name__ ( self : str, __A : List[Any], __A : Any, __A : Any ):
UpperCAmelCase : Optional[Any] = TFViTMAEModel(config=__A )
UpperCAmelCase : Tuple = model(__A, training=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple, __A : str, __A : int, __A : str ):
UpperCAmelCase : Dict = TFViTMAEForPreTraining(__A )
UpperCAmelCase : int = model(__A, training=__A )
# expected sequence length = num_patches
UpperCAmelCase : int = (self.image_size // self.patch_size) ** 2
UpperCAmelCase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase : Tuple = 1
UpperCAmelCase : List[Any] = TFViTMAEForPreTraining(__A )
UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : List[Any] = model(__A, training=__A )
UpperCAmelCase : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , 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 __magic_name__ ( self : List[str] ):
UpperCAmelCase : List[Any] = TFViTMAEModelTester(self )
UpperCAmelCase : int = ConfigTester(self, config_class=__A, has_text_modality=__A, hidden_size=3_7 )
def __magic_name__ ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ):
pass
def __magic_name__ ( self : List[str] ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[str] = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer) )
UpperCAmelCase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A, tf.keras.layers.Layer ) )
def __magic_name__ ( self : str ):
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Any = model_class(__A )
UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : int = [*signature.parameters.keys()]
UpperCAmelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __magic_name__ ( self : int ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : str = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : Dict = model(__A, noise=__A )
UpperCAmelCase : Any = copy.deepcopy(self._prepare_for_class(__A, __A ) )
UpperCAmelCase : Union[str, Any] = model(**__A, noise=__A )
UpperCAmelCase : Dict = outputs_dict[0].numpy()
UpperCAmelCase : Tuple = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ), 1E-6 )
def __magic_name__ ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(__A : Union[str, Any] ):
UpperCAmelCase : str = {}
for k, v in inputs_dict.items():
if tf.is_tensor(__A ):
UpperCAmelCase : Tuple = v.numpy()
else:
UpperCAmelCase : str = np.array(__A )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : Any = self._prepare_for_class(__A, __A )
UpperCAmelCase : Optional[int] = prepare_numpy_arrays(__A )
UpperCAmelCase : str = model(__A, noise=__A )
UpperCAmelCase : str = model(**__A, noise=__A )
self.assert_outputs_same(__A, __A )
def __magic_name__ ( self : int, __A : str, __A : Union[str, Any], __A : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
UpperCAmelCase : Any = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : int = tf.constant(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase : List[Any] = tf_noise
super().check_pt_tf_models(__A, __A, __A )
def __magic_name__ ( self : str ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Union[str, Any] = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(__A )
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(__A, __A ),)
if isinstance(__A, __A )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(__A, '''_keras_serializable''', __A )
}
UpperCAmelCase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase : str = tf.convert_to_tensor(__A )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCAmelCase : Tuple = main_layer_class(__A )
UpperCAmelCase : int = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCAmelCase : List[Any] = tf.keras.Model(__A, outputs=main_layer(__A ) )
UpperCAmelCase : List[Any] = model(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = os.path.join(__A, '''keras_model.h5''' )
model.save(__A )
UpperCAmelCase : List[str] = tf.keras.models.load_model(
__A, custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(__A, tf.keras.Model )
UpperCAmelCase : Tuple = model(__A )
self.assert_outputs_same(__A, __A )
@slow
def __magic_name__ ( self : Dict ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : int = model_class(__A )
UpperCAmelCase : List[str] = self._prepare_for_class(__A, __A )
UpperCAmelCase : Union[str, Any] = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : Optional[int] = outputs.last_hidden_state.numpy()
UpperCAmelCase : Union[str, Any] = 0
else:
UpperCAmelCase : Optional[int] = outputs.logits.numpy()
UpperCAmelCase : int = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A, saved_model=__A )
UpperCAmelCase : Dict = model_class.from_pretrained(__A )
UpperCAmelCase : str = model(__A, noise=__A )
if model_class.__name__ == "TFViTMAEModel":
UpperCAmelCase : int = after_outputs['''last_hidden_state'''].numpy()
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Any = after_outputs['''logits'''].numpy()
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A, 1E-5 )
def __magic_name__ ( self : Optional[Any] ):
# make mask reproducible
np.random.seed(2 )
UpperCAmelCase , UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = int((config.image_size // config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict = model_class(__A )
UpperCAmelCase : int = self._prepare_for_class(__A, __A )
UpperCAmelCase : List[Any] = model(__A, noise=__A )
UpperCAmelCase : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(__A )
UpperCAmelCase : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCAmelCase : str = model_class.from_config(model.config )
UpperCAmelCase : List[str] = new_model(__A ) # Build model
new_model.set_weights(model.get_weights() )
UpperCAmelCase : Tuple = new_model(__A, noise=__A )
self.assert_outputs_same(__A, __A )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def __magic_name__ ( self : Optional[int] ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def __magic_name__ ( self : Tuple ):
pass
@slow
def __magic_name__ ( self : str ):
UpperCAmelCase : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(__A )
def a__ ( ) -> Dict:
UpperCAmelCase : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[str] ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCAmelCase : Tuple = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
UpperCAmelCase : List[str] = self.default_image_processor
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : str = image_processor(images=__A, 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)
UpperCAmelCase : Optional[int] = ViTMAEConfig()
UpperCAmelCase : int = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCAmelCase : Optional[int] = model(**__A, noise=__A )
# verify the logits
UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
self.assertEqual(outputs.logits.shape, __A )
UpperCAmelCase : List[str] = tf.convert_to_tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3], __A, atol=1E-4 )
| 336 | 0 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
SCREAMING_SNAKE_CASE :int = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
SCREAMING_SNAKE_CASE :List[str] = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"
SCREAMING_SNAKE_CASE :Optional[Any] = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=[\"About 95 species are currently accepted .\"]\n >>> predictions=[\"About 95 you now get in .\"]\n >>> references=[[\"About 95 species are currently known .\"]]\n >>> wiki_split = datasets.load_metric(\"wiki_split\")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"
def _lowerCAmelCase ( lowerCAmelCase_ :List[str] )->Tuple:
'''simple docstring'''
def remove_articles(lowerCAmelCase_ :Dict ):
snake_case_ = re.compile(r"\b(a|an|the)\b" , re.UNICODE )
return re.sub(lowerCAmelCase_ , " " , lowerCAmelCase_ )
def white_space_fix(lowerCAmelCase_ :List[str] ):
return " ".join(text.split() )
def remove_punc(lowerCAmelCase_ :str ):
snake_case_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCAmelCase_ :Optional[int] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_ ) ) ) )
def _lowerCAmelCase ( lowerCAmelCase_ :str , lowerCAmelCase_ :str )->int:
'''simple docstring'''
return int(normalize_answer(lowerCAmelCase_ ) == normalize_answer(lowerCAmelCase_ ) )
def _lowerCAmelCase ( lowerCAmelCase_ :List[str] , lowerCAmelCase_ :Union[str, Any] )->List[Any]:
'''simple docstring'''
snake_case_ = [any(compute_exact(lowerCAmelCase_ , lowerCAmelCase_ ) for ref in refs ) for pred, refs in zip(lowerCAmelCase_ , lowerCAmelCase_ )]
return (sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ )) * 100
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[int] , lowerCAmelCase_ :Any , lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Optional[int] )->List[str]:
'''simple docstring'''
snake_case_ = [rgram for rgrams in rgramslist for rgram in rgrams]
snake_case_ = Counter(lowerCAmelCase_ )
snake_case_ = Counter(lowerCAmelCase_ )
snake_case_ = Counter()
for sgram, scount in sgramcounter.items():
snake_case_ = scount * numref
snake_case_ = Counter(lowerCAmelCase_ )
snake_case_ = Counter()
for cgram, ccount in cgramcounter.items():
snake_case_ = ccount * numref
# KEEP
snake_case_ = sgramcounter_rep & cgramcounter_rep
snake_case_ = keepgramcounter_rep & rgramcounter
snake_case_ = sgramcounter_rep & rgramcounter
snake_case_ = 0
snake_case_ = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
snake_case_ = 1
snake_case_ = 1
if len(lowerCAmelCase_ ) > 0:
snake_case_ = keeptmpscorea / len(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
snake_case_ = keeptmpscorea / sum(keepgramcounterall_rep.values() )
snake_case_ = 0
if keepscore_precision > 0 or keepscore_recall > 0:
snake_case_ = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
snake_case_ = sgramcounter_rep - cgramcounter_rep
snake_case_ = delgramcounter_rep - rgramcounter
snake_case_ = sgramcounter_rep - rgramcounter
snake_case_ = 0
snake_case_ = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
snake_case_ = 1
if len(lowerCAmelCase_ ) > 0:
snake_case_ = deltmpscorea / len(lowerCAmelCase_ )
# ADDITION
snake_case_ = set(lowerCAmelCase_ ) - set(lowerCAmelCase_ )
snake_case_ = set(lowerCAmelCase_ ) & set(lowerCAmelCase_ )
snake_case_ = set(lowerCAmelCase_ ) - set(lowerCAmelCase_ )
snake_case_ = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
snake_case_ = 1
snake_case_ = 1
if len(lowerCAmelCase_ ) > 0:
snake_case_ = addtmpscore / len(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
snake_case_ = addtmpscore / len(lowerCAmelCase_ )
snake_case_ = 0
if addscore_precision > 0 or addscore_recall > 0:
snake_case_ = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def _lowerCAmelCase ( lowerCAmelCase_ :str , lowerCAmelCase_ :List[str] , lowerCAmelCase_ :Optional[Any] )->Dict:
'''simple docstring'''
snake_case_ = len(lowerCAmelCase_ )
snake_case_ = ssent.split(" " )
snake_case_ = csent.split(" " )
snake_case_ = []
snake_case_ = []
snake_case_ = []
snake_case_ = []
snake_case_ = []
snake_case_ = []
snake_case_ = []
snake_case_ = []
snake_case_ = []
snake_case_ = []
for rsent in rsents:
snake_case_ = rsent.split(" " )
snake_case_ = []
snake_case_ = []
snake_case_ = []
ragramslist.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
snake_case_ = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
snake_case_ = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
snake_case_ = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
snake_case_ = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
snake_case_ = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
snake_case_ = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
snake_case_ = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
snake_case_ = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
snake_case_ = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(lowerCAmelCase_ )
(snake_case_) = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
(snake_case_) = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
(snake_case_) = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
(snake_case_) = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
snake_case_ = sum([delascore, delascore, delascore, delascore] ) / 4
snake_case_ = sum([addascore, addascore, addascore, addascore] ) / 4
snake_case_ = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def _lowerCAmelCase ( lowerCAmelCase_ :Tuple , lowerCAmelCase_ :bool = True , lowerCAmelCase_ :str = "13a" , lowerCAmelCase_ :bool = True )->int:
'''simple docstring'''
if lowercase:
snake_case_ = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
snake_case_ = sacrebleu.metrics.bleu._get_tokenizer(lowerCAmelCase_ )()(lowerCAmelCase_ )
else:
snake_case_ = sacrebleu.TOKENIZERS[tokenizer]()(lowerCAmelCase_ )
elif tokenizer == "moses":
snake_case_ = sacremoses.MosesTokenizer().tokenize(lowerCAmelCase_ , return_str=lowerCAmelCase_ , escape=lowerCAmelCase_ )
elif tokenizer == "penn":
snake_case_ = sacremoses.MosesTokenizer().penn_tokenize(lowerCAmelCase_ , return_str=lowerCAmelCase_ )
else:
snake_case_ = sentence
if not return_str:
snake_case_ = normalized_sent.split()
return normalized_sent
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[Any] , lowerCAmelCase_ :int , lowerCAmelCase_ :Any )->List[Any]:
'''simple docstring'''
if not (len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )):
raise ValueError("Sources length must match predictions and references lengths." )
snake_case_ = 0
for src, pred, refs in zip(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
sari_score += SARIsent(normalize(lowerCAmelCase_ ) , normalize(lowerCAmelCase_ ) , [normalize(lowerCAmelCase_ ) for sent in refs] )
snake_case_ = sari_score / len(lowerCAmelCase_ )
return 100 * sari_score
def _lowerCAmelCase ( lowerCAmelCase_ :Tuple , lowerCAmelCase_ :Tuple , lowerCAmelCase_ :Union[str, Any]="exp" , lowerCAmelCase_ :Tuple=None , lowerCAmelCase_ :Union[str, Any]=False , lowerCAmelCase_ :str=False , lowerCAmelCase_ :List[Any]=False , )->Optional[Any]:
'''simple docstring'''
snake_case_ = len(references[0] )
if any(len(lowerCAmelCase_ ) != references_per_prediction for refs in references ):
raise ValueError("Sacrebleu requires the same number of references for each prediction" )
snake_case_ = [[refs[i] for refs in references] for i in range(lowerCAmelCase_ )]
snake_case_ = sacrebleu.corpus_bleu(
lowerCAmelCase_ , lowerCAmelCase_ , smooth_method=lowerCAmelCase_ , smooth_value=lowerCAmelCase_ , force=lowerCAmelCase_ , lowercase=lowerCAmelCase_ , use_effective_order=lowerCAmelCase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def lowerCAmelCase__ ( self : List[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.Sequence(datasets.Value("string" , id="sequence" ) , id="references" ),
} ) , codebase_urls=[
"https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
"https://github.com/cocoxu/simplification/blob/master/SARI.py",
"https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
"https://github.com/mjpost/sacreBLEU",
] , reference_urls=[
"https://www.aclweb.org/anthology/Q16-1029.pdf",
"https://github.com/mjpost/sacreBLEU",
"https://en.wikipedia.org/wiki/BLEU",
"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
] , )
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ) -> str:
"""simple docstring"""
snake_case_ = {}
result.update({"sari": compute_sari(sources=__A , predictions=__A , references=__A )} )
result.update({"sacrebleu": compute_sacrebleu(predictions=__A , references=__A )} )
result.update({"exact": compute_em(predictions=__A , references=__A )} )
return result
| 159 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
_lowerCamelCase : List[Any] = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
_lowerCamelCase : str = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 336 | 0 |
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