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"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
a = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-classification''',
'''language-modeling''',
'''summarization''',
'''token-classification''',
'''question-answering''',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
a = logging.getLogger()
def _snake_case ( ) -> List[Any]:
'''simple docstring'''
_A = argparse.ArgumentParser()
parser.add_argument('-f' )
_A = parser.parse_args()
return args.f
def _snake_case ( _snake_case : int , _snake_case : Tuple="eval" ) -> Optional[Any]:
'''simple docstring'''
_A = os.path.join(_snake_case , F'''{split}_results.json''' )
if os.path.exists(_snake_case ):
with open(_snake_case , 'r' ) as f:
return json.load(_snake_case )
raise ValueError(F'''can\'t find {path}''' )
a = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = self.get_auto_remove_tmp_dir()
_A = F'''
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(_UpperCAmelCase , 'argv' , _UpperCAmelCase ):
run_flax_glue.main()
_A = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
@slow
def lowerCAmelCase_ ( self : Tuple ):
_A = self.get_auto_remove_tmp_dir()
_A = F'''
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(_UpperCAmelCase , 'argv' , _UpperCAmelCase ):
run_clm_flax.main()
_A = get_results(_UpperCAmelCase )
self.assertLess(result['eval_perplexity'] , 100 )
@slow
def lowerCAmelCase_ ( self : List[str] ):
_A = self.get_auto_remove_tmp_dir()
_A = F'''
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
'''.split()
with patch.object(_UpperCAmelCase , 'argv' , _UpperCAmelCase ):
run_summarization_flax.main()
_A = get_results(_UpperCAmelCase , split='test' )
self.assertGreaterEqual(result['test_rouge1'] , 10 )
self.assertGreaterEqual(result['test_rouge2'] , 2 )
self.assertGreaterEqual(result['test_rougeL'] , 7 )
self.assertGreaterEqual(result['test_rougeLsum'] , 7 )
@slow
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = self.get_auto_remove_tmp_dir()
_A = F'''
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
'''.split()
with patch.object(_UpperCAmelCase , 'argv' , _UpperCAmelCase ):
run_mlm_flax.main()
_A = get_results(_UpperCAmelCase )
self.assertLess(result['eval_perplexity'] , 42 )
@slow
def lowerCAmelCase_ ( self : int ):
_A = self.get_auto_remove_tmp_dir()
_A = F'''
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
'''.split()
with patch.object(_UpperCAmelCase , 'argv' , _UpperCAmelCase ):
run_ta_mlm_flax.main()
_A = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.42 )
@slow
def lowerCAmelCase_ ( self : Tuple ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
_A = 7 if get_gpu_count() > 1 else 2
_A = self.get_auto_remove_tmp_dir()
_A = F'''
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
'''.split()
with patch.object(_UpperCAmelCase , 'argv' , _UpperCAmelCase ):
run_flax_ner.main()
_A = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
self.assertGreaterEqual(result['eval_f1'] , 0.3 )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.get_auto_remove_tmp_dir()
_A = F'''
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
'''.split()
with patch.object(_UpperCAmelCase , 'argv' , _UpperCAmelCase ):
run_qa.main()
_A = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result['eval_f1'] , 30 )
self.assertGreaterEqual(result['eval_exact'] , 30 )
| 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[str] ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple=True ):
_A = ()
for resnet, attn in zip(self.resnets , self.attentions ):
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[Any] ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[str]=True ):
_A = ()
for resnet in self.resnets:
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any]=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : int , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int]=True ):
for resnet in self.resnets:
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Dict ):
# there is always at least one resnet
_A = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_A = []
for _ in range(self.num_layers ):
_A = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
_A = attentions
def __call__( self : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int]=True ):
_A = self.resnets[0](_UpperCAmelCase , _UpperCAmelCase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
return hidden_states
| 315 | 1 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
a = {
'''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
'''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
'''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
'''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
'''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
'''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
'''mask_downscaling.0''': '''mask_embed.conv1''',
'''mask_downscaling.1''': '''mask_embed.layer_norm1''',
'''mask_downscaling.3''': '''mask_embed.conv2''',
'''mask_downscaling.4''': '''mask_embed.layer_norm2''',
'''mask_downscaling.6''': '''mask_embed.conv3''',
'''point_embeddings''': '''point_embed''',
'''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
'''image_encoder''': '''vision_encoder''',
'''neck.0''': '''neck.conv1''',
'''neck.1''': '''neck.layer_norm1''',
'''neck.2''': '''neck.conv2''',
'''neck.3''': '''neck.layer_norm2''',
'''patch_embed.proj''': '''patch_embed.projection''',
'''.norm''': '''.layer_norm''',
'''blocks''': '''layers''',
}
def _snake_case ( _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
_A = {}
state_dict.pop('pixel_mean' , _snake_case )
state_dict.pop('pixel_std' , _snake_case )
_A = R'.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
_A = key.replace(_snake_case , _snake_case )
if re.match(_snake_case , _snake_case ):
_A = int(re.match(_snake_case , _snake_case ).group(2 ) )
if layer_nb == 0:
_A = key.replace('layers.0' , 'proj_in' )
elif layer_nb == 1:
_A = key.replace('layers.1' , 'layers.0' )
elif layer_nb == 2:
_A = key.replace('layers.2' , 'proj_out' )
_A = value
_A = model_state_dict[
'prompt_encoder.shared_embedding.positional_embedding'
]
return model_state_dict
def _snake_case ( _snake_case : Any , _snake_case : Dict , _snake_case : List[str] , _snake_case : str="ybelkada/segment-anything" ) -> str:
'''simple docstring'''
_A = hf_hub_download(_snake_case , F'''checkpoints/{model_name}.pth''' )
if "sam_vit_b" in model_name:
_A = SamConfig()
elif "sam_vit_l" in model_name:
_A = SamVisionConfig(
hidden_size=10_24 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
_A = SamConfig(
vision_config=_snake_case , )
elif "sam_vit_h" in model_name:
_A = SamVisionConfig(
hidden_size=12_80 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
_A = SamConfig(
vision_config=_snake_case , )
_A = torch.load(_snake_case , map_location='cpu' )
_A = replace_keys(_snake_case )
_A = SamImageProcessor()
_A = SamProcessor(image_processor=_snake_case )
_A = SamModel(_snake_case )
hf_model.load_state_dict(_snake_case )
_A = hf_model.to('cuda' )
_A = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
_A = Image.open(requests.get(_snake_case , stream=_snake_case ).raw ).convert('RGB' )
_A = [[[4_00, 6_50]]]
_A = [[1]]
_A = processor(images=np.array(_snake_case ) , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
_A = hf_model(**_snake_case )
_A = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579890251159668
_A = processor(
images=np.array(_snake_case ) , input_points=_snake_case , input_labels=_snake_case , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
_A = hf_model(**_snake_case )
_A = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9712603092193604
_A = ((75, 2_75, 17_25, 8_50),)
_A = processor(images=np.array(_snake_case ) , input_boxes=_snake_case , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
_A = hf_model(**_snake_case )
_A = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8686015605926514
# Test with 2 points and 1 image.
_A = [[[4_00, 6_50], [8_00, 6_50]]]
_A = [[1, 1]]
_A = processor(
images=np.array(_snake_case ) , input_points=_snake_case , input_labels=_snake_case , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
_A = hf_model(**_snake_case )
_A = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9936047792434692
if __name__ == "__main__":
a = argparse.ArgumentParser()
a = ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
parser.add_argument(
'''--model_name''',
default='''sam_vit_h_4b8939''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
parser.add_argument(
'''--model_hub_id''',
default='''ybelkada/segment-anything''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
a = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
_A = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
_A = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
_A = numpy.random.rand(3 , 1 )
# Real output values provided.
_A = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
_A = numpy.zeros(output_array.shape )
def lowerCAmelCase_ ( self : List[str] ):
_A = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def lowerCAmelCase_ ( self : Optional[int] ):
_A = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
_A = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
_A = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : int , _UpperCAmelCase : bool ):
for iteration in range(1 , iterations + 1 ):
_A = self.feedforward()
self.back_propagation()
if give_loss:
_A = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F'''Iteration {iteration} Loss: {loss}''' )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : numpy.ndarray ):
_A = input_arr
_A = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return 1 / (1 + numpy.exp(-value ))
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return (value) * (1 - (value))
def _snake_case ( ) -> int:
'''simple docstring'''
_A = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
_A = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
_A = TwoHiddenLayerNeuralNetwork(
input_array=_snake_case , output_array=_snake_case )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_snake_case , iterations=10 , give_loss=_snake_case )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 315 | 1 |
"""simple docstring"""
import os
import sys
import unittest
a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
a = os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
a = os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : List[Any] ):
_A = get_test_to_tester_mapping(_UpperCAmelCase )
_A = get_test_to_tester_mapping(_UpperCAmelCase )
_A = {'BertModelTest': 'BertModelTester'}
_A = {
'BlipModelTest': 'BlipModelTester',
'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
'BlipTextModelTest': 'BlipTextModelTester',
'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
'BlipVQAModelTest': 'BlipVQAModelTester',
'BlipVisionModelTest': 'BlipVisionModelTester',
}
self.assertEqual(get_test_info.to_json(_UpperCAmelCase ) , _UpperCAmelCase )
self.assertEqual(get_test_info.to_json(_UpperCAmelCase ) , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = get_model_to_test_mapping(_UpperCAmelCase )
_A = get_model_to_test_mapping(_UpperCAmelCase )
_A = {
'BertForMaskedLM': ['BertModelTest'],
'BertForMultipleChoice': ['BertModelTest'],
'BertForNextSentencePrediction': ['BertModelTest'],
'BertForPreTraining': ['BertModelTest'],
'BertForQuestionAnswering': ['BertModelTest'],
'BertForSequenceClassification': ['BertModelTest'],
'BertForTokenClassification': ['BertModelTest'],
'BertLMHeadModel': ['BertModelTest'],
'BertModel': ['BertModelTest'],
}
_A = {
'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
'BlipForQuestionAnswering': ['BlipVQAModelTest'],
'BlipModel': ['BlipModelTest'],
'BlipTextModel': ['BlipTextModelTest'],
'BlipVisionModel': ['BlipVisionModelTest'],
}
self.assertEqual(get_test_info.to_json(_UpperCAmelCase ) , _UpperCAmelCase )
self.assertEqual(get_test_info.to_json(_UpperCAmelCase ) , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Any ):
_A = get_model_to_tester_mapping(_UpperCAmelCase )
_A = get_model_to_tester_mapping(_UpperCAmelCase )
_A = {
'BertForMaskedLM': ['BertModelTester'],
'BertForMultipleChoice': ['BertModelTester'],
'BertForNextSentencePrediction': ['BertModelTester'],
'BertForPreTraining': ['BertModelTester'],
'BertForQuestionAnswering': ['BertModelTester'],
'BertForSequenceClassification': ['BertModelTester'],
'BertForTokenClassification': ['BertModelTester'],
'BertLMHeadModel': ['BertModelTester'],
'BertModel': ['BertModelTester'],
}
_A = {
'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
'BlipForQuestionAnswering': ['BlipVQAModelTester'],
'BlipModel': ['BlipModelTester'],
'BlipTextModel': ['BlipTextModelTester'],
'BlipVisionModel': ['BlipVisionModelTester'],
}
self.assertEqual(get_test_info.to_json(_UpperCAmelCase ) , _UpperCAmelCase )
self.assertEqual(get_test_info.to_json(_UpperCAmelCase ) , _UpperCAmelCase )
| 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : T ):
_A = data
_A = None
def __str__( self : str ):
return F'''{self.data}'''
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple ):
_A = None
def __iter__( self : List[Any] ):
_A = self.top
while node:
yield node.data
_A = node.next
def __str__( self : Union[str, Any] ):
return "->".join([str(_UpperCAmelCase ) for item in self] )
def __len__( self : List[Any] ):
return len(tuple(iter(self ) ) )
def lowerCAmelCase_ ( self : str ):
return self.top is None
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : T ):
_A = Node(_UpperCAmelCase )
if not self.is_empty():
_A = self.top
_A = node
def lowerCAmelCase_ ( self : Dict ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _UpperCAmelCase )
_A = self.top
_A = self.top.next
return pop_node.data
def lowerCAmelCase_ ( self : Tuple ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 315 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
'''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
'''kssteven/ibert-roberta-large-mnli''': (
'''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
),
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = '''ibert'''
def __init__( self : Dict , _UpperCAmelCase : List[str]=30_522 , _UpperCAmelCase : List[Any]=768 , _UpperCAmelCase : Any=12 , _UpperCAmelCase : Optional[Any]=12 , _UpperCAmelCase : str=3_072 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : List[str]=512 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : str=1E-1_2 , _UpperCAmelCase : Optional[int]=1 , _UpperCAmelCase : Tuple=0 , _UpperCAmelCase : str=2 , _UpperCAmelCase : Dict="absolute" , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Optional[Any]="none" , **_UpperCAmelCase : List[Any] , ):
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = quant_mode
_A = force_dequant
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
@property
def lowerCAmelCase_ ( self : int ):
if self.task == "multiple-choice":
_A = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_A = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Union[str, Any] ):
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : int = MBartConfig
UpperCAmelCase : Any = {}
UpperCAmelCase : Tuple = '''gelu'''
def __init__( self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : List[Any]=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : int=99 , _UpperCAmelCase : Union[str, Any]=32 , _UpperCAmelCase : Dict=2 , _UpperCAmelCase : List[str]=4 , _UpperCAmelCase : Union[str, Any]=37 , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : List[str]=20 , _UpperCAmelCase : Any=2 , _UpperCAmelCase : Optional[int]=1 , _UpperCAmelCase : Any=0 , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = eos_token_id
_A = pad_token_id
_A = bos_token_id
def lowerCAmelCase_ ( self : Tuple ):
_A = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_A = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_A = tf.concat([input_ids, eos_tensor] , axis=1 )
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_A = prepare_mbart_inputs_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return config, inputs_dict
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int ):
_A = TFMBartModel(config=_UpperCAmelCase ).get_decoder()
_A = inputs_dict['input_ids']
_A = input_ids[:1, :]
_A = inputs_dict['attention_mask'][:1, :]
_A = inputs_dict['head_mask']
_A = 1
# first forward pass
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , head_mask=_UpperCAmelCase , use_cache=_UpperCAmelCase )
_A , _A = outputs.to_tuple()
_A = past_key_values[1]
def _snake_case ( _snake_case : str , _snake_case : List[str] , _snake_case : str , _snake_case : Dict=None , _snake_case : Optional[int]=None , _snake_case : List[str]=None , _snake_case : str=None , _snake_case : Optional[int]=None , ) -> Tuple:
'''simple docstring'''
if attention_mask is None:
_A = tf.cast(tf.math.not_equal(_snake_case , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_A = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_A = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_A = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_A = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
UpperCAmelCase : Union[str, Any] = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
UpperCAmelCase : Tuple = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCAmelCase : Optional[int] = True
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Union[str, Any] = False
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] ):
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def lowerCAmelCase_ ( self : str ):
_A = TFMBartModelTester(self )
_A = ConfigTester(self , config_class=_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : Dict ):
_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase )
@require_sentencepiece
@require_tokenizers
@require_tf
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
UpperCAmelCase : str = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
UpperCAmelCase : Dict = '''facebook/mbart-large-en-ro'''
@cached_property
def lowerCAmelCase_ ( self : List[str] ):
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def lowerCAmelCase_ ( self : List[str] ):
_A = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def lowerCAmelCase_ ( self : Union[str, Any] , **_UpperCAmelCase : Any ):
_A = self.translate_src_text(**_UpperCAmelCase )
self.assertListEqual(self.expected_text , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] , **_UpperCAmelCase : List[Any] ):
_A = self.tokenizer(self.src_text , **_UpperCAmelCase , return_tensors='tf' )
_A = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
_A = self.tokenizer.batch_decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
return generated_words
@slow
def lowerCAmelCase_ ( self : Any ):
self._assert_generated_batch_equal_expected()
| 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def _snake_case ( _snake_case : str ) -> list[str]:
'''simple docstring'''
return word_by_signature[signature(_snake_case )]
a = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
a = sorted({word.strip().lower() for word in data.splitlines()})
a = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
a = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 315 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json''',
'''Salesforce/blip-vqa-capfit-large''': (
'''https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'''
),
'''Salesforce/blip-image-captioning-base''': (
'''https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'''
),
'''Salesforce/blip-image-captioning-large''': (
'''https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'''
),
'''Salesforce/blip-itm-base-coco''': '''https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json''',
'''Salesforce/blip-itm-large-coco''': '''https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json''',
'''Salesforce/blip-itm-base-flikr''': '''https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json''',
'''Salesforce/blip-itm-large-flikr''': (
'''https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'''
),
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Dict = '''blip_text_model'''
def __init__( self : int , _UpperCAmelCase : Optional[int]=30_524 , _UpperCAmelCase : List[str]=768 , _UpperCAmelCase : int=768 , _UpperCAmelCase : Tuple=3_072 , _UpperCAmelCase : Optional[Any]=768 , _UpperCAmelCase : List[Any]=12 , _UpperCAmelCase : List[Any]=8 , _UpperCAmelCase : List[Any]=512 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : int=1E-1_2 , _UpperCAmelCase : Union[str, Any]=0.0 , _UpperCAmelCase : Optional[int]=0.0 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : Optional[Any]=30_522 , _UpperCAmelCase : Tuple=2 , _UpperCAmelCase : List[Any]=0 , _UpperCAmelCase : Dict=102 , _UpperCAmelCase : Dict=True , _UpperCAmelCase : List[Any]=True , **_UpperCAmelCase : List[str] , ):
super().__init__(
pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , sep_token_id=_UpperCAmelCase , **_UpperCAmelCase , )
_A = vocab_size
_A = hidden_size
_A = encoder_hidden_size
_A = intermediate_size
_A = projection_dim
_A = hidden_dropout_prob
_A = num_hidden_layers
_A = num_attention_heads
_A = max_position_embeddings
_A = layer_norm_eps
_A = hidden_act
_A = initializer_range
_A = attention_probs_dropout_prob
_A = is_decoder
_A = use_cache
@classmethod
def lowerCAmelCase_ ( cls : Any , _UpperCAmelCase : Union[str, os.PathLike] , **_UpperCAmelCase : Any ):
cls._set_token_in_kwargs(_UpperCAmelCase )
_A , _A = cls.get_config_dict(_UpperCAmelCase , **_UpperCAmelCase )
# get the text config dict if we are loading from BlipConfig
if config_dict.get('model_type' ) == "blip":
_A = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = '''blip_vision_model'''
def __init__( self : List[str] , _UpperCAmelCase : Dict=768 , _UpperCAmelCase : Any=3_072 , _UpperCAmelCase : int=512 , _UpperCAmelCase : List[str]=12 , _UpperCAmelCase : List[str]=12 , _UpperCAmelCase : int=384 , _UpperCAmelCase : Any=16 , _UpperCAmelCase : Optional[int]="gelu" , _UpperCAmelCase : Optional[Any]=1E-5 , _UpperCAmelCase : Dict=0.0 , _UpperCAmelCase : List[Any]=1E-1_0 , **_UpperCAmelCase : int , ):
super().__init__(**_UpperCAmelCase )
_A = hidden_size
_A = intermediate_size
_A = projection_dim
_A = num_hidden_layers
_A = num_attention_heads
_A = patch_size
_A = image_size
_A = initializer_range
_A = attention_dropout
_A = layer_norm_eps
_A = hidden_act
@classmethod
def lowerCAmelCase_ ( cls : Union[str, Any] , _UpperCAmelCase : Union[str, os.PathLike] , **_UpperCAmelCase : Dict ):
cls._set_token_in_kwargs(_UpperCAmelCase )
_A , _A = cls.get_config_dict(_UpperCAmelCase , **_UpperCAmelCase )
# get the vision config dict if we are loading from BlipConfig
if config_dict.get('model_type' ) == "blip":
_A = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Dict = '''blip'''
UpperCAmelCase : Optional[Any] = True
def __init__( self : Dict , _UpperCAmelCase : Any=None , _UpperCAmelCase : int=None , _UpperCAmelCase : int=512 , _UpperCAmelCase : List[Any]=2.6592 , _UpperCAmelCase : Any=256 , **_UpperCAmelCase : Dict , ):
super().__init__(**_UpperCAmelCase )
if text_config is None:
_A = {}
logger.info('`text_config` is `None`. Initializing the `BlipTextConfig` with default values.' )
if vision_config is None:
_A = {}
logger.info('`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.' )
_A = BlipTextConfig(**_UpperCAmelCase )
_A = BlipVisionConfig(**_UpperCAmelCase )
_A = self.vision_config.hidden_size
_A = projection_dim
_A = logit_scale_init_value
_A = 1.0
_A = 0.02
_A = image_text_hidden_size
@classmethod
def lowerCAmelCase_ ( cls : Union[str, Any] , _UpperCAmelCase : BlipTextConfig , _UpperCAmelCase : BlipVisionConfig , **_UpperCAmelCase : int ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = copy.deepcopy(self.__dict__ )
_A = self.text_config.to_dict()
_A = self.vision_config.to_dict()
_A = self.__class__.model_type
return output
| 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The column name of the images in the files.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the training data.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the validation data.'''} )
UpperCAmelCase : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def lowerCAmelCase_ ( self : Dict ):
_A = {}
if self.train_dir is not None:
_A = self.train_dir
if self.validation_dir is not None:
_A = self.validation_dir
_A = data_files if data_files else None
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : str = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
UpperCAmelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase : str = field(default=__lowerCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCAmelCase : float = field(
default=0.75 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : float = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def _snake_case ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
_A = torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _snake_case ( ) -> List[str]:
'''simple docstring'''
_A = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A , _A , _A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , _snake_case , _snake_case )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_A = training_args.get_process_log_level()
logger.setLevel(_snake_case )
transformers.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
_A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_A = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _snake_case ) and data_args.train_val_split > 0.0:
_A = ds['train'].train_test_split(data_args.train_val_split )
_A = split['train']
_A = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_A = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
_A = ViTMAEConfig.from_pretrained(model_args.config_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_A = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_A = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
_A = ViTMAEForPreTraining(_snake_case )
if training_args.do_train:
_A = ds['train'].column_names
else:
_A = ds['validation'].column_names
if data_args.image_column_name is not None:
_A = data_args.image_column_name
elif "image" in column_names:
_A = 'image'
elif "img" in column_names:
_A = 'img'
else:
_A = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_A = image_processor.size['shortest_edge']
else:
_A = (image_processor.size['height'], image_processor.size['width'])
_A = Compose(
[
Lambda(lambda _snake_case : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_snake_case , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_snake_case : List[Any] ):
_A = [transforms(_snake_case ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
_A = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_snake_case )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
_A = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_snake_case )
# Compute absolute learning rate
_A = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_A = training_args.base_learning_rate * total_train_batch_size / 2_56
# Initialize our trainer
_A = Trainer(
model=_snake_case , args=_snake_case , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_snake_case , data_collator=_snake_case , )
# Training
if training_args.do_train:
_A = None
if training_args.resume_from_checkpoint is not None:
_A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A = last_checkpoint
_A = trainer.train(resume_from_checkpoint=_snake_case )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_A = trainer.evaluate()
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
# Write model card and (optionally) push to hub
_A = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def _snake_case ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
_A = [[] for _ in range(_snake_case )]
_A = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 or len(_snake_case ) <= key:
return input_string
for position, character in enumerate(_snake_case ):
_A = position % (lowest * 2) # puts it in bounds
_A = min(_snake_case , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(_snake_case )
_A = [''.join(_snake_case ) for row in temp_grid]
_A = ''.join(_snake_case )
return output_string
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
_A = []
_A = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1:
return input_string
_A = [[] for _ in range(_snake_case )] # generates template
for position in range(len(_snake_case ) ):
_A = position % (lowest * 2) # puts it in bounds
_A = min(_snake_case , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('*' )
_A = 0
for row in temp_grid: # fills in the characters
_A = input_string[counter : counter + len(_snake_case )]
grid.append(list(_snake_case ) )
counter += len(_snake_case )
_A = '' # reads as zigzag
for position in range(len(_snake_case ) ):
_A = position % (lowest * 2) # puts it in bounds
_A = min(_snake_case , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def _snake_case ( _snake_case : str ) -> dict[int, str]:
'''simple docstring'''
_A = {}
for key_guess in range(1 , len(_snake_case ) ): # tries every key
_A = decrypt(_snake_case , _snake_case )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer names
a = 70 # max width of quantizer names
def _snake_case ( _snake_case : int ) -> List[Any]:
'''simple docstring'''
_A = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=_snake_case , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=_snake_case , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=_snake_case , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=_snake_case , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=_snake_case , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=_snake_case , type=_snake_case , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=_snake_case , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def _snake_case ( _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
if args.calibrator == "max":
_A = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
_A = 'histogram'
elif args.calibrator == "mse":
_A = 'histogram'
else:
raise ValueError(F'''Invalid calibrator {args.calibrator}''' )
_A = QuantDescriptor(num_bits=args.aprec , calib_method=_snake_case )
_A = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(_snake_case )
quant_nn.QuantLinear.set_default_quant_desc_weight(_snake_case )
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Any=False , _snake_case : Union[str, Any]=False ) -> Optional[int]:
'''simple docstring'''
logger.info('Configuring Model for Quantization' )
logger.info(F'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_snake_case , ['embeddings'] , which='weight' , _disabled=_snake_case )
if args.quant_disable:
set_quantizer_by_name(_snake_case , [''] , _disabled=_snake_case )
if args.quant_disable_keyword:
set_quantizer_by_name(_snake_case , args.quant_disable_keyword , _disabled=_snake_case )
if args.quant_disable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_disable_layer_module] , _disabled=_snake_case )
if args.quant_enable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_enable_layer_module] , _disabled=_snake_case )
if args.recalibrate_weights:
recalibrate_weights(_snake_case )
if args.fuse_qkv:
fuse_qkv(_snake_case , _snake_case )
if args.clip_gelu:
clip_gelu(_snake_case , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str ) -> Any:
'''simple docstring'''
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F'''{name:80}: {module}''' )
def _snake_case ( _snake_case : List[Any] , _snake_case : List[Any] ) -> str:
'''simple docstring'''
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
def fusea(_snake_case : int , _snake_case : str , _snake_case : Optional[Any] ):
for mod in [qq, qk, qv]:
if not hasattr(_snake_case , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
_A = qq._amax.detach().item()
_A = qk._amax.detach().item()
_A = qv._amax.detach().item()
_A = max(_snake_case , _snake_case , _snake_case )
qq._amax.fill_(_snake_case )
qk._amax.fill_(_snake_case )
qv._amax.fill_(_snake_case )
logger.info(F''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(F'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def _snake_case ( _snake_case : int , _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
_A = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_snake_case )
_A = mod._input_quantizer._amax.data.detach().item()
logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def _snake_case ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
_A = mod.weight.shape[0]
_A = mod._weight_quantizer._amax.detach()
_A = torch.ones(_snake_case , dtype=amax.dtype , device=amax.device ) * amax
print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def _snake_case ( _snake_case : Dict ) -> Tuple:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
_A = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
_A = set(range(len(mod.weight.size() ) ) ) - axis_set
_A = pytorch_quantization.utils.reduce_amax(mod.weight , axis=_snake_case , keepdims=_snake_case ).detach()
logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
_A = amax
def _snake_case ( _snake_case : Tuple , _snake_case : List[str]=25 , _snake_case : str=1_80 , _snake_case : int=None ) -> List[Any]:
'''simple docstring'''
if ignore is None:
_A = []
elif not isinstance(_snake_case , _snake_case ):
_A = [ignore]
_A = 0
for name, mod in model.named_modules():
if not hasattr(_snake_case , 'weight' ):
continue
_A = max(_snake_case , len(_snake_case ) )
for name, mod in model.named_modules():
_A = getattr(_snake_case , '_input_quantizer' , _snake_case )
_A = getattr(_snake_case , '_weight_quantizer' , _snake_case )
if not hasattr(_snake_case , 'weight' ):
continue
if type(_snake_case ) in ignore:
continue
if [True for s in ignore if type(_snake_case ) is str and s in name]:
continue
_A = F'''Act:{input_q.extra_repr()}'''
_A = F'''Wgt:{weight_q.extra_repr()}'''
_A = F'''{name:{name_width}} {act_str} {wgt_str}'''
if len(_snake_case ) <= line_width:
logger.info(_snake_case )
else:
logger.info(F'''{name:{name_width}} {act_str}''' )
logger.info(F'''{" ":{name_width}} {wgt_str}''' )
def _snake_case ( _snake_case : Dict ) -> int:
'''simple docstring'''
_A = 0
for name, mod in model.named_modules():
if isinstance(_snake_case , pytorch_quantization.nn.TensorQuantizer ):
print(F'''{name:80} {mod}''' )
count += 1
print(F'''{count} TensorQuantizers found in model''' )
def _snake_case ( _snake_case : str , _snake_case : Dict , _snake_case : List[Any] , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
_A = getattr(_snake_case , _snake_case , _snake_case )
if quantizer_mod is not None:
assert hasattr(_snake_case , _snake_case )
setattr(_snake_case , _snake_case , _snake_case )
else:
logger.warning(F'''{name} has no {quantizer}''' )
def _snake_case ( _snake_case : Dict , _snake_case : Optional[int] , _snake_case : str="both" , **_snake_case : List[Any] ) -> str:
'''simple docstring'''
_A = F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(_snake_case , _snake_case , '_input_quantizer' , _snake_case , _snake_case )
if which in ["weight", "both"]:
set_quantizer(_snake_case , _snake_case , '_weight_quantizer' , _snake_case , _snake_case )
logger.info(_snake_case )
def _snake_case ( _snake_case : Any , _snake_case : int , **_snake_case : Dict ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_input_quantizer' ) or hasattr(_snake_case , '_weight_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
set_quantizers(_snake_case , _snake_case , **_snake_case )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
_A = F'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
setattr(_snake_case , _snake_case , _snake_case )
logger.info(_snake_case )
| 315 | 1 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _snake_case ( _snake_case : str , _snake_case : str , **_snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
_A = AutoConfig.from_pretrained(_snake_case , **_snake_case )
_A = AutoModelForSeqaSeqLM.from_config(_snake_case )
model.save_pretrained(_snake_case )
AutoTokenizer.from_pretrained(_snake_case ).save_pretrained(_snake_case )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'] , )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int]=False ):
_A = spearmanr(_UpperCAmelCase , _UpperCAmelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 315 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = '''lxmert'''
UpperCAmelCase : Tuple = {}
def __init__( self : List[Any] , _UpperCAmelCase : int=30_522 , _UpperCAmelCase : int=768 , _UpperCAmelCase : Any=12 , _UpperCAmelCase : int=9_500 , _UpperCAmelCase : Tuple=1_600 , _UpperCAmelCase : int=400 , _UpperCAmelCase : Optional[int]=3_072 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : Dict=512 , _UpperCAmelCase : List[Any]=2 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : Optional[Any]=1E-1_2 , _UpperCAmelCase : Optional[int]=9 , _UpperCAmelCase : Tuple=5 , _UpperCAmelCase : List[Any]=5 , _UpperCAmelCase : Union[str, Any]=2_048 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : Tuple=6.67 , _UpperCAmelCase : Any=True , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Any=True , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : List[Any]=True , **_UpperCAmelCase : Optional[int] , ):
_A = vocab_size
_A = hidden_size
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = num_qa_labels
_A = num_object_labels
_A = num_attr_labels
_A = l_layers
_A = x_layers
_A = r_layers
_A = visual_feat_dim
_A = visual_pos_dim
_A = visual_loss_normalizer
_A = task_matched
_A = task_mask_lm
_A = task_obj_predict
_A = task_qa
_A = visual_obj_loss
_A = visual_attr_loss
_A = visual_feat_loss
_A = {'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
super().__init__(**_UpperCAmelCase )
| 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A = b
if function(_snake_case ) == 0: # one of the a or b is a root for the function
return a
elif function(_snake_case ) == 0:
return b
elif (
function(_snake_case ) * function(_snake_case ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
_A = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(_snake_case ) == 0:
return mid
elif function(_snake_case ) * function(_snake_case ) < 0:
_A = mid
else:
_A = mid
_A = start + (end - start) / 2.0
return mid
def _snake_case ( _snake_case : float ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
a = _symbol_database.Default()
a = _descriptor_pool.Default().AddSerializedFile(
B'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)
a = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
a = None
a = B'''H\003'''
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
a = 45
a = 1_581
a = 1_517
a = 1_570
a = 1_584
a = 1_793
a = 1_795
a = 1_916
a = 1_864
a = 1_905
a = 1_919
a = 2_429
a = 2_208
a = 2_418
a = 2_323
a = 2_407
# @@protoc_insertion_point(module_scope)
| 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmelCase ) , '''Tatoeba directory does not exist.''' )
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Optional[int] ):
self.resolver.convert_models(['heb-eng'] )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A , _A = self.resolver.write_model_card('opus-mt-he-en' , dry_run=_UpperCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 315 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
a = logging.get_logger(__name__)
def _snake_case ( _snake_case : Dict , _snake_case : int ) -> List[str]:
'''simple docstring'''
_A = nn.functional.normalize(_snake_case )
_A = nn.functional.normalize(_snake_case )
return torch.mm(_snake_case , normalized_text_embeds.t() )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Dict = CLIPConfig
UpperCAmelCase : str = ['''CLIPEncoderLayer''']
def __init__( self : Optional[int] , _UpperCAmelCase : CLIPConfig ):
super().__init__(_UpperCAmelCase )
_A = CLIPVisionModel(config.vision_config )
_A = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=_UpperCAmelCase )
_A = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=_UpperCAmelCase )
_A = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=_UpperCAmelCase )
_A = nn.Parameter(torch.ones(17 ) , requires_grad=_UpperCAmelCase )
_A = nn.Parameter(torch.ones(3 ) , requires_grad=_UpperCAmelCase )
@torch.no_grad()
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ):
_A = self.vision_model(_UpperCAmelCase )[1] # pooled_output
_A = self.visual_projection(_UpperCAmelCase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_A = cosine_distance(_UpperCAmelCase , self.special_care_embeds ).cpu().float().numpy()
_A = cosine_distance(_UpperCAmelCase , self.concept_embeds ).cpu().float().numpy()
_A = []
_A = image_embeds.shape[0]
for i in range(_UpperCAmelCase ):
_A = {'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
_A = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
_A = special_cos_dist[i][concept_idx]
_A = self.special_care_embeds_weights[concept_idx].item()
_A = 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]} )
_A = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
_A = cos_dist[i][concept_idx]
_A = self.concept_embeds_weights[concept_idx].item()
_A = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(_UpperCAmelCase )
result.append(_UpperCAmelCase )
_A = [len(res['bad_concepts'] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : torch.FloatTensor , _UpperCAmelCase : torch.FloatTensor ):
_A = self.vision_model(_UpperCAmelCase )[1] # pooled_output
_A = self.visual_projection(_UpperCAmelCase )
_A = cosine_distance(_UpperCAmelCase , self.special_care_embeds )
_A = cosine_distance(_UpperCAmelCase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
_A = 0.0
_A = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
_A = torch.any(special_scores > 0 , dim=1 )
_A = special_care * 0.01
_A = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
_A = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
_A = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
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 .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 315 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def _snake_case ( _snake_case : list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
_A = {'+', '-', '*', '/'}
_A = []
for token in postfix_notation:
if token in operations:
_A , _A = 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(_snake_case ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
_A = []
for position in positions:
_A , _A = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_snake_case )
return permissible_positions
def _snake_case ( _snake_case : list[list[int]] ) -> bool:
'''simple docstring'''
return not any(elem == 0 for row in board for elem in row )
def _snake_case ( _snake_case : list[list[int]] , _snake_case : tuple[int, int] , _snake_case : int ) -> bool:
'''simple docstring'''
if is_complete(_snake_case ):
return True
for position in get_valid_pos(_snake_case , len(_snake_case ) ):
_A , _A = position
if board[y][x] == 0:
_A = curr + 1
if open_knight_tour_helper(_snake_case , _snake_case , curr + 1 ):
return True
_A = 0
return False
def _snake_case ( _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
for i in range(_snake_case ):
for j in range(_snake_case ):
_A = 1
if open_knight_tour_helper(_snake_case , (i, j) , 1 ):
return board
_A = 0
_A = F'''Open Kight Tour cannot be performed on a board of size {n}'''
raise ValueError(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : str ) -> list[int]:
'''simple docstring'''
return [ord(_snake_case ) - 96 for elem in plain]
def _snake_case ( _snake_case : list[int] ) -> str:
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def _snake_case ( ) -> None:
'''simple docstring'''
_A = encode(input('-> ' ).strip().lower() )
print('Encoded: ' , _snake_case )
print('Decoded:' , decode(_snake_case ) )
if __name__ == "__main__":
main()
| 315 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , *_UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str=None , **_UpperCAmelCase : List[Any] ):
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
_A = eval_examples
_A = post_process_function
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : str = "eval" ):
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(_UpperCAmelCase )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
else:
_A = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_UpperCAmelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , _UpperCAmelCase )
return metrics
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str = "test" ):
_A = self.get_test_dataloader(_UpperCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions , 'predict' )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
_A = abs(_snake_case )
_A = 0
while n > 0:
res += n % 10
n //= 10
return res
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
_A = abs(_snake_case )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
return sum(int(_snake_case ) for c in str(abs(_snake_case ) ) )
def _snake_case ( ) -> None:
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_snake_case : Callable , _snake_case : int ) -> None:
_A = F'''{func.__name__}({value})'''
_A = timeit(F'''__main__.{call}''' , setup='import __main__' )
print(F'''{call:56} = {func(_snake_case )} -- {timing:.4f} seconds''' )
for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(_snake_case , _snake_case )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
a = '''1'''
a = '''0'''
a = '''1'''
a = ort.SessionOptions()
a = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
a = ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
a = ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
a = ort.RunOptions()
a = 128
a = 1
a = np.ones((batch, sequence), dtype=np.intaa)
a = np.ones((batch, sequence), dtype=np.intaa)
a = 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...''')
a = time.time()
a = 2_000
a = {}
for iter in range(max_iters):
a = 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))
| 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_A = Vector()
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(_UpperCAmelCase ) , '(0,0,0,0,0,1)' )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = Vector([1, 2, 3, 4] )
self.assertEqual(len(_UpperCAmelCase ) , 4 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2] )
_A = Vector([1, 2, 3, 4, 5] )
_A = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_A = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCAmelCase_ ( self : str ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
_A = Vector([2, -1, 4] ) # for test of dot product
_A = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , '(3.0,6.0,9.0)' )
self.assertEqual((a * b) , 0 )
def lowerCAmelCase_ ( self : Dict ):
self.assertEqual(str(zero_vector(10 ) ).count('0' ) , 10 )
def lowerCAmelCase_ ( self : Tuple ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , _UpperCAmelCase , _UpperCAmelCase ) ) , '(3,4,7)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 0, 0, 0, 0, 0] )
_A = x.copy()
self.assertEqual(str(_UpperCAmelCase ) , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(_UpperCAmelCase ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : str ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_A = Vector([1, 2, 3] )
self.assertEqual('(14,32,50)' , str(a * x ) )
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' , str(a * 2 ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' , str(a + b ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' , str(a - b ) )
def lowerCAmelCase_ ( self : int ):
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 315 | 1 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( _snake_case : str , _snake_case : list[str] ) -> bool:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ) or len(_snake_case ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(_snake_case , _snake_case ) or not all(
isinstance(_snake_case , _snake_case ) and len(_snake_case ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
_A = {}
_A = 'WORD_KEEPER'
for word in words:
_A = trie
for c in word:
if c not in trie_node:
_A = {}
_A = trie_node[c]
_A = True
_A = len(_snake_case )
# Dynamic programming method
@functools.cache
def is_breakable(_snake_case : int ) -> bool:
if index == len_string:
return True
_A = trie
for i in range(_snake_case , _snake_case ):
_A = trie_node.get(string[i] , _snake_case )
if trie_node is None:
return False
if trie_node.get(_snake_case , _snake_case ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''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 lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = '''xlnet'''
UpperCAmelCase : List[Any] = ['''mems''']
UpperCAmelCase : Any = {
'''n_token''': '''vocab_size''', # Backward compatibility
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Union[str, Any] , _UpperCAmelCase : Dict=32_000 , _UpperCAmelCase : List[str]=1_024 , _UpperCAmelCase : Any=24 , _UpperCAmelCase : Union[str, Any]=16 , _UpperCAmelCase : Union[str, Any]=4_096 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Any=True , _UpperCAmelCase : str="bi" , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Optional[Any]=1E-1_2 , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : Any=512 , _UpperCAmelCase : Dict=None , _UpperCAmelCase : int=True , _UpperCAmelCase : int=False , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : int=-1 , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : Union[str, Any]="last" , _UpperCAmelCase : int=True , _UpperCAmelCase : str="tanh" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Dict=5 , _UpperCAmelCase : Optional[Any]=5 , _UpperCAmelCase : Union[str, Any]=5 , _UpperCAmelCase : List[str]=1 , _UpperCAmelCase : Dict=2 , **_UpperCAmelCase : int , ):
_A = vocab_size
_A = d_model
_A = n_layer
_A = 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})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = 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.' , _UpperCAmelCase , )
_A = kwargs['use_cache']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : Tuple ):
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 : Optional[Any] , _UpperCAmelCase : Optional[Any] ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 315 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = ['''ConvNextFeatureExtractor''']
a = ['''ConvNextImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvNextForImageClassification''',
'''ConvNextModel''',
'''ConvNextPreTrainedModel''',
'''ConvNextBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'''TFConvNextForImageClassification''',
'''TFConvNextModel''',
'''TFConvNextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
a = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 315 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
a = {
'''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
'''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def _snake_case ( _snake_case : Tuple ) -> Dict:
'''simple docstring'''
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def _snake_case ( _snake_case : str , _snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
if args.student_type == "roberta":
_A = False
elif args.student_type == "gpt2":
_A = False
def _snake_case ( _snake_case : str , _snake_case : int ) -> Tuple:
'''simple docstring'''
if args.student_type == "roberta":
_A = False
def _snake_case ( ) -> Tuple:
'''simple docstring'''
_A = argparse.ArgumentParser(description='Training' )
parser.add_argument('--force' , action='store_true' , help='Overwrite dump_path if it already exists.' )
parser.add_argument(
'--dump_path' , type=_snake_case , required=_snake_case , help='The output directory (log, checkpoints, parameters, etc.)' )
parser.add_argument(
'--data_file' , type=_snake_case , required=_snake_case , help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.' , )
parser.add_argument(
'--student_type' , type=_snake_case , choices=['distilbert', 'roberta', 'gpt2'] , required=_snake_case , help='The student type (DistilBERT, RoBERTa).' , )
parser.add_argument('--student_config' , type=_snake_case , required=_snake_case , help='Path to the student configuration.' )
parser.add_argument(
'--student_pretrained_weights' , default=_snake_case , type=_snake_case , help='Load student initialization checkpoint.' )
parser.add_argument(
'--teacher_type' , choices=['bert', 'roberta', 'gpt2'] , required=_snake_case , help='Teacher type (BERT, RoBERTa).' )
parser.add_argument('--teacher_name' , type=_snake_case , required=_snake_case , help='The teacher model.' )
parser.add_argument('--temperature' , default=2.0 , type=_snake_case , help='Temperature for the softmax temperature.' )
parser.add_argument(
'--alpha_ce' , default=0.5 , type=_snake_case , help='Linear weight for the distillation loss. Must be >=0.' )
parser.add_argument(
'--alpha_mlm' , default=0.0 , type=_snake_case , help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.' , )
parser.add_argument('--alpha_clm' , default=0.5 , type=_snake_case , help='Linear weight for the CLM loss. Must be >=0.' )
parser.add_argument('--alpha_mse' , default=0.0 , type=_snake_case , help='Linear weight of the MSE loss. Must be >=0.' )
parser.add_argument(
'--alpha_cos' , default=0.0 , type=_snake_case , help='Linear weight of the cosine embedding loss. Must be >=0.' )
parser.add_argument(
'--mlm' , action='store_true' , help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.' )
parser.add_argument(
'--mlm_mask_prop' , default=0.15 , type=_snake_case , help='Proportion of tokens for which we need to make a prediction.' , )
parser.add_argument('--word_mask' , default=0.8 , type=_snake_case , help='Proportion of tokens to mask out.' )
parser.add_argument('--word_keep' , default=0.1 , type=_snake_case , help='Proportion of tokens to keep.' )
parser.add_argument('--word_rand' , default=0.1 , type=_snake_case , help='Proportion of tokens to randomly replace.' )
parser.add_argument(
'--mlm_smoothing' , default=0.7 , type=_snake_case , help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).' , )
parser.add_argument('--token_counts' , type=_snake_case , help='The token counts in the data_file for MLM.' )
parser.add_argument(
'--restrict_ce_to_mask' , action='store_true' , help='If true, compute the distillation loss only the [MLM] prediction distribution.' , )
parser.add_argument(
'--freeze_pos_embs' , action='store_true' , help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.' , )
parser.add_argument(
'--freeze_token_type_embds' , action='store_true' , help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.' , )
parser.add_argument('--n_epoch' , type=_snake_case , default=3 , help='Number of pass on the whole dataset.' )
parser.add_argument('--batch_size' , type=_snake_case , default=5 , help='Batch size (for each process).' )
parser.add_argument(
'--group_by_size' , action='store_false' , help='If true, group sequences that have similar length into the same batch. Default is true.' , )
parser.add_argument(
'--gradient_accumulation_steps' , type=_snake_case , default=50 , help='Gradient accumulation for larger training batches.' , )
parser.add_argument('--warmup_prop' , default=0.05 , type=_snake_case , help='Linear warmup proportion.' )
parser.add_argument('--weight_decay' , default=0.0 , type=_snake_case , help='Weight decay if we apply some.' )
parser.add_argument('--learning_rate' , default=5E-4 , type=_snake_case , help='The initial learning rate for Adam.' )
parser.add_argument('--adam_epsilon' , default=1E-6 , type=_snake_case , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , default=5.0 , type=_snake_case , help='Max gradient norm.' )
parser.add_argument('--initializer_range' , default=0.02 , type=_snake_case , help='Random initialization range.' )
parser.add_argument(
'--fp16' , action='store_true' , help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit' , )
parser.add_argument(
'--fp16_opt_level' , type=_snake_case , default='O1' , help=(
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
'See details at https://nvidia.github.io/apex/amp.html'
) , )
parser.add_argument('--n_gpu' , type=_snake_case , default=1 , help='Number of GPUs in the node.' )
parser.add_argument('--local_rank' , type=_snake_case , default=-1 , help='Distributed training - Local rank' )
parser.add_argument('--seed' , type=_snake_case , default=56 , help='Random seed' )
parser.add_argument('--log_interval' , type=_snake_case , default=5_00 , help='Tensorboard logging interval.' )
parser.add_argument('--checkpoint_interval' , type=_snake_case , default=40_00 , help='Checkpoint interval.' )
_A = parser.parse_args()
sanity_checks(_snake_case )
# ARGS #
init_gpu_params(_snake_case )
set_seed(_snake_case )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
' itUse `--force` if you want to overwrite it' )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(F'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(F'''Param: {args}''' )
with open(os.path.join(args.dump_path , 'parameters.json' ) , 'w' ) as f:
json.dump(vars(_snake_case ) , _snake_case , indent=4 )
git_log(args.dump_path )
_A , _A , _A = MODEL_CLASSES[args.student_type]
_A , _A , _A = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
_A = teacher_tokenizer_class.from_pretrained(args.teacher_name )
_A = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
_A = tokenizer.all_special_tokens.index(_snake_case )
_A = tokenizer.all_special_ids[idx]
logger.info(F'''Special tokens {special_tok_ids}''' )
_A = special_tok_ids
_A = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(F'''Loading data from {args.data_file}''' )
with open(args.data_file , 'rb' ) as fp:
_A = pickle.load(_snake_case )
if args.mlm:
logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , 'rb' ) as fp:
_A = pickle.load(_snake_case )
_A = np.maximum(_snake_case , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
_A = 0.0 # do not predict special tokens
_A = torch.from_numpy(_snake_case )
else:
_A = None
_A = LmSeqsDataset(params=_snake_case , data=_snake_case )
logger.info('Data loader created.' )
# STUDENT #
logger.info(F'''Loading student config from {args.student_config}''' )
_A = student_config_class.from_pretrained(args.student_config )
_A = True
if args.student_pretrained_weights is not None:
logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
_A = student_model_class.from_pretrained(args.student_pretrained_weights , config=_snake_case )
else:
_A = student_model_class(_snake_case )
if args.n_gpu > 0:
student.to(F'''cuda:{args.local_rank}''' )
logger.info('Student loaded.' )
# TEACHER #
_A = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=_snake_case )
if args.n_gpu > 0:
teacher.to(F'''cuda:{args.local_rank}''' )
logger.info(F'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(_snake_case , _snake_case )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(_snake_case , _snake_case )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
_A = Distiller(
params=_snake_case , dataset=_snake_case , token_probs=_snake_case , student=_snake_case , teacher=_snake_case )
distiller.train()
logger.info('Let\'s go get some drinks.' )
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_A = Rectangle(height=0.25 , width=0.25 )
_A = [mem.copy() for i in range(6 )]
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('CPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(4 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('GPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Model' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(_UpperCAmelCase )
_A = []
_A = []
for i, rect in enumerate(_UpperCAmelCase ):
_A = fill.copy().set_fill(_UpperCAmelCase , opacity=0.8 )
target.move_to(_UpperCAmelCase )
model_arr.append(_UpperCAmelCase )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_UpperCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(_UpperCAmelCase )
self.add(*_UpperCAmelCase , *_UpperCAmelCase )
_A = [meta_mem.copy() for i in range(6 )]
_A = [meta_mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Disk' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_A = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_UpperCAmelCase )
_A = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase ) )
_A = Square(0.3 )
input.set_fill(_UpperCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , _UpperCAmelCase , buff=0.5 )
self.play(Write(_UpperCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=_UpperCAmelCase , buff=0.02 )
self.play(MoveToTarget(_UpperCAmelCase ) )
self.play(FadeOut(_UpperCAmelCase ) )
_A = Arrow(start=_UpperCAmelCase , end=_UpperCAmelCase , color=_UpperCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , _UpperCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
_A = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) )
_A = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(_UpperCAmelCase ) , Circumscribe(model_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
_A = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , _UpperCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
_A = AnimationGroup(
FadeOut(_UpperCAmelCase , run_time=0.5 ) , MoveToTarget(_UpperCAmelCase , run_time=0.5 ) , FadeIn(_UpperCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(_UpperCAmelCase )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
_A = 0.7
self.play(
Circumscribe(model_arr[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_arr[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
_A = a_c
_A = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(_UpperCAmelCase ) , FadeOut(_UpperCAmelCase , run_time=0.5 ) , )
_A = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) , MoveToTarget(_UpperCAmelCase ) )
self.wait()
| 315 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Any ):
_A = tempfile.mkdtemp()
# fmt: off
_A = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
_A = 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] ) )
_A = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
_A = os.path.join(self.tmpdirname , _UpperCAmelCase )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] , **_UpperCAmelCase : List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] , **_UpperCAmelCase : Tuple ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Tuple ):
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase_ ( self : List[str] ):
_A = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.get_tokenizer()
_A = self.get_image_processor()
_A = VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
_A = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
_A = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
_A = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
_A = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
_A = self.prepare_image_inputs()
_A = image_processor(_UpperCAmelCase , return_tensors='np' )
_A = processor(images=_UpperCAmelCase , 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 : str ):
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
_A = 'lower newer'
_A = processor(text=_UpperCAmelCase )
_A = tokenizer(_UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase_ ( self : Tuple ):
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
_A = 'lower newer'
_A = self.prepare_image_inputs()
_A = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(_UpperCAmelCase ):
processor()
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(_UpperCAmelCase )
_A = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
_A = 'lower newer'
_A = self.prepare_image_inputs()
_A = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
_A , _A = list_data[i + 1], list_data[i]
_A = True
return list_data if not swapped else bubble_sort(_snake_case , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import os
def _snake_case ( _snake_case : str = "input.txt" ) -> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
_A = [
[int(_snake_case ) for element in line.split(',' )]
for line in input_file.readlines()
]
_A = len(_snake_case )
_A = len(matrix[0] )
_A = [[-1 for _ in range(_snake_case )] for _ in range(_snake_case )]
for i in range(_snake_case ):
_A = matrix[i][0]
for j in range(1 , _snake_case ):
for i in range(_snake_case ):
_A = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , _snake_case ):
_A = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
_A = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Any = ['''input_values''', '''attention_mask''']
def __init__( self : Dict , _UpperCAmelCase : int = 1 , _UpperCAmelCase : int = 16_000 , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 80 , _UpperCAmelCase : int = 16 , _UpperCAmelCase : int = 64 , _UpperCAmelCase : str = "hann_window" , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 80 , _UpperCAmelCase : float = 7_600 , _UpperCAmelCase : float = 1E-1_0 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : bool = True , **_UpperCAmelCase : List[Any] , ):
super().__init__(feature_size=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , padding_value=_UpperCAmelCase , **_UpperCAmelCase )
_A = do_normalize
_A = return_attention_mask
_A = num_mel_bins
_A = hop_length
_A = win_length
_A = win_function
_A = frame_signal_scale
_A = fmin
_A = fmax
_A = mel_floor
_A = reduction_factor
_A = win_length * sampling_rate // 1_000
_A = hop_length * sampling_rate // 1_000
_A = optimal_fft_length(self.sample_size )
_A = (self.n_fft // 2) + 1
_A = window_function(window_length=self.sample_size , name=self.win_function , periodic=_UpperCAmelCase )
_A = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm='slaney' , mel_scale='slaney' , )
if frame_signal_scale != 1.0:
warnings.warn(
'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , )
if reduction_factor != 2.0:
warnings.warn(
'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def lowerCAmelCase_ ( _UpperCAmelCase : List[np.ndarray] , _UpperCAmelCase : List[np.ndarray] , _UpperCAmelCase : float = 0.0 ):
if attention_mask is not None:
_A = np.array(_UpperCAmelCase , np.intaa )
_A = []
for vector, length in zip(_UpperCAmelCase , attention_mask.sum(-1 ) ):
_A = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
_A = padding_value
normed_input_values.append(_UpperCAmelCase )
else:
_A = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : np.ndarray , ):
_A = spectrogram(
_UpperCAmelCase , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel='log10' , )
return log_mel_spec.T
def __call__( self : int , _UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : Optional[int] = None , **_UpperCAmelCase : Optional[int] , ):
if audio is None and audio_target is None:
raise ValueError('You must provide either `audio` or `audio_target` values.' )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
if audio is not None:
_A = self._process_audio(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , )
else:
_A = None
if audio_target is not None:
_A = self._process_audio(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , )
if inputs is None:
return inputs_target
else:
_A = inputs_target['input_values']
_A = inputs_target.get('attention_mask' )
if decoder_attention_mask is not None:
_A = decoder_attention_mask
return inputs
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _UpperCAmelCase : bool = False , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , **_UpperCAmelCase : List[Any] , ):
_A = isinstance(_UpperCAmelCase , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
_A = is_batched_numpy or (
isinstance(_UpperCAmelCase , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_A = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(_UpperCAmelCase , np.ndarray ):
_A = np.asarray(_UpperCAmelCase , dtype=np.floataa )
elif isinstance(_UpperCAmelCase , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
_A = speech.astype(np.floataa )
# always return batch
if not is_batched:
_A = [speech]
# needed to make pad() work on spectrogram inputs
_A = self.feature_size
# convert into correct format for padding
if is_target:
_A = [self._extract_mel_features(_UpperCAmelCase ) for waveform in speech]
_A = BatchFeature({'input_values': features} )
_A = self.num_mel_bins
else:
_A = BatchFeature({'input_values': speech} )
_A = self.pad(
_UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , **_UpperCAmelCase , )
_A = feature_size_hack
# convert input values to correct format
_A = padded_inputs['input_values']
if not isinstance(input_values[0] , np.ndarray ):
_A = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(_UpperCAmelCase , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
_A = [array.astype(np.floataa ) for array in input_values]
elif isinstance(_UpperCAmelCase , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
_A = input_values.astype(np.floataa )
# convert attention_mask to correct format
_A = padded_inputs.get('attention_mask' )
if attention_mask is not None:
_A = [np.asarray(_UpperCAmelCase , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
_A = (
attention_mask
if self._get_padding_strategies(_UpperCAmelCase , max_length=_UpperCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
else None
)
_A = self.zero_mean_unit_var_norm(
padded_inputs['input_values'] , attention_mask=_UpperCAmelCase , padding_value=self.padding_value )
if return_tensors is not None:
_A = padded_inputs.convert_to_tensors(_UpperCAmelCase )
return padded_inputs
def lowerCAmelCase_ ( self : Any ):
_A = super().to_dict()
# Don't serialize these as they are derived from the other properties.
_A = ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
for name in names:
if name in output:
del output[name]
return output
| 315 | 1 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( _snake_case : str = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(_snake_case ):
_A = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(_snake_case )[1] in (".py", ".ipynb"):
yield os.path.join(_snake_case , _snake_case ).lstrip('./' )
def _snake_case ( _snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
return F'''{i * " "}*''' if i else "\n##"
def _snake_case ( _snake_case : str , _snake_case : str ) -> str:
'''simple docstring'''
_A = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(_snake_case ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(_snake_case )} {new_part.replace("_" , " " ).title()}''' )
return new_path
def _snake_case ( _snake_case : str = "." ) -> None:
'''simple docstring'''
_A = ''
for filepath in sorted(good_file_paths(_snake_case ) ):
_A , _A = os.path.split(_snake_case )
if filepath != old_path:
_A = print_path(_snake_case , _snake_case )
_A = (filepath.count(os.sep ) + 1) if filepath else 0
_A = F'''{filepath}/{filename}'''.replace(' ' , '%20' )
_A = os.path.splitext(filename.replace('_' , ' ' ).title() )[0]
print(F'''{md_prefix(_snake_case )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md('''.''')
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = []
create_all_state(1 , _snake_case , _snake_case , [] , _snake_case )
return result
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : list[int] , _snake_case : list[list[int]] , ) -> None:
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(_snake_case , total_number - level + 2 ):
current_list.append(_snake_case )
create_all_state(i + 1 , _snake_case , level - 1 , _snake_case , _snake_case )
current_list.pop()
def _snake_case ( _snake_case : list[list[int]] ) -> None:
'''simple docstring'''
for i in total_list:
print(*_snake_case )
if __name__ == "__main__":
a = 4
a = 2
a = generate_all_combinations(n, k)
print_all_state(total_list)
| 315 | 1 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def _snake_case ( _snake_case : Any ) -> int:
'''simple docstring'''
_A , _A = image.size
_A , _A = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_A = image.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] )
_A = np.array(_snake_case ).astype(np.floataa ) / 255.0
_A = image[None].transpose(0 , 3 , 1 , 2 )
_A = torch.from_numpy(_snake_case )
return 2.0 * image - 1.0
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCAmelCase : VQModel , _UpperCAmelCase : UNetaDModel , _UpperCAmelCase : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=_UpperCAmelCase , unet=_UpperCAmelCase , scheduler=_UpperCAmelCase )
@torch.no_grad()
def __call__( self : str , _UpperCAmelCase : Union[torch.Tensor, PIL.Image.Image] = None , _UpperCAmelCase : Optional[int] = 1 , _UpperCAmelCase : Optional[int] = 100 , _UpperCAmelCase : Optional[float] = 0.0 , _UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _UpperCAmelCase : Optional[str] = "pil" , _UpperCAmelCase : bool = True , ):
if isinstance(_UpperCAmelCase , PIL.Image.Image ):
_A = 1
elif isinstance(_UpperCAmelCase , torch.Tensor ):
_A = image.shape[0]
else:
raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_UpperCAmelCase )}''' )
if isinstance(_UpperCAmelCase , PIL.Image.Image ):
_A = preprocess(_UpperCAmelCase )
_A , _A = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_A = (batch_size, self.unet.config.in_channels // 2, height, width)
_A = next(self.unet.parameters() ).dtype
_A = randn_tensor(_UpperCAmelCase , generator=_UpperCAmelCase , device=self.device , dtype=_UpperCAmelCase )
_A = image.to(device=self.device , dtype=_UpperCAmelCase )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(_UpperCAmelCase , device=self.device )
_A = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_A = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_A = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_A = {}
if accepts_eta:
_A = eta
for t in self.progress_bar(_UpperCAmelCase ):
# concat latents and low resolution image in the channel dimension.
_A = torch.cat([latents, image] , dim=1 )
_A = self.scheduler.scale_model_input(_UpperCAmelCase , _UpperCAmelCase )
# predict the noise residual
_A = self.unet(_UpperCAmelCase , _UpperCAmelCase ).sample
# compute the previous noisy sample x_t -> x_t-1
_A = self.scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
# decode the image latents with the VQVAE
_A = self.vqvae.decode(_UpperCAmelCase ).sample
_A = torch.clamp(_UpperCAmelCase , -1.0 , 1.0 )
_A = image / 2 + 0.5
_A = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_A = self.numpy_to_pil(_UpperCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_UpperCAmelCase )
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 315 | 1 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _snake_case ( _snake_case : Callable , _snake_case : float , _snake_case : float , _snake_case : float , _snake_case : float ) -> np.array:
'''simple docstring'''
_A = int(np.ceil((x_end - xa) / step_size ) )
_A = np.zeros((n + 1,) )
_A = ya
_A = xa
for k in range(_snake_case ):
_A = y[k] + step_size * ode_func(_snake_case , y[k] )
_A = y[k] + (
(step_size / 2) * (ode_func(_snake_case , y[k] ) + ode_func(x + step_size , _snake_case ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[str] ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple=True ):
_A = ()
for resnet, attn in zip(self.resnets , self.attentions ):
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[Any] ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[str]=True ):
_A = ()
for resnet in self.resnets:
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any]=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : int , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int]=True ):
for resnet in self.resnets:
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Dict ):
# there is always at least one resnet
_A = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_A = []
for _ in range(self.num_layers ):
_A = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
_A = attentions
def __call__( self : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int]=True ):
_A = self.resnets[0](_UpperCAmelCase , _UpperCAmelCase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
return hidden_states
| 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : float , _snake_case : float , _snake_case : float , _snake_case : float , ) -> float:
'''simple docstring'''
_A = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('All input parameters must be positive' )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError('Relative densities cannot be greater than one' )
else:
_A = 1 - (matter_density + radiation_density + dark_energy)
_A = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
_A = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
a = 0.3
print(
hubble_parameter(
hubble_constant=6_8.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
_A = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
_A = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
_A = numpy.random.rand(3 , 1 )
# Real output values provided.
_A = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
_A = numpy.zeros(output_array.shape )
def lowerCAmelCase_ ( self : List[str] ):
_A = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def lowerCAmelCase_ ( self : Optional[int] ):
_A = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
_A = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
_A = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : int , _UpperCAmelCase : bool ):
for iteration in range(1 , iterations + 1 ):
_A = self.feedforward()
self.back_propagation()
if give_loss:
_A = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F'''Iteration {iteration} Loss: {loss}''' )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : numpy.ndarray ):
_A = input_arr
_A = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return 1 / (1 + numpy.exp(-value ))
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return (value) * (1 - (value))
def _snake_case ( ) -> int:
'''simple docstring'''
_A = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
_A = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
_A = TwoHiddenLayerNeuralNetwork(
input_array=_snake_case , output_array=_snake_case )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_snake_case , iterations=10 , give_loss=_snake_case )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 315 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = '''megatron-bert'''
def __init__( self : Any , _UpperCAmelCase : int=29_056 , _UpperCAmelCase : int=1_024 , _UpperCAmelCase : List[Any]=24 , _UpperCAmelCase : Tuple=16 , _UpperCAmelCase : str=4_096 , _UpperCAmelCase : Any="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : Any=512 , _UpperCAmelCase : Tuple=2 , _UpperCAmelCase : Any=0.02 , _UpperCAmelCase : Tuple=1E-1_2 , _UpperCAmelCase : str=0 , _UpperCAmelCase : str="absolute" , _UpperCAmelCase : Any=True , **_UpperCAmelCase : List[str] , ):
super().__init__(pad_token_id=_UpperCAmelCase , **_UpperCAmelCase )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
| 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : T ):
_A = data
_A = None
def __str__( self : str ):
return F'''{self.data}'''
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple ):
_A = None
def __iter__( self : List[Any] ):
_A = self.top
while node:
yield node.data
_A = node.next
def __str__( self : Union[str, Any] ):
return "->".join([str(_UpperCAmelCase ) for item in self] )
def __len__( self : List[Any] ):
return len(tuple(iter(self ) ) )
def lowerCAmelCase_ ( self : str ):
return self.top is None
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : T ):
_A = Node(_UpperCAmelCase )
if not self.is_empty():
_A = self.top
_A = node
def lowerCAmelCase_ ( self : Dict ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _UpperCAmelCase )
_A = self.top
_A = self.top.next
return pop_node.data
def lowerCAmelCase_ ( self : Tuple ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 315 | 1 |
"""simple docstring"""
from collections import defaultdict
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
_A = 1
_A = True
for v in tree[start]:
if v not in visited:
ret += dfs(_snake_case )
if ret % 2 == 0:
cuts.append(_snake_case )
return ret
def _snake_case ( ) -> str:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
a , a = 10, 9
a = defaultdict(list)
a = {}
a = []
a = 0
a = [(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)
| 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Union[str, Any] ):
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Dict = ProphetNetTokenizer
UpperCAmelCase : Optional[Any] = False
def lowerCAmelCase_ ( self : Optional[int] ):
super().setUp()
_A = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
_A = 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 lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Any ):
_A = 'UNwant\u00E9d,running'
_A = 'unwanted, running'
return input_text, output_text
def lowerCAmelCase_ ( self : int ):
_A = self.tokenizer_class(self.vocab_file )
_A = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(_UpperCAmelCase , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , [9, 6, 7, 12, 10, 11] )
def lowerCAmelCase_ ( self : str ):
_A = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def lowerCAmelCase_ ( self : List[str] ):
_A = BasicTokenizer(do_lower_case=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def lowerCAmelCase_ ( self : Dict ):
_A = BasicTokenizer(do_lower_case=_UpperCAmelCase , strip_accents=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def lowerCAmelCase_ ( self : int ):
_A = BasicTokenizer(do_lower_case=_UpperCAmelCase , strip_accents=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def lowerCAmelCase_ ( self : Tuple ):
_A = BasicTokenizer(do_lower_case=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def lowerCAmelCase_ ( self : Dict ):
_A = BasicTokenizer(do_lower_case=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = BasicTokenizer(do_lower_case=_UpperCAmelCase , strip_accents=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def lowerCAmelCase_ ( self : Dict ):
_A = BasicTokenizer(do_lower_case=_UpperCAmelCase , strip_accents=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def lowerCAmelCase_ ( self : Dict ):
_A = BasicTokenizer(do_lower_case=_UpperCAmelCase , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def lowerCAmelCase_ ( self : str ):
_A = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
_A = {}
for i, token in enumerate(_UpperCAmelCase ):
_A = i
_A = WordpieceTokenizer(vocab=_UpperCAmelCase , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
@require_torch
def lowerCAmelCase_ ( self : str ):
_A = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
_A = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_A = [1_037, 2_146, 20_423, 2_005, 7_680, 7_849, 3_989, 1_012, 102]
_A = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='pt' )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
_A = list(batch.input_ids.numpy()[0] )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def lowerCAmelCase_ ( self : Any ):
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def lowerCAmelCase_ ( self : List[Any] ):
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def lowerCAmelCase_ ( self : Any ):
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
@slow
def lowerCAmelCase_ ( self : Dict ):
_A = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
_A = tokenizer.encode('sequence builders' , add_special_tokens=_UpperCAmelCase )
_A = tokenizer.encode('multi-sequence build' , add_special_tokens=_UpperCAmelCase )
_A = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase )
_A = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase , _UpperCAmelCase )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def _snake_case ( _snake_case : str ) -> list[str]:
'''simple docstring'''
return word_by_signature[signature(_snake_case )]
a = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
a = sorted({word.strip().lower() for word in data.splitlines()})
a = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
a = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
_A = set()
# Replace all the whitespace in our sentence
_A = input_str.replace(' ' , '' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(_snake_case ) == 26
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
_A = [False] * 26
for char in input_str:
if char.islower():
_A = True
elif char.isupper():
_A = True
return all(_snake_case )
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def _snake_case ( ) -> None:
'''simple docstring'''
from timeit import timeit
_A = 'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
print(timeit('is_pangram()' , setup=_snake_case ) )
print(timeit('is_pangram_faster()' , setup=_snake_case ) )
print(timeit('is_pangram_fastest()' , setup=_snake_case ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The column name of the images in the files.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the training data.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the validation data.'''} )
UpperCAmelCase : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def lowerCAmelCase_ ( self : Dict ):
_A = {}
if self.train_dir is not None:
_A = self.train_dir
if self.validation_dir is not None:
_A = self.validation_dir
_A = data_files if data_files else None
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : str = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
UpperCAmelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase : str = field(default=__lowerCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCAmelCase : float = field(
default=0.75 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : float = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def _snake_case ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
_A = torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _snake_case ( ) -> List[str]:
'''simple docstring'''
_A = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A , _A , _A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , _snake_case , _snake_case )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_A = training_args.get_process_log_level()
logger.setLevel(_snake_case )
transformers.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
_A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_A = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _snake_case ) and data_args.train_val_split > 0.0:
_A = ds['train'].train_test_split(data_args.train_val_split )
_A = split['train']
_A = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_A = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
_A = ViTMAEConfig.from_pretrained(model_args.config_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_A = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_A = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
_A = ViTMAEForPreTraining(_snake_case )
if training_args.do_train:
_A = ds['train'].column_names
else:
_A = ds['validation'].column_names
if data_args.image_column_name is not None:
_A = data_args.image_column_name
elif "image" in column_names:
_A = 'image'
elif "img" in column_names:
_A = 'img'
else:
_A = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_A = image_processor.size['shortest_edge']
else:
_A = (image_processor.size['height'], image_processor.size['width'])
_A = Compose(
[
Lambda(lambda _snake_case : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_snake_case , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_snake_case : List[Any] ):
_A = [transforms(_snake_case ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
_A = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_snake_case )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
_A = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_snake_case )
# Compute absolute learning rate
_A = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_A = training_args.base_learning_rate * total_train_batch_size / 2_56
# Initialize our trainer
_A = Trainer(
model=_snake_case , args=_snake_case , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_snake_case , data_collator=_snake_case , )
# Training
if training_args.do_train:
_A = None
if training_args.resume_from_checkpoint is not None:
_A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A = last_checkpoint
_A = trainer.train(resume_from_checkpoint=_snake_case )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_A = trainer.evaluate()
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
# Write model card and (optionally) push to hub
_A = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def _snake_case ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
a = HfArgumentParser(InitializationArguments)
a = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
a = AutoTokenizer.from_pretrained(args.tokenizer_name)
# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
a = {
'''vocab_size''': len(tokenizer),
'''scale_attn_by_inverse_layer_idx''': True,
'''reorder_and_upcast_attn''': True,
}
# Load model config (GPT-2 large in this case)
a = AutoConfig.from_pretrained(args.config_name, **config_kwargs)
# Initialize new model with config
a = AutoModelForCausalLM.from_config(config)
# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
| 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer names
a = 70 # max width of quantizer names
def _snake_case ( _snake_case : int ) -> List[Any]:
'''simple docstring'''
_A = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=_snake_case , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=_snake_case , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=_snake_case , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=_snake_case , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=_snake_case , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=_snake_case , type=_snake_case , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=_snake_case , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def _snake_case ( _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
if args.calibrator == "max":
_A = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
_A = 'histogram'
elif args.calibrator == "mse":
_A = 'histogram'
else:
raise ValueError(F'''Invalid calibrator {args.calibrator}''' )
_A = QuantDescriptor(num_bits=args.aprec , calib_method=_snake_case )
_A = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(_snake_case )
quant_nn.QuantLinear.set_default_quant_desc_weight(_snake_case )
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Any=False , _snake_case : Union[str, Any]=False ) -> Optional[int]:
'''simple docstring'''
logger.info('Configuring Model for Quantization' )
logger.info(F'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_snake_case , ['embeddings'] , which='weight' , _disabled=_snake_case )
if args.quant_disable:
set_quantizer_by_name(_snake_case , [''] , _disabled=_snake_case )
if args.quant_disable_keyword:
set_quantizer_by_name(_snake_case , args.quant_disable_keyword , _disabled=_snake_case )
if args.quant_disable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_disable_layer_module] , _disabled=_snake_case )
if args.quant_enable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_enable_layer_module] , _disabled=_snake_case )
if args.recalibrate_weights:
recalibrate_weights(_snake_case )
if args.fuse_qkv:
fuse_qkv(_snake_case , _snake_case )
if args.clip_gelu:
clip_gelu(_snake_case , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str ) -> Any:
'''simple docstring'''
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F'''{name:80}: {module}''' )
def _snake_case ( _snake_case : List[Any] , _snake_case : List[Any] ) -> str:
'''simple docstring'''
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
def fusea(_snake_case : int , _snake_case : str , _snake_case : Optional[Any] ):
for mod in [qq, qk, qv]:
if not hasattr(_snake_case , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
_A = qq._amax.detach().item()
_A = qk._amax.detach().item()
_A = qv._amax.detach().item()
_A = max(_snake_case , _snake_case , _snake_case )
qq._amax.fill_(_snake_case )
qk._amax.fill_(_snake_case )
qv._amax.fill_(_snake_case )
logger.info(F''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(F'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def _snake_case ( _snake_case : int , _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
_A = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_snake_case )
_A = mod._input_quantizer._amax.data.detach().item()
logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def _snake_case ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
_A = mod.weight.shape[0]
_A = mod._weight_quantizer._amax.detach()
_A = torch.ones(_snake_case , dtype=amax.dtype , device=amax.device ) * amax
print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def _snake_case ( _snake_case : Dict ) -> Tuple:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
_A = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
_A = set(range(len(mod.weight.size() ) ) ) - axis_set
_A = pytorch_quantization.utils.reduce_amax(mod.weight , axis=_snake_case , keepdims=_snake_case ).detach()
logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
_A = amax
def _snake_case ( _snake_case : Tuple , _snake_case : List[str]=25 , _snake_case : str=1_80 , _snake_case : int=None ) -> List[Any]:
'''simple docstring'''
if ignore is None:
_A = []
elif not isinstance(_snake_case , _snake_case ):
_A = [ignore]
_A = 0
for name, mod in model.named_modules():
if not hasattr(_snake_case , 'weight' ):
continue
_A = max(_snake_case , len(_snake_case ) )
for name, mod in model.named_modules():
_A = getattr(_snake_case , '_input_quantizer' , _snake_case )
_A = getattr(_snake_case , '_weight_quantizer' , _snake_case )
if not hasattr(_snake_case , 'weight' ):
continue
if type(_snake_case ) in ignore:
continue
if [True for s in ignore if type(_snake_case ) is str and s in name]:
continue
_A = F'''Act:{input_q.extra_repr()}'''
_A = F'''Wgt:{weight_q.extra_repr()}'''
_A = F'''{name:{name_width}} {act_str} {wgt_str}'''
if len(_snake_case ) <= line_width:
logger.info(_snake_case )
else:
logger.info(F'''{name:{name_width}} {act_str}''' )
logger.info(F'''{" ":{name_width}} {wgt_str}''' )
def _snake_case ( _snake_case : Dict ) -> int:
'''simple docstring'''
_A = 0
for name, mod in model.named_modules():
if isinstance(_snake_case , pytorch_quantization.nn.TensorQuantizer ):
print(F'''{name:80} {mod}''' )
count += 1
print(F'''{count} TensorQuantizers found in model''' )
def _snake_case ( _snake_case : str , _snake_case : Dict , _snake_case : List[Any] , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
_A = getattr(_snake_case , _snake_case , _snake_case )
if quantizer_mod is not None:
assert hasattr(_snake_case , _snake_case )
setattr(_snake_case , _snake_case , _snake_case )
else:
logger.warning(F'''{name} has no {quantizer}''' )
def _snake_case ( _snake_case : Dict , _snake_case : Optional[int] , _snake_case : str="both" , **_snake_case : List[Any] ) -> str:
'''simple docstring'''
_A = F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(_snake_case , _snake_case , '_input_quantizer' , _snake_case , _snake_case )
if which in ["weight", "both"]:
set_quantizer(_snake_case , _snake_case , '_weight_quantizer' , _snake_case , _snake_case )
logger.info(_snake_case )
def _snake_case ( _snake_case : Any , _snake_case : int , **_snake_case : Dict ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_input_quantizer' ) or hasattr(_snake_case , '_weight_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
set_quantizers(_snake_case , _snake_case , **_snake_case )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
_A = F'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
setattr(_snake_case , _snake_case , _snake_case )
logger.info(_snake_case )
| 315 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class lowercase_ :
'''simple docstring'''
def __init__( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Any=13 , _UpperCAmelCase : Optional[int]=7 , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Any=33 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : Optional[int]=5 , _UpperCAmelCase : str=4 , _UpperCAmelCase : Optional[int]=37 , _UpperCAmelCase : Union[str, Any]="gelu" , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : int=512 , _UpperCAmelCase : Optional[int]=16 , _UpperCAmelCase : Optional[int]=2 , _UpperCAmelCase : Any=0.02 , _UpperCAmelCase : List[Any]=3 , _UpperCAmelCase : Tuple=4 , _UpperCAmelCase : Dict=None , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_input_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
def lowerCAmelCase_ ( self : int ):
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase_ ( self : Tuple ):
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , 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 , initializer_range=self.initializer_range , )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] ):
_A = EsmModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )
_A = model(_UpperCAmelCase )
_A = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int ):
_A = EsmForMaskedLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Any ):
_A = self.num_labels
_A = EsmForTokenClassification(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase_ ( self : Any ):
_A = self.prepare_config_and_inputs()
(
(
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) ,
) = config_and_inputs
_A = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Optional[Any] = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCAmelCase : List[Any] = ()
UpperCAmelCase : Tuple = (
{
'''feature-extraction''': EsmModel,
'''fill-mask''': EsmForMaskedLM,
'''text-classification''': EsmForSequenceClassification,
'''token-classification''': EsmForTokenClassification,
'''zero-shot''': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase : Optional[Any] = True
def lowerCAmelCase_ ( self : str ):
_A = EsmModelTester(self )
_A = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def lowerCAmelCase_ ( self : Tuple ):
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : List[str] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_A = type
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : int ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : List[str] ):
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = EsmModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.model_tester.prepare_config_and_inputs()[0]
_A = EsmEmbeddings(config=_UpperCAmelCase )
_A = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
_A = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
_A = create_position_ids_from_input_ids(_UpperCAmelCase , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(_UpperCAmelCase , _UpperCAmelCase ) ) )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = self.model_tester.prepare_config_and_inputs()[0]
_A = EsmEmbeddings(config=_UpperCAmelCase )
_A = torch.empty(2 , 4 , 30 )
_A = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
_A = torch.as_tensor([expected_single_positions, expected_single_positions] )
_A = embeddings.create_position_ids_from_inputs_embeds(_UpperCAmelCase )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(_UpperCAmelCase , _UpperCAmelCase ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def lowerCAmelCase_ ( self : List[Any] ):
pass
@unittest.skip('Esm does not support embedding resizing' )
def lowerCAmelCase_ ( self : Tuple ):
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
pass
@require_torch
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
with torch.no_grad():
_A = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
_A = torch.tensor([[0, 1, 2, 3, 4, 5]] )
_A = model(_UpperCAmelCase )[0]
_A = 33
_A = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , _UpperCAmelCase )
_A = torch.tensor(
[[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCAmelCase_ ( self : Any ):
with torch.no_grad():
_A = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
_A = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
_A = model(_UpperCAmelCase )[0]
# compare the actual values for a slice.
_A = torch.tensor(
[[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
| 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'] , )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int]=False ):
_A = spearmanr(_UpperCAmelCase , _UpperCAmelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 315 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ) -> List[str]:
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(_snake_case ):
requests.request('GET' , 'https://huggingface.co' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('GET' , 'https://huggingface.co' , timeout=1.0 )
@pytest.mark.integration
def _snake_case ( ) -> Union[str, Any]:
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('GET' , 'https://huggingface.co' )
def _snake_case ( ) -> int:
'''simple docstring'''
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(_snake_case ):
http_head('https://huggingface.co' )
| 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A = b
if function(_snake_case ) == 0: # one of the a or b is a root for the function
return a
elif function(_snake_case ) == 0:
return b
elif (
function(_snake_case ) * function(_snake_case ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
_A = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(_snake_case ) == 0:
return mid
elif function(_snake_case ) * function(_snake_case ) < 0:
_A = mid
else:
_A = mid
_A = start + (end - start) / 2.0
return mid
def _snake_case ( _snake_case : float ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : int ) -> list:
'''simple docstring'''
_A = [True] * n
_A = False
_A = False
_A = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
_A = i * 2
while index < n:
_A = False
_A = index + i
_A = [2]
for i in range(3 , _snake_case , 2 ):
if is_prime[i]:
primes.append(_snake_case )
return primes
def _snake_case ( _snake_case : int = 99_99_66_66_33_33 ) -> int:
'''simple docstring'''
_A = math.floor(math.sqrt(_snake_case ) ) + 1_00
_A = prime_sieve(_snake_case )
_A = 0
_A = 0
_A = primes[prime_index]
while (last_prime**2) <= limit:
_A = primes[prime_index + 1]
_A = last_prime**2
_A = next_prime**2
# Get numbers divisible by lps(current)
_A = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
_A = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
_A = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
_A = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmelCase ) , '''Tatoeba directory does not exist.''' )
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Optional[int] ):
self.resolver.convert_models(['heb-eng'] )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A , _A = self.resolver.write_model_card('opus-mt-he-en' , dry_run=_UpperCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 315 | 1 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
a = {
'''/attention/''': '''/0/SelfAttention/''',
'''/self_attention/''': '''/0/SelfAttention/''',
'''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
'''value''': '''v''',
'''query''': '''q''',
'''key''': '''k''',
'''out''': '''o''',
'''pre_self_attention_layer_norm''': '''0/layer_norm''',
'''pre_cross_attention_layer_norm''': '''1/layer_norm''',
'''pre_attention_layer_norm''': '''0/layer_norm''', # previously 1, but seems wrong
'''token_embedder''': '''shared''',
'''encoder_norm''': '''final_layer_norm''',
'''decoder_norm''': '''final_layer_norm''',
'''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
'''router/router_weights/w/''': '''router/classifier/''',
'''roer/roer_weights/w/''': '''router/classifier/''',
'''logits_dense''': '''lm_head''',
}
def _snake_case ( _snake_case : Union[str, Any] ) -> List[str]:
'''simple docstring'''
_A = list(s_dict.keys() )
for key in keys:
_A = R'.*/layers_(\d+)'
_A = key
if re.match(_snake_case , _snake_case ):
_A = re.sub(R'layers_(\d+)' , R'block/\1/layer' , _snake_case )
_A = R'(encoder|decoder)\/'
if re.match(_snake_case , _snake_case ):
_A = re.match(_snake_case , _snake_case ).groups()
if groups[0] == "encoder":
_A = re.sub(R'/mlp/' , R'/1/mlp/' , _snake_case )
_A = re.sub(R'/pre_mlp_layer_norm/' , R'/1/layer_norm/' , _snake_case )
elif groups[0] == "decoder":
_A = re.sub(R'/mlp/' , R'/2/mlp/' , _snake_case )
_A = re.sub(R'/pre_mlp_layer_norm/' , R'/2/layer_norm/' , _snake_case )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_A = new_key.replace(_snake_case , _snake_case )
print(F'''{key} -> {new_key}''' )
_A = s_dict.pop(_snake_case )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_A = s_dict[
'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_A = s_dict[
'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
_A = s_dict[key].shape[0]
_A = s_dict[key]
for idx in range(_snake_case ):
_A = expert_weihts[idx]
print(F'''{key} -> {key.replace("expert/" , "nested fstring" )}''' )
s_dict.pop(_snake_case )
return s_dict
a = {
'''NUM_ENCODER_LAYERS''': '''num_layers''',
'''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
'''NUM_HEADS''': '''num_heads''',
'''HEAD_DIM''': '''d_kv''',
'''EMBED_DIM''': '''d_model''',
'''MLP_DIM''': '''d_ff''',
'''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
'''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
'''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
'''dense.MlpBlock.activations''': '''feed_forward_proj''',
}
def _snake_case ( _snake_case : List[Any] , _snake_case : Tuple ) -> Any:
'''simple docstring'''
import regex as re
with open(_snake_case , 'r' ) as f:
_A = f.read()
_A = re.findall(R'(.*) = ([0-9.]*)' , _snake_case )
_A = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_A = float(_snake_case ) if '.' in value else int(_snake_case )
_A = re.findall(R'(.*activations) = \(\'(.*)\',\)' , _snake_case )[0]
_A = str(activation[1] )
_A = num_experts
_A = SwitchTransformersConfig(**_snake_case )
return config
def _snake_case ( _snake_case : List[Any] , _snake_case : List[Any] , _snake_case : List[Any]=None , _snake_case : Optional[Any]="./" , _snake_case : List[str]=8 ) -> int:
'''simple docstring'''
print(F'''Loading flax weights from : {flax_checkpoint_path}''' )
_A = checkpoints.load_tax_checkpoint(_snake_case )
if gin_file is not None:
_A = convert_gin_to_config(_snake_case , _snake_case )
else:
_A = SwitchTransformersConfig.from_pretrained(_snake_case )
_A = SwitchTransformersForConditionalGeneration(_snake_case )
_A = flax_params['target']
_A = flatten_dict(_snake_case , sep='/' )
_A = rename_keys(_snake_case )
_A = unflatten_dict(_snake_case , sep='/' )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(_snake_case , _snake_case )
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
pt_model.save_pretrained(_snake_case )
if __name__ == "__main__":
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--switch_t5x_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
''' model architecture. If not provided, a `gin_file` has to be provided.'''
),
)
parser.add_argument(
'''--gin_file''',
default=None,
type=str,
required=False,
help='''Path to the gin config file. If not provided, a `config_file` has to be passed ''',
)
parser.add_argument(
'''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
)
parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
a = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
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 .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 315 | 1 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _snake_case ( _snake_case : dict ) -> tuple:
'''simple docstring'''
return (data["data"], data["target"])
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray , _snake_case : np.ndarray ) -> np.ndarray:
'''simple docstring'''
_A = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(_snake_case , _snake_case )
# Predict target for test data
_A = xgb.predict(_snake_case )
_A = predictions.reshape(len(_snake_case ) , 1 )
return predictions
def _snake_case ( ) -> None:
'''simple docstring'''
_A = fetch_california_housing()
_A , _A = data_handling(_snake_case )
_A , _A , _A , _A = train_test_split(
_snake_case , _snake_case , test_size=0.25 , random_state=1 )
_A = xgboost(_snake_case , _snake_case , _snake_case )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(_snake_case , _snake_case )}''' )
print(F'''Mean Square Error : {mean_squared_error(_snake_case , _snake_case )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
_A = []
for position in positions:
_A , _A = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_snake_case )
return permissible_positions
def _snake_case ( _snake_case : list[list[int]] ) -> bool:
'''simple docstring'''
return not any(elem == 0 for row in board for elem in row )
def _snake_case ( _snake_case : list[list[int]] , _snake_case : tuple[int, int] , _snake_case : int ) -> bool:
'''simple docstring'''
if is_complete(_snake_case ):
return True
for position in get_valid_pos(_snake_case , len(_snake_case ) ):
_A , _A = position
if board[y][x] == 0:
_A = curr + 1
if open_knight_tour_helper(_snake_case , _snake_case , curr + 1 ):
return True
_A = 0
return False
def _snake_case ( _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
for i in range(_snake_case ):
for j in range(_snake_case ):
_A = 1
if open_knight_tour_helper(_snake_case , (i, j) , 1 ):
return board
_A = 0
_A = F'''Open Kight Tour cannot be performed on a board of size {n}'''
raise ValueError(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = 0
UpperCAmelCase : bool = False
UpperCAmelCase : float = 3.0
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'a': 2} )
self.assertDictEqual(MockClass(a=2 , b=_UpperCAmelCase ).to_kwargs() , {'a': 2, 'b': True} )
self.assertDictEqual(MockClass(a=2 , c=2.25 ).to_kwargs() , {'a': 2, 'c': 2.25} )
@require_cuda
def lowerCAmelCase_ ( self : int ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
_A = GradScalerKwargs(init_scale=1_024 , growth_factor=2 )
AcceleratorState._reset_state()
_A = Accelerator(mixed_precision='fp16' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
_A = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 1024.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2_000 )
self.assertEqual(scaler._enabled , _UpperCAmelCase )
@require_multi_gpu
def lowerCAmelCase_ ( self : Tuple ):
_A = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
execute_subprocess_async(_UpperCAmelCase , env=os.environ.copy() )
if __name__ == "__main__":
a = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
a = Accelerator(kwargs_handlers=[ddp_scaler])
a = torch.nn.Linear(100, 200)
a = accelerator.prepare(model)
# Check the values changed in kwargs
a = ''''''
a = model.bucket_bytes_cap // (1_024 * 1_024)
if observed_bucket_cap_map != 15:
error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 315 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , *_UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str=None , **_UpperCAmelCase : List[Any] ):
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
_A = eval_examples
_A = post_process_function
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : str = "eval" ):
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(_UpperCAmelCase )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
else:
_A = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_UpperCAmelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , _UpperCAmelCase )
return metrics
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str = "test" ):
_A = self.get_test_dataloader(_UpperCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions , 'predict' )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , *_UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str=None , **_UpperCAmelCase : List[Any] ):
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
_A = eval_examples
_A = post_process_function
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : str = "eval" ):
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(_UpperCAmelCase )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
else:
_A = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_UpperCAmelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , _UpperCAmelCase )
return metrics
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str = "test" ):
_A = self.get_test_dataloader(_UpperCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions , 'predict' )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_UpperCAmelCase )
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def _snake_case ( _snake_case : Dict=None ) -> int:
'''simple docstring'''
if subparsers is not None:
_A = subparsers.add_parser('env' )
else:
_A = argparse.ArgumentParser('Accelerate env command' )
parser.add_argument(
'--config_file' , default=_snake_case , help='The config file to use for the default values in the launching script.' )
if subparsers is not None:
parser.set_defaults(func=_snake_case )
return parser
def _snake_case ( _snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
_A = torch.__version__
_A = torch.cuda.is_available()
_A = is_xpu_available()
_A = is_npu_available()
_A = 'Not found'
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_snake_case ):
_A = load_config_from_file(args.config_file ).to_dict()
_A = {
'`Accelerate` version': version,
'Platform': platform.platform(),
'Python version': platform.python_version(),
'Numpy version': np.__version__,
'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
'PyTorch XPU available': str(_snake_case ),
'PyTorch NPU available': str(_snake_case ),
'System RAM': F'''{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB''',
}
if pt_cuda_available:
_A = torch.cuda.get_device_name()
print('\nCopy-and-paste the text below in your GitHub issue\n' )
print('\n'.join([F'''- {prop}: {val}''' for prop, val in info.items()] ) )
print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:' )
_A = (
'\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(_snake_case , _snake_case )
else F'''\t{accelerate_config}'''
)
print(_snake_case )
_A = accelerate_config
return info
def _snake_case ( ) -> int:
'''simple docstring'''
_A = env_command_parser()
_A = parser.parse_args()
env_command(_snake_case )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_A = Vector()
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(_UpperCAmelCase ) , '(0,0,0,0,0,1)' )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = Vector([1, 2, 3, 4] )
self.assertEqual(len(_UpperCAmelCase ) , 4 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2] )
_A = Vector([1, 2, 3, 4, 5] )
_A = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_A = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCAmelCase_ ( self : str ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
_A = Vector([2, -1, 4] ) # for test of dot product
_A = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , '(3.0,6.0,9.0)' )
self.assertEqual((a * b) , 0 )
def lowerCAmelCase_ ( self : Dict ):
self.assertEqual(str(zero_vector(10 ) ).count('0' ) , 10 )
def lowerCAmelCase_ ( self : Tuple ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , _UpperCAmelCase , _UpperCAmelCase ) ) , '(3,4,7)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 0, 0, 0, 0, 0] )
_A = x.copy()
self.assertEqual(str(_UpperCAmelCase ) , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(_UpperCAmelCase ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : str ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_A = Vector([1, 2, 3] )
self.assertEqual('(14,32,50)' , str(a * x ) )
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' , str(a * 2 ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' , str(a + b ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' , str(a - b ) )
def lowerCAmelCase_ ( self : int ):
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 315 | 1 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
a = '''src/transformers'''
# Matches is_xxx_available()
a = re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
a = re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
a = re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
a = re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
a = re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
a = re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
a = re.compile(r'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
a = re.compile(r'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
a = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
a = re.compile(r'''^\s*try:''')
# Catches a line with else:
a = re.compile(r'''^\s*else:''')
def _snake_case ( _snake_case : Optional[int] ) -> Optional[int]:
'''simple docstring'''
if _re_test_backend.search(_snake_case ) is None:
return None
_A = [b[0] for b in _re_backend.findall(_snake_case )]
backends.sort()
return "_and_".join(_snake_case )
def _snake_case ( _snake_case : str ) -> Tuple:
'''simple docstring'''
with open(_snake_case , 'r' , encoding='utf-8' , newline='\n' ) as f:
_A = f.readlines()
_A = 0
while line_index < len(_snake_case ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(_snake_case ):
return None
# First grab the objects without a specific backend in _import_structure
_A = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
_A = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(_snake_case ):
_A = _re_one_line_import_struct.search(_snake_case ).groups()[0]
_A = re.findall(R'\[([^\]]+)\]' , _snake_case )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
_A = _re_import_struct_key_value.search(_snake_case )
if single_line_import_search is not None:
_A = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(_snake_case ) > 0]
objects.extend(_snake_case )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
_A = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
_A = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_A = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
_A = lines[line_index]
if _re_import_struct_add_one.search(_snake_case ) is not None:
objects.append(_re_import_struct_add_one.search(_snake_case ).groups()[0] )
elif _re_import_struct_add_many.search(_snake_case ) is not None:
_A = _re_import_struct_add_many.search(_snake_case ).groups()[0].split(', ' )
_A = [obj[1:-1] for obj in imports if len(_snake_case ) > 0]
objects.extend(_snake_case )
elif _re_between_brackets.search(_snake_case ) is not None:
_A = _re_between_brackets.search(_snake_case ).groups()[0].split(', ' )
_A = [obj[1:-1] for obj in imports if len(_snake_case ) > 0]
objects.extend(_snake_case )
elif _re_quote_object.search(_snake_case ) is not None:
objects.append(_re_quote_object.search(_snake_case ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
_A = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_A = []
while (
line_index < len(_snake_case )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
_A = lines[line_index]
_A = _re_import.search(_snake_case )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
_A = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(_snake_case ):
# If the line is an if is_backend_available, we grab all objects associated.
_A = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_A = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
_A = lines[line_index]
_A = _re_import.search(_snake_case )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
_A = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def _snake_case ( _snake_case : List[Any] , _snake_case : str ) -> int:
'''simple docstring'''
def find_duplicates(_snake_case : List[str] ):
return [k for k, v in collections.Counter(_snake_case ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
_A = []
for key in import_dict_objects.keys():
_A = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
_A = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
_A = 'base imports' if key == 'none' else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def _snake_case ( ) -> Optional[int]:
'''simple docstring'''
_A = []
for root, _, files in os.walk(_snake_case ):
if "__init__.py" in files:
_A = os.path.join(_snake_case , '__init__.py' )
_A = parse_init(_snake_case )
if objects is not None:
_A = analyze_results(*_snake_case )
if len(_snake_case ) > 0:
_A = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(_snake_case ) )
if len(_snake_case ) > 0:
raise ValueError('\n\n'.join(_snake_case ) )
def _snake_case ( ) -> str:
'''simple docstring'''
_A = []
for path, directories, files in os.walk(_snake_case ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(_snake_case )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(_snake_case ) / folder).glob('*.py' ) ) ) == 0:
continue
_A = str((Path(_snake_case ) / folder).relative_to(_snake_case ) )
_A = short_path.replace(os.path.sep , '.' )
submodules.append(_snake_case )
for fname in files:
if fname == "__init__.py":
continue
_A = str((Path(_snake_case ) / fname).relative_to(_snake_case ) )
_A = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(_snake_case )
return submodules
a = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
'''models.esm.openfold_utils''',
]
def _snake_case ( ) -> str:
'''simple docstring'''
from transformers.utils import direct_transformers_import
_A = direct_transformers_import(_snake_case )
_A = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_snake_case , '__init__.py' ) , 'r' ) as f:
_A = f.read()
import_structure_keys.update(set(re.findall(R'import_structure\[\"([^\"]*)\"\]' , _snake_case ) ) )
_A = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_snake_case ) > 0:
_A = '\n'.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registed in the main init of Transformers:\n'
F'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 315 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''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 lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = '''xlnet'''
UpperCAmelCase : List[Any] = ['''mems''']
UpperCAmelCase : Any = {
'''n_token''': '''vocab_size''', # Backward compatibility
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Union[str, Any] , _UpperCAmelCase : Dict=32_000 , _UpperCAmelCase : List[str]=1_024 , _UpperCAmelCase : Any=24 , _UpperCAmelCase : Union[str, Any]=16 , _UpperCAmelCase : Union[str, Any]=4_096 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Any=True , _UpperCAmelCase : str="bi" , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Optional[Any]=1E-1_2 , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : Any=512 , _UpperCAmelCase : Dict=None , _UpperCAmelCase : int=True , _UpperCAmelCase : int=False , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : int=-1 , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : Union[str, Any]="last" , _UpperCAmelCase : int=True , _UpperCAmelCase : str="tanh" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Dict=5 , _UpperCAmelCase : Optional[Any]=5 , _UpperCAmelCase : Union[str, Any]=5 , _UpperCAmelCase : List[str]=1 , _UpperCAmelCase : Dict=2 , **_UpperCAmelCase : int , ):
_A = vocab_size
_A = d_model
_A = n_layer
_A = 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})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = 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.' , _UpperCAmelCase , )
_A = kwargs['use_cache']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : Tuple ):
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 : Optional[Any] , _UpperCAmelCase : Optional[Any] ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 315 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCAmelCase )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
# `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] , _UpperCAmelCase : Optional[Any] ):
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] , _UpperCAmelCase ):
raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' )
_A = copy.deepcopy(self )
_A = self.label_schema.copy()
_A = features[self.label_column]
_A = label_schema
return task_template
@property
def lowerCAmelCase_ ( self : Any ):
return {
self.text_column: "text",
self.label_column: "labels",
}
| 315 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
a = {
'''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
'''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def _snake_case ( _snake_case : Tuple ) -> Dict:
'''simple docstring'''
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def _snake_case ( _snake_case : str , _snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
if args.student_type == "roberta":
_A = False
elif args.student_type == "gpt2":
_A = False
def _snake_case ( _snake_case : str , _snake_case : int ) -> Tuple:
'''simple docstring'''
if args.student_type == "roberta":
_A = False
def _snake_case ( ) -> Tuple:
'''simple docstring'''
_A = argparse.ArgumentParser(description='Training' )
parser.add_argument('--force' , action='store_true' , help='Overwrite dump_path if it already exists.' )
parser.add_argument(
'--dump_path' , type=_snake_case , required=_snake_case , help='The output directory (log, checkpoints, parameters, etc.)' )
parser.add_argument(
'--data_file' , type=_snake_case , required=_snake_case , help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.' , )
parser.add_argument(
'--student_type' , type=_snake_case , choices=['distilbert', 'roberta', 'gpt2'] , required=_snake_case , help='The student type (DistilBERT, RoBERTa).' , )
parser.add_argument('--student_config' , type=_snake_case , required=_snake_case , help='Path to the student configuration.' )
parser.add_argument(
'--student_pretrained_weights' , default=_snake_case , type=_snake_case , help='Load student initialization checkpoint.' )
parser.add_argument(
'--teacher_type' , choices=['bert', 'roberta', 'gpt2'] , required=_snake_case , help='Teacher type (BERT, RoBERTa).' )
parser.add_argument('--teacher_name' , type=_snake_case , required=_snake_case , help='The teacher model.' )
parser.add_argument('--temperature' , default=2.0 , type=_snake_case , help='Temperature for the softmax temperature.' )
parser.add_argument(
'--alpha_ce' , default=0.5 , type=_snake_case , help='Linear weight for the distillation loss. Must be >=0.' )
parser.add_argument(
'--alpha_mlm' , default=0.0 , type=_snake_case , help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.' , )
parser.add_argument('--alpha_clm' , default=0.5 , type=_snake_case , help='Linear weight for the CLM loss. Must be >=0.' )
parser.add_argument('--alpha_mse' , default=0.0 , type=_snake_case , help='Linear weight of the MSE loss. Must be >=0.' )
parser.add_argument(
'--alpha_cos' , default=0.0 , type=_snake_case , help='Linear weight of the cosine embedding loss. Must be >=0.' )
parser.add_argument(
'--mlm' , action='store_true' , help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.' )
parser.add_argument(
'--mlm_mask_prop' , default=0.15 , type=_snake_case , help='Proportion of tokens for which we need to make a prediction.' , )
parser.add_argument('--word_mask' , default=0.8 , type=_snake_case , help='Proportion of tokens to mask out.' )
parser.add_argument('--word_keep' , default=0.1 , type=_snake_case , help='Proportion of tokens to keep.' )
parser.add_argument('--word_rand' , default=0.1 , type=_snake_case , help='Proportion of tokens to randomly replace.' )
parser.add_argument(
'--mlm_smoothing' , default=0.7 , type=_snake_case , help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).' , )
parser.add_argument('--token_counts' , type=_snake_case , help='The token counts in the data_file for MLM.' )
parser.add_argument(
'--restrict_ce_to_mask' , action='store_true' , help='If true, compute the distillation loss only the [MLM] prediction distribution.' , )
parser.add_argument(
'--freeze_pos_embs' , action='store_true' , help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.' , )
parser.add_argument(
'--freeze_token_type_embds' , action='store_true' , help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.' , )
parser.add_argument('--n_epoch' , type=_snake_case , default=3 , help='Number of pass on the whole dataset.' )
parser.add_argument('--batch_size' , type=_snake_case , default=5 , help='Batch size (for each process).' )
parser.add_argument(
'--group_by_size' , action='store_false' , help='If true, group sequences that have similar length into the same batch. Default is true.' , )
parser.add_argument(
'--gradient_accumulation_steps' , type=_snake_case , default=50 , help='Gradient accumulation for larger training batches.' , )
parser.add_argument('--warmup_prop' , default=0.05 , type=_snake_case , help='Linear warmup proportion.' )
parser.add_argument('--weight_decay' , default=0.0 , type=_snake_case , help='Weight decay if we apply some.' )
parser.add_argument('--learning_rate' , default=5E-4 , type=_snake_case , help='The initial learning rate for Adam.' )
parser.add_argument('--adam_epsilon' , default=1E-6 , type=_snake_case , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , default=5.0 , type=_snake_case , help='Max gradient norm.' )
parser.add_argument('--initializer_range' , default=0.02 , type=_snake_case , help='Random initialization range.' )
parser.add_argument(
'--fp16' , action='store_true' , help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit' , )
parser.add_argument(
'--fp16_opt_level' , type=_snake_case , default='O1' , help=(
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
'See details at https://nvidia.github.io/apex/amp.html'
) , )
parser.add_argument('--n_gpu' , type=_snake_case , default=1 , help='Number of GPUs in the node.' )
parser.add_argument('--local_rank' , type=_snake_case , default=-1 , help='Distributed training - Local rank' )
parser.add_argument('--seed' , type=_snake_case , default=56 , help='Random seed' )
parser.add_argument('--log_interval' , type=_snake_case , default=5_00 , help='Tensorboard logging interval.' )
parser.add_argument('--checkpoint_interval' , type=_snake_case , default=40_00 , help='Checkpoint interval.' )
_A = parser.parse_args()
sanity_checks(_snake_case )
# ARGS #
init_gpu_params(_snake_case )
set_seed(_snake_case )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
' itUse `--force` if you want to overwrite it' )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(F'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(F'''Param: {args}''' )
with open(os.path.join(args.dump_path , 'parameters.json' ) , 'w' ) as f:
json.dump(vars(_snake_case ) , _snake_case , indent=4 )
git_log(args.dump_path )
_A , _A , _A = MODEL_CLASSES[args.student_type]
_A , _A , _A = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
_A = teacher_tokenizer_class.from_pretrained(args.teacher_name )
_A = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
_A = tokenizer.all_special_tokens.index(_snake_case )
_A = tokenizer.all_special_ids[idx]
logger.info(F'''Special tokens {special_tok_ids}''' )
_A = special_tok_ids
_A = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(F'''Loading data from {args.data_file}''' )
with open(args.data_file , 'rb' ) as fp:
_A = pickle.load(_snake_case )
if args.mlm:
logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , 'rb' ) as fp:
_A = pickle.load(_snake_case )
_A = np.maximum(_snake_case , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
_A = 0.0 # do not predict special tokens
_A = torch.from_numpy(_snake_case )
else:
_A = None
_A = LmSeqsDataset(params=_snake_case , data=_snake_case )
logger.info('Data loader created.' )
# STUDENT #
logger.info(F'''Loading student config from {args.student_config}''' )
_A = student_config_class.from_pretrained(args.student_config )
_A = True
if args.student_pretrained_weights is not None:
logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
_A = student_model_class.from_pretrained(args.student_pretrained_weights , config=_snake_case )
else:
_A = student_model_class(_snake_case )
if args.n_gpu > 0:
student.to(F'''cuda:{args.local_rank}''' )
logger.info('Student loaded.' )
# TEACHER #
_A = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=_snake_case )
if args.n_gpu > 0:
teacher.to(F'''cuda:{args.local_rank}''' )
logger.info(F'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(_snake_case , _snake_case )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(_snake_case , _snake_case )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
_A = Distiller(
params=_snake_case , dataset=_snake_case , token_probs=_snake_case , student=_snake_case , teacher=_snake_case )
distiller.train()
logger.info('Let\'s go get some drinks.' )
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
a = random.Random()
def _snake_case ( _snake_case : Optional[int] , _snake_case : Union[str, Any]=1.0 , _snake_case : Any=None , _snake_case : Optional[Any]=None ) -> Optional[Any]:
'''simple docstring'''
if rng is None:
_A = global_rng
_A = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=7 , _UpperCAmelCase : str=400 , _UpperCAmelCase : Optional[Any]=2_000 , _UpperCAmelCase : int=24 , _UpperCAmelCase : List[str]=24 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : str=16_000 , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Dict=True , ):
_A = parent
_A = batch_size
_A = min_seq_length
_A = max_seq_length
_A = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_A = feature_size
_A = num_mel_bins
_A = padding_value
_A = sampling_rate
_A = return_attention_mask
_A = do_normalize
def lowerCAmelCase_ ( self : Any ):
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Dict=False , _UpperCAmelCase : str=False ):
def _flatten(_UpperCAmelCase : List[Any] ):
return list(itertools.chain(*_UpperCAmelCase ) )
if equal_length:
_A = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_A = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_A = [np.asarray(_UpperCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : int = SpeechaTextFeatureExtractor if is_speech_available() else None
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = SpeechaTextFeatureExtractionTester(self )
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : Any ):
self.assertTrue(np.all(np.mean(_UpperCAmelCase , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase , axis=0 ) - 1 ) < 1E-3 ) )
def lowerCAmelCase_ ( self : int ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
# Test feature size
_A = feature_extractor(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='np' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
_A = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_features
_A = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_features
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test batched
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
_A = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_A = np.asarray(_UpperCAmelCase )
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = ['longest', 'max_length', 'do_not_pad']
_A = [None, 16, None]
for max_length, padding in zip(_UpperCAmelCase , _UpperCAmelCase ):
_A = feature_extractor(
_UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase )
_A = inputs.input_features
_A = inputs.attention_mask
_A = [np.sum(_UpperCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowerCAmelCase_ ( self : Any ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = ['longest', 'max_length', 'do_not_pad']
_A = [None, 16, None]
for max_length, padding in zip(_UpperCAmelCase , _UpperCAmelCase ):
_A = feature_extractor(
_UpperCAmelCase , max_length=_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='np' , return_attention_mask=_UpperCAmelCase )
_A = inputs.input_features
_A = inputs.attention_mask
_A = [np.sum(_UpperCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowerCAmelCase_ ( self : int ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = feature_extractor(
_UpperCAmelCase , padding='max_length' , max_length=4 , truncation=_UpperCAmelCase , return_tensors='np' , return_attention_mask=_UpperCAmelCase , )
_A = inputs.input_features
_A = inputs.attention_mask
_A = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def lowerCAmelCase_ ( self : Dict ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = feature_extractor(
_UpperCAmelCase , padding='longest' , max_length=4 , truncation=_UpperCAmelCase , return_tensors='np' , return_attention_mask=_UpperCAmelCase , )
_A = inputs.input_features
_A = inputs.attention_mask
_A = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 4, 24) )
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = feature_extractor(
_UpperCAmelCase , padding='longest' , max_length=16 , truncation=_UpperCAmelCase , return_tensors='np' , return_attention_mask=_UpperCAmelCase , )
_A = inputs.input_features
_A = inputs.attention_mask
_A = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 6, 24) )
def lowerCAmelCase_ ( self : List[str] ):
import torch
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = np.random.rand(100 , 32 ).astype(np.floataa )
_A = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_A = feature_extractor.pad([{'input_features': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_A = feature_extractor.pad([{'input_features': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Dict ):
from datasets import load_dataset
_A = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
_A = ds.sort('id' ).select(range(_UpperCAmelCase ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def lowerCAmelCase_ ( self : Dict ):
# fmt: off
_A = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
] )
# fmt: on
_A = self._load_datasamples(1 )
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = feature_extractor(_UpperCAmelCase , return_tensors='pt' ).input_features
self.assertEquals(input_features.shape , (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30] , _UpperCAmelCase , atol=1E-4 ) )
| 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_A = Rectangle(height=0.25 , width=0.25 )
_A = [mem.copy() for i in range(6 )]
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('CPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(4 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('GPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Model' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(_UpperCAmelCase )
_A = []
_A = []
for i, rect in enumerate(_UpperCAmelCase ):
_A = fill.copy().set_fill(_UpperCAmelCase , opacity=0.8 )
target.move_to(_UpperCAmelCase )
model_arr.append(_UpperCAmelCase )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_UpperCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(_UpperCAmelCase )
self.add(*_UpperCAmelCase , *_UpperCAmelCase )
_A = [meta_mem.copy() for i in range(6 )]
_A = [meta_mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Disk' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_A = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_UpperCAmelCase )
_A = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase ) )
_A = Square(0.3 )
input.set_fill(_UpperCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , _UpperCAmelCase , buff=0.5 )
self.play(Write(_UpperCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=_UpperCAmelCase , buff=0.02 )
self.play(MoveToTarget(_UpperCAmelCase ) )
self.play(FadeOut(_UpperCAmelCase ) )
_A = Arrow(start=_UpperCAmelCase , end=_UpperCAmelCase , color=_UpperCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , _UpperCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
_A = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) )
_A = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(_UpperCAmelCase ) , Circumscribe(model_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
_A = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , _UpperCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
_A = AnimationGroup(
FadeOut(_UpperCAmelCase , run_time=0.5 ) , MoveToTarget(_UpperCAmelCase , run_time=0.5 ) , FadeIn(_UpperCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(_UpperCAmelCase )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
_A = 0.7
self.play(
Circumscribe(model_arr[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_arr[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
_A = a_c
_A = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(_UpperCAmelCase ) , FadeOut(_UpperCAmelCase , run_time=0.5 ) , )
_A = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) , MoveToTarget(_UpperCAmelCase ) )
self.wait()
| 315 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''facebook/deit-base-distilled-patch16-224''': (
'''https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'''
),
# See all DeiT models at https://huggingface.co/models?filter=deit
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : str = '''deit'''
def __init__( self : Optional[int] , _UpperCAmelCase : Optional[int]=768 , _UpperCAmelCase : Optional[Any]=12 , _UpperCAmelCase : Any=12 , _UpperCAmelCase : Optional[int]=3_072 , _UpperCAmelCase : List[Any]="gelu" , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : str=0.0 , _UpperCAmelCase : Tuple=0.02 , _UpperCAmelCase : Dict=1E-1_2 , _UpperCAmelCase : int=224 , _UpperCAmelCase : List[Any]=16 , _UpperCAmelCase : str=3 , _UpperCAmelCase : int=True , _UpperCAmelCase : Dict=16 , **_UpperCAmelCase : Tuple , ):
super().__init__(**_UpperCAmelCase )
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = initializer_range
_A = layer_norm_eps
_A = image_size
_A = patch_size
_A = num_channels
_A = qkv_bias
_A = encoder_stride
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = version.parse('''1.11''' )
@property
def lowerCAmelCase_ ( self : Dict ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCAmelCase_ ( self : List[Any] ):
return 1E-4
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
_A , _A = list_data[i + 1], list_data[i]
_A = True
return list_data if not swapped else bubble_sort(_snake_case , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a = False
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_torch_gpu
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = 'A painting of a squirrel eating a burger '
_A = torch.manual_seed(0 )
_A = pipe(
prompt=_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_UpperCAmelCase )
_A = VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = generator.manual_seed(0 )
_A = pipe(
prompt=_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def lowerCAmelCase_ ( self : Tuple ):
_A = VersatileDiffusionTextToImagePipeline.from_pretrained(
'shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = 'A painting of a squirrel eating a burger '
_A = torch.manual_seed(0 )
_A = pipe(
prompt=_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' ).images
_A = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_A = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Any = ['''input_values''', '''attention_mask''']
def __init__( self : Dict , _UpperCAmelCase : int = 1 , _UpperCAmelCase : int = 16_000 , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 80 , _UpperCAmelCase : int = 16 , _UpperCAmelCase : int = 64 , _UpperCAmelCase : str = "hann_window" , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 80 , _UpperCAmelCase : float = 7_600 , _UpperCAmelCase : float = 1E-1_0 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : bool = True , **_UpperCAmelCase : List[Any] , ):
super().__init__(feature_size=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , padding_value=_UpperCAmelCase , **_UpperCAmelCase )
_A = do_normalize
_A = return_attention_mask
_A = num_mel_bins
_A = hop_length
_A = win_length
_A = win_function
_A = frame_signal_scale
_A = fmin
_A = fmax
_A = mel_floor
_A = reduction_factor
_A = win_length * sampling_rate // 1_000
_A = hop_length * sampling_rate // 1_000
_A = optimal_fft_length(self.sample_size )
_A = (self.n_fft // 2) + 1
_A = window_function(window_length=self.sample_size , name=self.win_function , periodic=_UpperCAmelCase )
_A = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm='slaney' , mel_scale='slaney' , )
if frame_signal_scale != 1.0:
warnings.warn(
'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , )
if reduction_factor != 2.0:
warnings.warn(
'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def lowerCAmelCase_ ( _UpperCAmelCase : List[np.ndarray] , _UpperCAmelCase : List[np.ndarray] , _UpperCAmelCase : float = 0.0 ):
if attention_mask is not None:
_A = np.array(_UpperCAmelCase , np.intaa )
_A = []
for vector, length in zip(_UpperCAmelCase , attention_mask.sum(-1 ) ):
_A = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
_A = padding_value
normed_input_values.append(_UpperCAmelCase )
else:
_A = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : np.ndarray , ):
_A = spectrogram(
_UpperCAmelCase , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel='log10' , )
return log_mel_spec.T
def __call__( self : int , _UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : Optional[int] = None , **_UpperCAmelCase : Optional[int] , ):
if audio is None and audio_target is None:
raise ValueError('You must provide either `audio` or `audio_target` values.' )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
if audio is not None:
_A = self._process_audio(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , )
else:
_A = None
if audio_target is not None:
_A = self._process_audio(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , )
if inputs is None:
return inputs_target
else:
_A = inputs_target['input_values']
_A = inputs_target.get('attention_mask' )
if decoder_attention_mask is not None:
_A = decoder_attention_mask
return inputs
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _UpperCAmelCase : bool = False , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , **_UpperCAmelCase : List[Any] , ):
_A = isinstance(_UpperCAmelCase , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
_A = is_batched_numpy or (
isinstance(_UpperCAmelCase , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_A = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(_UpperCAmelCase , np.ndarray ):
_A = np.asarray(_UpperCAmelCase , dtype=np.floataa )
elif isinstance(_UpperCAmelCase , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
_A = speech.astype(np.floataa )
# always return batch
if not is_batched:
_A = [speech]
# needed to make pad() work on spectrogram inputs
_A = self.feature_size
# convert into correct format for padding
if is_target:
_A = [self._extract_mel_features(_UpperCAmelCase ) for waveform in speech]
_A = BatchFeature({'input_values': features} )
_A = self.num_mel_bins
else:
_A = BatchFeature({'input_values': speech} )
_A = self.pad(
_UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , **_UpperCAmelCase , )
_A = feature_size_hack
# convert input values to correct format
_A = padded_inputs['input_values']
if not isinstance(input_values[0] , np.ndarray ):
_A = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(_UpperCAmelCase , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
_A = [array.astype(np.floataa ) for array in input_values]
elif isinstance(_UpperCAmelCase , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
_A = input_values.astype(np.floataa )
# convert attention_mask to correct format
_A = padded_inputs.get('attention_mask' )
if attention_mask is not None:
_A = [np.asarray(_UpperCAmelCase , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
_A = (
attention_mask
if self._get_padding_strategies(_UpperCAmelCase , max_length=_UpperCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
else None
)
_A = self.zero_mean_unit_var_norm(
padded_inputs['input_values'] , attention_mask=_UpperCAmelCase , padding_value=self.padding_value )
if return_tensors is not None:
_A = padded_inputs.convert_to_tensors(_UpperCAmelCase )
return padded_inputs
def lowerCAmelCase_ ( self : Any ):
_A = super().to_dict()
# Don't serialize these as they are derived from the other properties.
_A = ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
for name in names:
if name in output:
del output[name]
return output
| 315 | 1 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowercase_ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : Optional[Any]="sayef/fsner-bert-base-uncased" ):
super(_UpperCAmelCase , self ).__init__()
_A = AutoModel.from_pretrained(_UpperCAmelCase , return_dict=_UpperCAmelCase )
_A = torch.nn.CosineSimilarity(3 , 1E-0_8 )
_A = torch.nn.Softmax(dim=1 )
def lowerCAmelCase_ ( self : Dict , **_UpperCAmelCase : int ):
return self.bert(**_UpperCAmelCase ).last_hidden_state
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : List[str] ):
return token_embeddings.sum(2 , keepdim=_UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any]=1 ):
return self.softmax(T * self.cos(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple ):
_A = W_supports['sizes'].tolist()
_A = W_supports['start_token_id'].item()
_A = W_supports['end_token_id'].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
_A = self.BERT(**_UpperCAmelCase )
_A = self.BERT(**_UpperCAmelCase )
_A = None
_A = None
_A = W_supports['input_ids'] == start_token_id
_A = W_supports['input_ids'] == end_token_id
for i, size in enumerate(_UpperCAmelCase ):
if i == 0:
_A = 0
else:
_A = support_sizes[i - 1]
_A = S[s : s + size][start_token_masks[s : s + size]]
_A = S[s : s + size][end_token_masks[s : s + size]]
_A = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
_A = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
_A = torch.vstack((p_starts, p_start) )
_A = torch.vstack((p_ends, p_end) )
else:
_A = p_start
_A = p_end
return p_starts, p_ends
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = []
create_all_state(1 , _snake_case , _snake_case , [] , _snake_case )
return result
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : list[int] , _snake_case : list[list[int]] , ) -> None:
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(_snake_case , total_number - level + 2 ):
current_list.append(_snake_case )
create_all_state(i + 1 , _snake_case , level - 1 , _snake_case , _snake_case )
current_list.pop()
def _snake_case ( _snake_case : list[list[int]] ) -> None:
'''simple docstring'''
for i in total_list:
print(*_snake_case )
if __name__ == "__main__":
a = 4
a = 2
a = generate_all_combinations(n, k)
print_all_state(total_list)
| 315 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _snake_case ( _snake_case : str = "https://www.worldometers.info/coronavirus" ) -> dict:
'''simple docstring'''
_A = BeautifulSoup(requests.get(_snake_case ).text , 'html.parser' )
_A = soup.findAll('h1' )
_A = soup.findAll('div' , {'class': 'maincounter-number'} )
keys += soup.findAll('span' , {'class': 'panel-title'} )
values += soup.findAll('div' , {'class': 'number-table-main'} )
return {key.text.strip(): value.text.strip() for key, value in zip(_snake_case , _snake_case )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 315 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Union[str, Any] ):
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[str] ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple=True ):
_A = ()
for resnet, attn in zip(self.resnets , self.attentions ):
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[Any] ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[str]=True ):
_A = ()
for resnet in self.resnets:
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any]=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : int , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int]=True ):
for resnet in self.resnets:
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Dict ):
# there is always at least one resnet
_A = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_A = []
for _ in range(self.num_layers ):
_A = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
_A = attentions
def __call__( self : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int]=True ):
_A = self.resnets[0](_UpperCAmelCase , _UpperCAmelCase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
return hidden_states
| 315 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = ShapEImgaImgPipeline
UpperCAmelCase : Dict = ['''image''']
UpperCAmelCase : List[Any] = ['''image''']
UpperCAmelCase : Optional[Any] = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
UpperCAmelCase : Union[str, Any] = False
@property
def lowerCAmelCase_ ( self : List[str] ):
return 32
@property
def lowerCAmelCase_ ( self : str ):
return 32
@property
def lowerCAmelCase_ ( self : Tuple ):
return self.time_input_dim * 4
@property
def lowerCAmelCase_ ( self : Optional[int] ):
return 8
@property
def lowerCAmelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
_A = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
_A = CLIPVisionModel(_UpperCAmelCase )
return model
@property
def lowerCAmelCase_ ( self : Optional[int] ):
_A = CLIPImageProcessor(
crop_size=224 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] , image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] , resample=3 , size=224 , )
return image_processor
@property
def lowerCAmelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
_A = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
_A = PriorTransformer(**_UpperCAmelCase )
return model
@property
def lowerCAmelCase_ ( self : str ):
torch.manual_seed(0 )
_A = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
_A = ShapERenderer(**_UpperCAmelCase )
return model
def lowerCAmelCase_ ( self : List[str] ):
_A = self.dummy_prior
_A = self.dummy_image_encoder
_A = self.dummy_image_processor
_A = self.dummy_renderer
_A = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1_024 , prediction_type='sample' , use_karras_sigmas=_UpperCAmelCase , clip_sample=_UpperCAmelCase , clip_sample_range=1.0 , )
_A = {
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=0 ):
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith('mps' ):
_A = torch.manual_seed(_UpperCAmelCase )
else:
_A = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
_A = {
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def lowerCAmelCase_ ( self : Optional[int] ):
_A = 'cpu'
_A = self.get_dummy_components()
_A = self.pipeline_class(**_UpperCAmelCase )
_A = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
_A = output.images[0]
_A = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_A = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase_ ( self : Any ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCAmelCase_ ( self : Tuple ):
_A = torch_device == 'cpu'
_A = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , )
def lowerCAmelCase_ ( self : int ):
_A = self.get_dummy_components()
_A = self.pipeline_class(**_UpperCAmelCase )
_A = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = 1
_A = 2
_A = self.get_dummy_inputs(_UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
_A = batch_size * [inputs[key]]
_A = pipe(**_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : List[str] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self : str ):
_A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png' )
_A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy' )
_A = ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img' )
_A = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
_A = pipe(
_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
| 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
_A = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
_A = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
_A = numpy.random.rand(3 , 1 )
# Real output values provided.
_A = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
_A = numpy.zeros(output_array.shape )
def lowerCAmelCase_ ( self : List[str] ):
_A = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def lowerCAmelCase_ ( self : Optional[int] ):
_A = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
_A = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
_A = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : int , _UpperCAmelCase : bool ):
for iteration in range(1 , iterations + 1 ):
_A = self.feedforward()
self.back_propagation()
if give_loss:
_A = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F'''Iteration {iteration} Loss: {loss}''' )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : numpy.ndarray ):
_A = input_arr
_A = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return 1 / (1 + numpy.exp(-value ))
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return (value) * (1 - (value))
def _snake_case ( ) -> int:
'''simple docstring'''
_A = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
_A = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
_A = TwoHiddenLayerNeuralNetwork(
input_array=_snake_case , output_array=_snake_case )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_snake_case , iterations=10 , give_loss=_snake_case )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 315 | 1 |
"""simple docstring"""
import math
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 SchedulerMixin, SchedulerOutput
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Any = 1
@register_to_config
def __init__( self : str , _UpperCAmelCase : int = 1_000 , _UpperCAmelCase : Optional[Union[np.ndarray, List[float]]] = None ):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(_UpperCAmelCase )
# standard deviation of the initial noise distribution
_A = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_A = 4
# running values
_A = []
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, torch.device] = None ):
_A = num_inference_steps
_A = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_A = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_A = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_A = torch.sin(steps * math.pi / 2 ) ** 2
_A = (1.0 - self.betas**2) ** 0.5
_A = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_A = timesteps.to(_UpperCAmelCase )
_A = []
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : torch.FloatTensor , _UpperCAmelCase : int , _UpperCAmelCase : torch.FloatTensor , _UpperCAmelCase : bool = True , ):
if self.num_inference_steps is None:
raise ValueError(
'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler' )
_A = (self.timesteps == timestep).nonzero().item()
_A = timestep_index + 1
_A = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(_UpperCAmelCase )
if len(self.ets ) == 1:
_A = self.ets[-1]
elif len(self.ets ) == 2:
_A = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_A = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_A = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_A = self._get_prev_sample(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_UpperCAmelCase )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : torch.FloatTensor , *_UpperCAmelCase : Dict , **_UpperCAmelCase : Dict ):
return sample
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int ):
_A = self.alphas[timestep_index]
_A = self.betas[timestep_index]
_A = self.alphas[prev_timestep_index]
_A = self.betas[prev_timestep_index]
_A = (sample - sigma * ets) / max(_UpperCAmelCase , 1E-8 )
_A = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : List[Any] ):
return self.config.num_train_timesteps
| 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : T ):
_A = data
_A = None
def __str__( self : str ):
return F'''{self.data}'''
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple ):
_A = None
def __iter__( self : List[Any] ):
_A = self.top
while node:
yield node.data
_A = node.next
def __str__( self : Union[str, Any] ):
return "->".join([str(_UpperCAmelCase ) for item in self] )
def __len__( self : List[Any] ):
return len(tuple(iter(self ) ) )
def lowerCAmelCase_ ( self : str ):
return self.top is None
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : T ):
_A = Node(_UpperCAmelCase )
if not self.is_empty():
_A = self.top
_A = node
def lowerCAmelCase_ ( self : Dict ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _UpperCAmelCase )
_A = self.top
_A = self.top.next
return pop_node.data
def lowerCAmelCase_ ( self : Tuple ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 315 | 1 |
"""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 : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any]=13 , _UpperCAmelCase : Dict=30 , _UpperCAmelCase : str=2 , _UpperCAmelCase : Optional[Any]=3 , _UpperCAmelCase : Optional[int]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : List[Any]=32 , _UpperCAmelCase : str=5 , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : Optional[Any]=37 , _UpperCAmelCase : str="gelu" , _UpperCAmelCase : List[str]=0.1 , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : Optional[Any]=10 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : str=3 , _UpperCAmelCase : int=0.6 , _UpperCAmelCase : Optional[int]=None , ):
_A = parent
_A = batch_size
_A = image_size
_A = patch_size
_A = num_channels
_A = is_training
_A = use_labels
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = type_sequence_label_size
_A = initializer_range
_A = mask_ratio
_A = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
_A = (image_size // patch_size) ** 2
_A = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ ( self : 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=_UpperCAmelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str] ):
_A = ViTMAEModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : int ):
_A = ViTMAEForPreTraining(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase )
_A = (self.image_size // self.patch_size) ** 2
_A = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
_A = 1
_A = ViTMAEForPreTraining(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_A = model(_UpperCAmelCase )
_A = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowerCAmelCase_ ( self : int ):
_A = self.prepare_config_and_inputs()
_A , _A , _A = config_and_inputs
_A = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : str = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
UpperCAmelCase : int = {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : List[str] = False
UpperCAmelCase : Optional[int] = False
UpperCAmelCase : Optional[int] = False
def lowerCAmelCase_ ( self : Optional[int] ):
_A = ViTMAEModelTester(self )
_A = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def lowerCAmelCase_ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMAE does not use inputs_embeds' )
def lowerCAmelCase_ ( self : Optional[int] ):
pass
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(_UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_A = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_UpperCAmelCase , nn.Linear ) )
def lowerCAmelCase_ ( self : Tuple ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(_UpperCAmelCase )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['pixel_values']
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : str ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
_A = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
_A = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
_A = torch.from_numpy(_UpperCAmelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
_A = pt_noise
super().check_pt_tf_models(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Tuple ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
_A = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
_A = outputs[0].cpu().numpy()
_A = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_UpperCAmelCase )
_A = model_class.from_pretrained(_UpperCAmelCase )
model.to(_UpperCAmelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
_A = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
# Make sure we don't have nans
_A = after_outputs[0].cpu().numpy()
_A = 0
_A = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_UpperCAmelCase , 1E-5 )
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def lowerCAmelCase_ ( self : Optional[int] ):
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def lowerCAmelCase_ ( self : Tuple ):
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def lowerCAmelCase_ ( self : Optional[Any] ):
pass
@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
def lowerCAmelCase_ ( self : List[Any] ):
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
pass
@slow
def lowerCAmelCase_ ( self : Any ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = ViTMAEModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def _snake_case ( ) -> Optional[int]:
'''simple docstring'''
_A = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : List[Any] ):
return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None
@slow
def lowerCAmelCase_ ( self : Dict ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
_A = ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' ).to(_UpperCAmelCase )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=_UpperCAmelCase , return_tensors='pt' ).to(_UpperCAmelCase )
# 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)
_A = ViTMAEConfig()
_A = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
_A = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
_A = model(**_UpperCAmelCase , noise=torch.from_numpy(_UpperCAmelCase ).to(device=_UpperCAmelCase ) )
# verify the logits
_A = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , _UpperCAmelCase )
_A = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(_UpperCAmelCase ) , atol=1E-4 ) )
| 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Union[str, Any] ):
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = '''visual_bert'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : Optional[Any]=30_522 , _UpperCAmelCase : List[Any]=768 , _UpperCAmelCase : Dict=512 , _UpperCAmelCase : Any=12 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : Union[str, Any]=3_072 , _UpperCAmelCase : Optional[Any]="gelu" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : Union[str, Any]=512 , _UpperCAmelCase : str=2 , _UpperCAmelCase : List[str]=0.02 , _UpperCAmelCase : Union[str, Any]=1E-1_2 , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : List[Any]=1 , _UpperCAmelCase : str=0 , _UpperCAmelCase : str=2 , **_UpperCAmelCase : Any , ):
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
_A = vocab_size
_A = max_position_embeddings
_A = hidden_size
_A = visual_embedding_dim
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = initializer_range
_A = type_vocab_size
_A = layer_norm_eps
_A = bypass_transformer
_A = special_visual_initialize
| 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def _snake_case ( _snake_case : str ) -> list[str]:
'''simple docstring'''
return word_by_signature[signature(_snake_case )]
a = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
a = sorted({word.strip().lower() for word in data.splitlines()})
a = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
a = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 315 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The column name of the images in the files.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the training data.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the validation data.'''} )
UpperCAmelCase : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def lowerCAmelCase_ ( self : Dict ):
_A = {}
if self.train_dir is not None:
_A = self.train_dir
if self.validation_dir is not None:
_A = self.validation_dir
_A = data_files if data_files else None
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : str = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
UpperCAmelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase : str = field(default=__lowerCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCAmelCase : float = field(
default=0.75 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : float = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def _snake_case ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
_A = torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _snake_case ( ) -> List[str]:
'''simple docstring'''
_A = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A , _A , _A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , _snake_case , _snake_case )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_A = training_args.get_process_log_level()
logger.setLevel(_snake_case )
transformers.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
_A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_A = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _snake_case ) and data_args.train_val_split > 0.0:
_A = ds['train'].train_test_split(data_args.train_val_split )
_A = split['train']
_A = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_A = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
_A = ViTMAEConfig.from_pretrained(model_args.config_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_A = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_A = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
_A = ViTMAEForPreTraining(_snake_case )
if training_args.do_train:
_A = ds['train'].column_names
else:
_A = ds['validation'].column_names
if data_args.image_column_name is not None:
_A = data_args.image_column_name
elif "image" in column_names:
_A = 'image'
elif "img" in column_names:
_A = 'img'
else:
_A = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_A = image_processor.size['shortest_edge']
else:
_A = (image_processor.size['height'], image_processor.size['width'])
_A = Compose(
[
Lambda(lambda _snake_case : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_snake_case , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_snake_case : List[Any] ):
_A = [transforms(_snake_case ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
_A = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_snake_case )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
_A = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_snake_case )
# Compute absolute learning rate
_A = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_A = training_args.base_learning_rate * total_train_batch_size / 2_56
# Initialize our trainer
_A = Trainer(
model=_snake_case , args=_snake_case , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_snake_case , data_collator=_snake_case , )
# Training
if training_args.do_train:
_A = None
if training_args.resume_from_checkpoint is not None:
_A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A = last_checkpoint
_A = trainer.train(resume_from_checkpoint=_snake_case )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_A = trainer.evaluate()
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
# Write model card and (optionally) push to hub
_A = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def _snake_case ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The column name of the images in the files.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the training data.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the validation data.'''} )
UpperCAmelCase : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def lowerCAmelCase_ ( self : Dict ):
_A = {}
if self.train_dir is not None:
_A = self.train_dir
if self.validation_dir is not None:
_A = self.validation_dir
_A = data_files if data_files else None
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : str = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
UpperCAmelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase : str = field(default=__lowerCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCAmelCase : float = field(
default=0.75 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : float = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def _snake_case ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
_A = torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _snake_case ( ) -> List[str]:
'''simple docstring'''
_A = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A , _A , _A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , _snake_case , _snake_case )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_A = training_args.get_process_log_level()
logger.setLevel(_snake_case )
transformers.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
_A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_A = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _snake_case ) and data_args.train_val_split > 0.0:
_A = ds['train'].train_test_split(data_args.train_val_split )
_A = split['train']
_A = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_A = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
_A = ViTMAEConfig.from_pretrained(model_args.config_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_A = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_A = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
_A = ViTMAEForPreTraining(_snake_case )
if training_args.do_train:
_A = ds['train'].column_names
else:
_A = ds['validation'].column_names
if data_args.image_column_name is not None:
_A = data_args.image_column_name
elif "image" in column_names:
_A = 'image'
elif "img" in column_names:
_A = 'img'
else:
_A = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_A = image_processor.size['shortest_edge']
else:
_A = (image_processor.size['height'], image_processor.size['width'])
_A = Compose(
[
Lambda(lambda _snake_case : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_snake_case , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_snake_case : List[Any] ):
_A = [transforms(_snake_case ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
_A = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_snake_case )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
_A = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_snake_case )
# Compute absolute learning rate
_A = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_A = training_args.base_learning_rate * total_train_batch_size / 2_56
# Initialize our trainer
_A = Trainer(
model=_snake_case , args=_snake_case , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_snake_case , data_collator=_snake_case , )
# Training
if training_args.do_train:
_A = None
if training_args.resume_from_checkpoint is not None:
_A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A = last_checkpoint
_A = trainer.train(resume_from_checkpoint=_snake_case )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_A = trainer.evaluate()
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
# Write model card and (optionally) push to hub
_A = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def _snake_case ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
_A = []
for position in positions:
_A , _A = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_snake_case )
return permissible_positions
def _snake_case ( _snake_case : list[list[int]] ) -> bool:
'''simple docstring'''
return not any(elem == 0 for row in board for elem in row )
def _snake_case ( _snake_case : list[list[int]] , _snake_case : tuple[int, int] , _snake_case : int ) -> bool:
'''simple docstring'''
if is_complete(_snake_case ):
return True
for position in get_valid_pos(_snake_case , len(_snake_case ) ):
_A , _A = position
if board[y][x] == 0:
_A = curr + 1
if open_knight_tour_helper(_snake_case , _snake_case , curr + 1 ):
return True
_A = 0
return False
def _snake_case ( _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
for i in range(_snake_case ):
for j in range(_snake_case ):
_A = 1
if open_knight_tour_helper(_snake_case , (i, j) , 1 ):
return board
_A = 0
_A = F'''Open Kight Tour cannot be performed on a board of size {n}'''
raise ValueError(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer names
a = 70 # max width of quantizer names
def _snake_case ( _snake_case : int ) -> List[Any]:
'''simple docstring'''
_A = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=_snake_case , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=_snake_case , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=_snake_case , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=_snake_case , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=_snake_case , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=_snake_case , type=_snake_case , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=_snake_case , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def _snake_case ( _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
if args.calibrator == "max":
_A = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
_A = 'histogram'
elif args.calibrator == "mse":
_A = 'histogram'
else:
raise ValueError(F'''Invalid calibrator {args.calibrator}''' )
_A = QuantDescriptor(num_bits=args.aprec , calib_method=_snake_case )
_A = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(_snake_case )
quant_nn.QuantLinear.set_default_quant_desc_weight(_snake_case )
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Any=False , _snake_case : Union[str, Any]=False ) -> Optional[int]:
'''simple docstring'''
logger.info('Configuring Model for Quantization' )
logger.info(F'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_snake_case , ['embeddings'] , which='weight' , _disabled=_snake_case )
if args.quant_disable:
set_quantizer_by_name(_snake_case , [''] , _disabled=_snake_case )
if args.quant_disable_keyword:
set_quantizer_by_name(_snake_case , args.quant_disable_keyword , _disabled=_snake_case )
if args.quant_disable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_disable_layer_module] , _disabled=_snake_case )
if args.quant_enable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_enable_layer_module] , _disabled=_snake_case )
if args.recalibrate_weights:
recalibrate_weights(_snake_case )
if args.fuse_qkv:
fuse_qkv(_snake_case , _snake_case )
if args.clip_gelu:
clip_gelu(_snake_case , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str ) -> Any:
'''simple docstring'''
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F'''{name:80}: {module}''' )
def _snake_case ( _snake_case : List[Any] , _snake_case : List[Any] ) -> str:
'''simple docstring'''
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
def fusea(_snake_case : int , _snake_case : str , _snake_case : Optional[Any] ):
for mod in [qq, qk, qv]:
if not hasattr(_snake_case , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
_A = qq._amax.detach().item()
_A = qk._amax.detach().item()
_A = qv._amax.detach().item()
_A = max(_snake_case , _snake_case , _snake_case )
qq._amax.fill_(_snake_case )
qk._amax.fill_(_snake_case )
qv._amax.fill_(_snake_case )
logger.info(F''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(F'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def _snake_case ( _snake_case : int , _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
_A = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_snake_case )
_A = mod._input_quantizer._amax.data.detach().item()
logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def _snake_case ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
_A = mod.weight.shape[0]
_A = mod._weight_quantizer._amax.detach()
_A = torch.ones(_snake_case , dtype=amax.dtype , device=amax.device ) * amax
print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def _snake_case ( _snake_case : Dict ) -> Tuple:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
_A = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
_A = set(range(len(mod.weight.size() ) ) ) - axis_set
_A = pytorch_quantization.utils.reduce_amax(mod.weight , axis=_snake_case , keepdims=_snake_case ).detach()
logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
_A = amax
def _snake_case ( _snake_case : Tuple , _snake_case : List[str]=25 , _snake_case : str=1_80 , _snake_case : int=None ) -> List[Any]:
'''simple docstring'''
if ignore is None:
_A = []
elif not isinstance(_snake_case , _snake_case ):
_A = [ignore]
_A = 0
for name, mod in model.named_modules():
if not hasattr(_snake_case , 'weight' ):
continue
_A = max(_snake_case , len(_snake_case ) )
for name, mod in model.named_modules():
_A = getattr(_snake_case , '_input_quantizer' , _snake_case )
_A = getattr(_snake_case , '_weight_quantizer' , _snake_case )
if not hasattr(_snake_case , 'weight' ):
continue
if type(_snake_case ) in ignore:
continue
if [True for s in ignore if type(_snake_case ) is str and s in name]:
continue
_A = F'''Act:{input_q.extra_repr()}'''
_A = F'''Wgt:{weight_q.extra_repr()}'''
_A = F'''{name:{name_width}} {act_str} {wgt_str}'''
if len(_snake_case ) <= line_width:
logger.info(_snake_case )
else:
logger.info(F'''{name:{name_width}} {act_str}''' )
logger.info(F'''{" ":{name_width}} {wgt_str}''' )
def _snake_case ( _snake_case : Dict ) -> int:
'''simple docstring'''
_A = 0
for name, mod in model.named_modules():
if isinstance(_snake_case , pytorch_quantization.nn.TensorQuantizer ):
print(F'''{name:80} {mod}''' )
count += 1
print(F'''{count} TensorQuantizers found in model''' )
def _snake_case ( _snake_case : str , _snake_case : Dict , _snake_case : List[Any] , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
_A = getattr(_snake_case , _snake_case , _snake_case )
if quantizer_mod is not None:
assert hasattr(_snake_case , _snake_case )
setattr(_snake_case , _snake_case , _snake_case )
else:
logger.warning(F'''{name} has no {quantizer}''' )
def _snake_case ( _snake_case : Dict , _snake_case : Optional[int] , _snake_case : str="both" , **_snake_case : List[Any] ) -> str:
'''simple docstring'''
_A = F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(_snake_case , _snake_case , '_input_quantizer' , _snake_case , _snake_case )
if which in ["weight", "both"]:
set_quantizer(_snake_case , _snake_case , '_weight_quantizer' , _snake_case , _snake_case )
logger.info(_snake_case )
def _snake_case ( _snake_case : Any , _snake_case : int , **_snake_case : Dict ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_input_quantizer' ) or hasattr(_snake_case , '_weight_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
set_quantizers(_snake_case , _snake_case , **_snake_case )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
_A = F'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
setattr(_snake_case , _snake_case , _snake_case )
logger.info(_snake_case )
| 315 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def _snake_case ( ) -> Any:
'''simple docstring'''
_A = 'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
_A = Image.open(requests.get(_snake_case , stream=_snake_case ).raw ).convert('RGB' )
return image
def _snake_case ( _snake_case : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
_A = []
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias') )
# fmt: on
return rename_keys
def _snake_case ( _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
_A = dct.pop(_snake_case )
_A = val
def _snake_case ( _snake_case : Any , _snake_case : Any ) -> int:
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
_A = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
_A = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
_A = torch.cat((q_bias, torch.zeros_like(_snake_case , requires_grad=_snake_case ), v_bias) )
_A = qkv_bias
def _snake_case ( _snake_case : Any ) -> Dict:
'''simple docstring'''
_A = 3_64 if 'coco' in model_name else 2_24
_A = InstructBlipVisionConfig(image_size=_snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
_A = TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
_A = TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
_A = LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' , vocab_size=3_20_01 ).to_dict()
elif "vicuna-13b" in model_name:
_A = LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' , vocab_size=3_20_01 ).to_dict()
else:
raise ValueError('Model name not supported' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
_A = InstructBlipQFormerConfig(vocab_size=3_05_23 ).to_dict()
_A = InstructBlipConfig(vision_config=_snake_case , text_config=_snake_case , qformer_config=_snake_case )
return config, image_size
@torch.no_grad()
def _snake_case ( _snake_case : List[str] , _snake_case : Optional[Any]=None , _snake_case : Optional[Any]=False ) -> str:
'''simple docstring'''
_A = AutoTokenizer.from_pretrained('bert-base-uncased' , truncation_side='left' )
qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'} )
if "t5" in model_name:
_A = TaTokenizerFast.from_pretrained('google/flan-t5-xl' , truncation_side='left' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
_A = LlamaTokenizerFast.from_pretrained(
'huggyllama/llama-7b' , truncation_side='left' , bos_token='</s>' , unk_token='</s>' )
tokenizer.add_special_tokens({'pad_token': '[PAD]'} )
_A , _A = get_blipa_config(_snake_case )
_A = InstructBlipForConditionalGeneration(_snake_case ).eval()
_A = {
'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
}
_A , _A = model_name_to_original[model_name]
# load original model
print('Loading original model...' )
_A = 'cuda:1' if torch.cuda.is_available() else 'cpu'
_A = 'cuda:2' if torch.cuda.is_available() else 'cpu'
_A , _A , _A = load_model_and_preprocess(
name=_snake_case , model_type=_snake_case , is_eval=_snake_case , device=_snake_case )
original_model.eval()
print('Done!' )
# update state dict keys
_A = original_model.state_dict()
_A = create_rename_keys(_snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
_A = state_dict.pop(_snake_case )
if key.startswith('Qformer.bert' ):
_A = key.replace('Qformer.bert' , 'qformer' )
if "attention.self" in key:
_A = key.replace('self' , 'attention' )
if "llm_proj" in key:
_A = key.replace('llm_proj' , 'language_projection' )
if "t5_proj" in key:
_A = key.replace('t5_proj' , 'language_projection' )
if key.startswith('llm_model' ):
_A = key.replace('llm_model' , 'language_model' )
if key.startswith('t5' ):
_A = key.replace('t5' , 'language' )
_A = val
# read in qv biases
read_in_q_v_bias(_snake_case , _snake_case )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(_snake_case , strict=_snake_case )
_A = load_demo_image()
_A = 'What is unusual about this image?'
# create processor
_A = BlipImageProcessor(
size={'height': image_size, 'width': image_size} , image_mean=_snake_case , image_std=_snake_case )
_A = InstructBlipProcessor(
image_processor=_snake_case , tokenizer=_snake_case , qformer_tokenizer=_snake_case , )
_A = processor(images=_snake_case , text=_snake_case , return_tensors='pt' ).to(_snake_case )
# make sure processor creates exact same pixel values
_A = vis_processors['eval'](_snake_case ).unsqueeze(0 ).to(_snake_case )
_A = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , _snake_case )
original_model.to(_snake_case )
hf_model.to(_snake_case )
with torch.no_grad():
if "vicuna" in model_name:
_A = original_model({'image': original_pixel_values, 'text_input': [prompt]} ).logits
_A = hf_model(**_snake_case ).logits
else:
_A = original_model(
{'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']} ).logits
_A = tokenizer('\n' , return_tensors='pt' ).input_ids.to(_snake_case )
_A = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -1_00 )
_A = hf_model(**_snake_case , labels=_snake_case ).logits
print('First values of original logits:' , original_logits[0, :3, :3] )
print('First values of HF logits:' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
_A = 1E-4 if 'vicuna' in model_name else 1E-5
assert torch.allclose(original_logits.to(logits.device ) , _snake_case , atol=_snake_case )
print('Looks ok!' )
print('Generating with original model...' )
_A = original_model.generate({'image': original_pixel_values, 'prompt': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('Generating with HF model...' )
_A = hf_model.generate(
**_snake_case , do_sample=_snake_case , num_beams=5 , max_length=2_56 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
_A = 2
print('Original generation:' , _snake_case )
_A = processor.batch_decode(_snake_case , skip_special_tokens=_snake_case )
_A = [text.strip() for text in output_text]
print('HF generation:' , _snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_snake_case )
hf_model.save_pretrained(_snake_case )
if push_to_hub:
processor.push_to_hub(F'''Salesforce/{model_name}''' )
hf_model.push_to_hub(F'''Salesforce/{model_name}''' )
if __name__ == "__main__":
a = argparse.ArgumentParser()
a = [
'''instructblip-vicuna-7b''',
'''instructblip-vicuna-13b''',
'''instructblip-flan-t5-xl''',
'''instructblip-flan-t5-xxl''',
]
parser.add_argument(
'''--model_name''',
default='''instructblip-flan-t5-xl''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
a = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'] , )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int]=False ):
_A = spearmanr(_UpperCAmelCase , _UpperCAmelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 315 | 1 |
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _snake_case ( _snake_case : str ) -> Tuple:
'''simple docstring'''
_A = int(_snake_case )
_A , _A , _A = t // 36_00, (t // 60) % 60, t % 60
return F'''{h}:{m:02d}:{s:02d}''' if h != 0 else F'''{m:02d}:{s:02d}'''
def _snake_case ( _snake_case : Optional[Any] , _snake_case : Tuple , _snake_case : Tuple , _snake_case : int , _snake_case : Optional[int]=3_00 ) -> Tuple:
'''simple docstring'''
return F'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def _snake_case ( _snake_case : str ) -> Optional[int]:
'''simple docstring'''
_A = '<table border="1" class="dataframe">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += F''' <th>{i}</th>\n'''
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
_A = F'''{elt:.6f}''' if isinstance(_snake_case , _snake_case ) else str(_snake_case )
html_code += F''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Dict = 5
UpperCAmelCase : int = 0.2
def __init__( self : int , _UpperCAmelCase : int , _UpperCAmelCase : Optional[str] = None , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional["NotebookTrainingTracker"] = None , _UpperCAmelCase : int = 300 , ):
_A = total
_A = '' if prefix is None else prefix
_A = leave
_A = parent
_A = width
_A = None
_A = None
_A = None
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : int , _UpperCAmelCase : bool = False , _UpperCAmelCase : str = None ):
_A = value
if comment is not None:
_A = comment
if self.last_value is None:
_A = _A = time.time()
_A = _A = value
_A = _A = None
_A = self.warmup
_A = 1
self.update_bar(_UpperCAmelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
_A = time.time()
_A = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
_A = self.elapsed_time / (value - self.start_value)
else:
_A = None
if value >= self.total:
_A = self.total
_A = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
_A = self.average_time_per_item * (self.total - value)
self.update_bar(_UpperCAmelCase )
_A = value
_A = current_time
if self.average_time_per_item is None:
_A = 1
else:
_A = max(int(self.update_every / self.average_time_per_item ) , 1 )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict=None ):
_A = ' ' * (len(str(self.total ) ) - len(str(_UpperCAmelCase ) )) + str(_UpperCAmelCase )
if self.elapsed_time is None:
_A = F'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
_A = F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
_A = (
F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'''
F''' {format_time(self.predicted_remaining )}'''
)
self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F''', {self.comment}]'''
self.display()
def lowerCAmelCase_ ( self : List[str] ):
_A = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
_A = disp.display(disp.HTML(self.html_code ) , display_id=_UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowerCAmelCase_ ( self : List[Any] ):
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any]=None ):
super().__init__(_UpperCAmelCase )
_A = None if column_names is None else [column_names]
_A = None
def lowerCAmelCase_ ( self : List[str] ):
_A = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
_A = disp.display(disp.HTML(self.html_code ) , display_id=_UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : str ):
if self.inner_table is None:
_A = [list(values.keys() ), list(values.values() )]
else:
_A = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(_UpperCAmelCase )
_A = columns
self.inner_table.append([values[c] for c in columns] )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any=None , _UpperCAmelCase : Optional[int]=300 ):
_A = NotebookProgressBar(_UpperCAmelCase , prefix=_UpperCAmelCase , parent=self , width=_UpperCAmelCase )
return self.child_bar
def lowerCAmelCase_ ( self : Optional[int] ):
_A = None
self.display()
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] ):
_A = None
_A = None
_A = False
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple , **_UpperCAmelCase : Union[str, Any] ):
_A = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
_A = 0
_A = 0
_A = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
_A = NotebookTrainingTracker(state.max_steps , _UpperCAmelCase )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : Any ):
_A = int(state.epoch ) if int(state.epoch ) == state.epoch else F'''{state.epoch:.2f}'''
self.training_tracker.update(
state.global_step + 1 , comment=F'''Epoch {epoch}/{state.num_train_epochs}''' , force_update=self._force_next_update , )
_A = False
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any]=None , **_UpperCAmelCase : Optional[Any] ):
if not has_length(_UpperCAmelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
_A = self.training_tracker.add_child(len(_UpperCAmelCase ) )
else:
_A = NotebookProgressBar(len(_UpperCAmelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , **_UpperCAmelCase : Any ):
if self.prediction_bar is not None:
self.prediction_bar.close()
_A = None
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : str=None , **_UpperCAmelCase : Tuple ):
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
_A = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
_A = state.global_step
self.training_tracker.write_line(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any]=None , **_UpperCAmelCase : Union[str, Any] ):
if self.training_tracker is not None:
_A = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
_A = log['loss']
break
if self.first_column == "Epoch":
_A = int(state.epoch )
else:
_A = state.global_step
_A = 'eval'
for k in metrics:
if k.endswith('_loss' ):
_A = re.sub(r'\_loss$' , '' , _UpperCAmelCase )
_A = metrics.pop('total_flos' , _UpperCAmelCase )
_A = metrics.pop('epoch' , _UpperCAmelCase )
_A = metrics.pop(F'''{metric_key_prefix}_runtime''' , _UpperCAmelCase )
_A = metrics.pop(F'''{metric_key_prefix}_samples_per_second''' , _UpperCAmelCase )
_A = metrics.pop(F'''{metric_key_prefix}_steps_per_second''' , _UpperCAmelCase )
_A = metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''' , _UpperCAmelCase )
for k, v in metrics.items():
if k == F'''{metric_key_prefix}_loss''':
_A = v
else:
_A = k.split('_' )
_A = ' '.join([part.capitalize() for part in splits[1:]] )
_A = v
self.training_tracker.write_line(_UpperCAmelCase )
self.training_tracker.remove_child()
_A = None
# Evaluation takes a long time so we should force the next update.
_A = True
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , **_UpperCAmelCase : int ):
self.training_tracker.update(
state.global_step , comment=F'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=_UpperCAmelCase )
_A = None
| 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A = b
if function(_snake_case ) == 0: # one of the a or b is a root for the function
return a
elif function(_snake_case ) == 0:
return b
elif (
function(_snake_case ) * function(_snake_case ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
_A = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(_snake_case ) == 0:
return mid
elif function(_snake_case ) * function(_snake_case ) < 0:
_A = mid
else:
_A = mid
_A = start + (end - start) / 2.0
return mid
def _snake_case ( _snake_case : float ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
a = logging.get_logger(__name__)
def _snake_case ( _snake_case : np.ndarray , _snake_case : Union[int, Iterable[int]] , _snake_case : bool , _snake_case : int ) -> Tuple[int, int]:
'''simple docstring'''
def constraint_to_multiple_of(_snake_case : int , _snake_case : Tuple , _snake_case : Dict=0 , _snake_case : Union[str, Any]=None ):
_A = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_A = math.floor(val / multiple ) * multiple
if x < min_val:
_A = math.ceil(val / multiple ) * multiple
return x
_A = (output_size, output_size) if isinstance(_snake_case , _snake_case ) else output_size
_A , _A = get_image_size(_snake_case )
_A , _A = output_size
# determine new height and width
_A = output_height / input_height
_A = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_A = scale_width
else:
# fit height
_A = scale_height
_A = constraint_to_multiple_of(scale_height * input_height , multiple=_snake_case )
_A = constraint_to_multiple_of(scale_width * input_width , multiple=_snake_case )
return (new_height, new_width)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Any = ['''pixel_values''']
def __init__( self : str , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 1 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[int, float] = 1 / 255 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , **_UpperCAmelCase : Union[str, Any] , ):
super().__init__(**_UpperCAmelCase )
_A = size if size is not None else {'height': 384, 'width': 384}
_A = get_size_dict(_UpperCAmelCase )
_A = do_resize
_A = size
_A = keep_aspect_ratio
_A = ensure_multiple_of
_A = resample
_A = do_rescale
_A = rescale_factor
_A = do_normalize
_A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 1 , _UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : List[Any] , ):
_A = get_size_dict(_UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''' )
_A = get_resize_output_image_size(
_UpperCAmelCase , output_size=(size['height'], size['width']) , keep_aspect_ratio=_UpperCAmelCase , multiple=_UpperCAmelCase , )
return resize(_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[int, float] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Any , ):
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : List[str] , ):
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : ImageInput , _UpperCAmelCase : bool = None , _UpperCAmelCase : int = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : int = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : float = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCAmelCase : Any , ):
_A = do_resize if do_resize is not None else self.do_resize
_A = size if size is not None else self.size
_A = get_size_dict(_UpperCAmelCase )
_A = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_A = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_A = resample if resample is not None else self.resample
_A = do_rescale if do_rescale is not None else self.do_rescale
_A = rescale_factor if rescale_factor is not None else self.rescale_factor
_A = do_normalize if do_normalize is not None else self.do_normalize
_A = image_mean if image_mean is not None else self.image_mean
_A = image_std if image_std is not None else self.image_std
_A = make_list_of_images(_UpperCAmelCase )
if not valid_images(_UpperCAmelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
_A = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
_A = [self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase ) for image in images]
if do_rescale:
_A = [self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) for image in images]
if do_normalize:
_A = [self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) for image in images]
_A = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
_A = {'pixel_values': images}
return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Tuple] = None ):
_A = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_UpperCAmelCase ):
_A = target_sizes.numpy()
_A = []
for idx in range(len(_UpperCAmelCase ) ):
_A = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_UpperCAmelCase )
_A = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_UpperCAmelCase )
else:
_A = logits.argmax(dim=1 )
_A = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmelCase ) , '''Tatoeba directory does not exist.''' )
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Optional[int] ):
self.resolver.convert_models(['heb-eng'] )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A , _A = self.resolver.write_model_card('opus-mt-he-en' , dry_run=_UpperCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 315 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any]=13 , _UpperCAmelCase : int=7 , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Optional[int]=True , _UpperCAmelCase : Any=True , _UpperCAmelCase : int=99 , _UpperCAmelCase : List[Any]=32 , _UpperCAmelCase : List[Any]=5 , _UpperCAmelCase : Optional[int]=4 , _UpperCAmelCase : List[Any]=37 , _UpperCAmelCase : List[str]="gelu" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : Optional[int]=512 , _UpperCAmelCase : Any=16 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : Any=4 , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_attention_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_choices
def lowerCAmelCase_ ( self : Dict ):
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_attention_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = RobertaPreLayerNormConfig(
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=_UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCAmelCase_ ( self : Optional[int] ):
_A = self.prepare_config_and_inputs()
_A , _A , _A , _A = config_and_inputs
_A = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowerCAmelCase_ ( self : int ):
_A = self.prepare_config_and_inputs()
_A , _A , _A , _A = config_and_inputs
_A = True
_A = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_A = 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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = True
UpperCAmelCase : Dict = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase_ ( self : Tuple ):
_A = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
for model_class_name in self.all_model_classes:
_A = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=_UpperCAmelCase )
_A = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCAmelCase )
@require_flax
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase_ ( self : str ):
_A = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=_UpperCAmelCase )
_A = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
_A = model(_UpperCAmelCase )[0]
_A = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , _UpperCAmelCase )
# compare the actual values for a slice.
_A = np.array(
[[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=_UpperCAmelCase )
_A = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
_A = model(_UpperCAmelCase )[0]
# compare the actual values for a slice.
_A = np.array(
[[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
| 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
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 .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 315 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _snake_case ( ) -> Optional[int]:
'''simple docstring'''
_A = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
_A = parser.add_subparsers(help='diffusers-cli command helpers' )
# Register commands
EnvironmentCommand.register_subcommand(_snake_case )
# Let's go
_A = parser.parse_args()
if not hasattr(_snake_case , 'func' ):
parser.print_help()
exit(1 )
# Run
_A = args.func(_snake_case )
service.run()
if __name__ == "__main__":
main()
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
_A = []
for position in positions:
_A , _A = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_snake_case )
return permissible_positions
def _snake_case ( _snake_case : list[list[int]] ) -> bool:
'''simple docstring'''
return not any(elem == 0 for row in board for elem in row )
def _snake_case ( _snake_case : list[list[int]] , _snake_case : tuple[int, int] , _snake_case : int ) -> bool:
'''simple docstring'''
if is_complete(_snake_case ):
return True
for position in get_valid_pos(_snake_case , len(_snake_case ) ):
_A , _A = position
if board[y][x] == 0:
_A = curr + 1
if open_knight_tour_helper(_snake_case , _snake_case , curr + 1 ):
return True
_A = 0
return False
def _snake_case ( _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
for i in range(_snake_case ):
for j in range(_snake_case ):
_A = 1
if open_knight_tour_helper(_snake_case , (i, j) , 1 ):
return board
_A = 0
_A = F'''Open Kight Tour cannot be performed on a board of size {n}'''
raise ValueError(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _snake_case ( _snake_case : str , _snake_case : str ) -> str | Literal[False]:
'''simple docstring'''
_A = list(_snake_case )
_A = list(_snake_case )
_A = 0
for i in range(len(_snake_case ) ):
if lista[i] != lista[i]:
count += 1
_A = '_'
if count > 1:
return False
else:
return "".join(_snake_case )
def _snake_case ( _snake_case : list[str] ) -> list[str]:
'''simple docstring'''
_A = []
while True:
_A = ['$'] * len(_snake_case )
_A = []
for i in range(len(_snake_case ) ):
for j in range(i + 1 , len(_snake_case ) ):
_A = compare_string(binary[i] , binary[j] )
if k is False:
_A = '*'
_A = '*'
temp.append('X' )
for i in range(len(_snake_case ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(_snake_case ) == 0:
return pi
_A = list(set(_snake_case ) )
def _snake_case ( _snake_case : int , _snake_case : Sequence[float] ) -> list[str]:
'''simple docstring'''
_A = []
for minterm in minterms:
_A = ''
for _ in range(_snake_case ):
_A = str(minterm % 2 ) + string
minterm //= 2
temp.append(_snake_case )
return temp
def _snake_case ( _snake_case : str , _snake_case : str , _snake_case : int ) -> bool:
'''simple docstring'''
_A = list(_snake_case )
_A = list(_snake_case )
_A = 0
for i in range(len(_snake_case ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def _snake_case ( _snake_case : list[list[int]] , _snake_case : list[str] ) -> list[str]:
'''simple docstring'''
_A = []
_A = [0] * len(_snake_case )
for i in range(len(chart[0] ) ):
_A = 0
_A = -1
for j in range(len(_snake_case ) ):
if chart[j][i] == 1:
count += 1
_A = j
if count == 1:
_A = 1
for i in range(len(_snake_case ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(_snake_case ) ):
_A = 0
temp.append(prime_implicants[i] )
while True:
_A = 0
_A = -1
_A = 0
for i in range(len(_snake_case ) ):
_A = chart[i].count(1 )
if count_n > max_n:
_A = count_n
_A = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(_snake_case ) ):
_A = 0
def _snake_case ( _snake_case : list[str] , _snake_case : list[str] ) -> list[list[int]]:
'''simple docstring'''
_A = [[0 for x in range(len(_snake_case ) )] for x in range(len(_snake_case ) )]
for i in range(len(_snake_case ) ):
_A = prime_implicants[i].count('_' )
for j in range(len(_snake_case ) ):
if is_for_table(prime_implicants[i] , binary[j] , _snake_case ):
_A = 1
return chart
def _snake_case ( ) -> None:
'''simple docstring'''
_A = int(input('Enter the no. of variables\n' ) )
_A = [
float(_snake_case )
for x in input(
'Enter the decimal representation of Minterms \'Spaces Separated\'\n' ).split()
]
_A = decimal_to_binary(_snake_case , _snake_case )
_A = check(_snake_case )
print('Prime Implicants are:' )
print(_snake_case )
_A = prime_implicant_chart(_snake_case , _snake_case )
_A = selection(_snake_case , _snake_case )
print('Essential Prime Implicants are:' )
print(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 315 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , *_UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str=None , **_UpperCAmelCase : List[Any] ):
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
_A = eval_examples
_A = post_process_function
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : str = "eval" ):
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(_UpperCAmelCase )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
else:
_A = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_UpperCAmelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , _UpperCAmelCase )
return metrics
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str = "test" ):
_A = self.get_test_dataloader(_UpperCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions , 'predict' )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def _snake_case ( _snake_case : Optional[Any] , _snake_case : Tuple , _snake_case : Dict , _snake_case : Union[str, Any]=None , _snake_case : str=None , _snake_case : Optional[Any]=None , _snake_case : Tuple=None , _snake_case : Dict=None , ) -> str:
'''simple docstring'''
if attention_mask is None:
_A = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_A = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_A = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=_snake_case )
if decoder_head_mask is None:
_A = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_snake_case )
if cross_attn_head_mask is None:
_A = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_snake_case )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : Optional[Any]=99 , _UpperCAmelCase : Optional[int]=16 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Dict=4 , _UpperCAmelCase : List[Any]=4 , _UpperCAmelCase : List[Any]="relu" , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : List[Any]=0.0 , _UpperCAmelCase : Union[str, Any]=0.0 , _UpperCAmelCase : List[str]=20 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : Union[str, Any]=1 , _UpperCAmelCase : str=0 , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = encoder_layerdrop
_A = decoder_layerdrop
_A = max_position_embeddings
_A = eos_token_id
_A = pad_token_id
_A = bos_token_id
def lowerCAmelCase_ ( self : Dict ):
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = self.eos_token_id # Eos Token
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_A = input_ids.clamp(self.pad_token_id + 1 )
_A = decoder_input_ids.clamp(self.pad_token_id + 1 )
_A = self.get_config()
_A = prepare_mam_aaa_inputs_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return config, inputs_dict
def lowerCAmelCase_ ( self : Dict ):
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def lowerCAmelCase_ ( self : List[str] ):
_A , _A = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str , _UpperCAmelCase : Any ):
_A = MaMaaaModel(config=_UpperCAmelCase ).get_decoder().to(_UpperCAmelCase ).eval()
_A = inputs_dict['input_ids']
_A = inputs_dict['attention_mask']
_A = inputs_dict['head_mask']
# first forward pass
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , head_mask=_UpperCAmelCase , use_cache=_UpperCAmelCase )
_A , _A = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_A = ids_tensor((self.batch_size, 3) , config.vocab_size )
_A = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_A = torch.cat([input_ids, next_tokens] , dim=-1 )
_A = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )['last_hidden_state']
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , past_key_values=_UpperCAmelCase )[
'last_hidden_state'
]
# select random slice
_A = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_A = output_from_no_past[:, -3:, random_slice_idx].detach()
_A = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-2 ) )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ):
_A = MaMaaaModel(config=_UpperCAmelCase ).to(_UpperCAmelCase ).eval()
_A = model(**_UpperCAmelCase )
_A = outputs.encoder_last_hidden_state
_A = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_A = model.get_encoder()
encoder.save_pretrained(_UpperCAmelCase )
_A = MaMaaaEncoder.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
_A = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
_A = model.get_decoder()
decoder.save_pretrained(_UpperCAmelCase )
_A = MaMaaaDecoder.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
_A = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
UpperCAmelCase : Dict = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
UpperCAmelCase : int = (
{
'''conversational''': MaMaaaForConditionalGeneration,
'''feature-extraction''': MaMaaaModel,
'''summarization''': MaMaaaForConditionalGeneration,
'''text2text-generation''': MaMaaaForConditionalGeneration,
'''translation''': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
UpperCAmelCase : int = True
UpperCAmelCase : List[Any] = True
UpperCAmelCase : int = False
UpperCAmelCase : Dict = False
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def lowerCAmelCase_ ( self : Tuple ):
_A = MaMaaaModelTester(self )
_A = ConfigTester(self , config_class=_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : Optional[int] ):
_A , _A = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_A = model_class(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_UpperCAmelCase )
_A , _A = model_class.from_pretrained(_UpperCAmelCase , output_loading_info=_UpperCAmelCase )
self.assertEqual(info['missing_keys'] , [] )
def lowerCAmelCase_ ( self : str ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
_A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
_A = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = copy.deepcopy(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
if not self.is_encoder_decoder:
_A = inputs['input_ids']
del inputs["input_ids"]
else:
_A = inputs['input_ids']
_A = inputs.get('decoder_input_ids' , _UpperCAmelCase )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , _UpperCAmelCase )
_A = model.get_input_embeddings()
if not self.is_encoder_decoder:
_A = wte(_UpperCAmelCase )
else:
_A = wte(_UpperCAmelCase )
_A = wte(_UpperCAmelCase )
with torch.no_grad():
model(**_UpperCAmelCase )[0]
def lowerCAmelCase_ ( self : Tuple ):
_A , _A = self.model_tester.prepare_config_and_inputs()
_A = input_dict['input_ids']
_A = input_ids.ne(1 ).to(_UpperCAmelCase )
_A = MaMaaaForConditionalGeneration(_UpperCAmelCase ).eval().to(_UpperCAmelCase )
if torch_device == "cuda":
model.half()
model.generate(_UpperCAmelCase , attention_mask=_UpperCAmelCase )
model.generate(num_beams=4 , do_sample=_UpperCAmelCase , early_stopping=_UpperCAmelCase , num_return_sequences=3 )
def _snake_case ( _snake_case : Optional[Any] ) -> str:
'''simple docstring'''
return torch.tensor(_snake_case , dtype=torch.long , device=_snake_case )
a = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : Dict ):
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def lowerCAmelCase_ ( self : List[Any] ):
_A = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(_UpperCAmelCase )
_A = _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
_A = _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
_A = prepare_mam_aaa_inputs_dict(model.config , _UpperCAmelCase , _UpperCAmelCase )
with torch.no_grad():
_A = model(**_UpperCAmelCase )[0]
_A = torch.Size((1, 11, 1_024) )
self.assertEqual(output.shape , _UpperCAmelCase )
# change to expected output here
_A = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=_UpperCAmelCase )
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[str] ):
_A = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_UpperCAmelCase )
# change to intended input
_A = _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
_A = _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
_A = prepare_mam_aaa_inputs_dict(model.config , _UpperCAmelCase , _UpperCAmelCase )
with torch.no_grad():
_A = model(**_UpperCAmelCase )[0]
_A = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , _UpperCAmelCase )
# change to expected output here
_A = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=_UpperCAmelCase )
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_UpperCAmelCase )
_A = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
_A = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
_A = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='pt' )
_A = model.generate(
input_ids=dct['input_ids'].to(_UpperCAmelCase ) , attention_mask=dct['attention_mask'].to(_UpperCAmelCase ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
_A = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
_A = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
assert generated == expected_en
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLMRobertaXLOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'''XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMRobertaXLForCausalLM''',
'''XLMRobertaXLForMaskedLM''',
'''XLMRobertaXLForMultipleChoice''',
'''XLMRobertaXLForQuestionAnswering''',
'''XLMRobertaXLForSequenceClassification''',
'''XLMRobertaXLForTokenClassification''',
'''XLMRobertaXLModel''',
'''XLMRobertaXLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
a = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_A = Vector()
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(_UpperCAmelCase ) , '(0,0,0,0,0,1)' )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = Vector([1, 2, 3, 4] )
self.assertEqual(len(_UpperCAmelCase ) , 4 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2] )
_A = Vector([1, 2, 3, 4, 5] )
_A = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_A = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCAmelCase_ ( self : str ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
_A = Vector([2, -1, 4] ) # for test of dot product
_A = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , '(3.0,6.0,9.0)' )
self.assertEqual((a * b) , 0 )
def lowerCAmelCase_ ( self : Dict ):
self.assertEqual(str(zero_vector(10 ) ).count('0' ) , 10 )
def lowerCAmelCase_ ( self : Tuple ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , _UpperCAmelCase , _UpperCAmelCase ) ) , '(3,4,7)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 0, 0, 0, 0, 0] )
_A = x.copy()
self.assertEqual(str(_UpperCAmelCase ) , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(_UpperCAmelCase ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : str ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_A = Vector([1, 2, 3] )
self.assertEqual('(14,32,50)' , str(a * x ) )
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' , str(a * 2 ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' , str(a + b ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' , str(a - b ) )
def lowerCAmelCase_ ( self : int ):
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 315 | 1 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
a = json.load(f)
@require_torch
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Dict ):
return FSMTTokenizer.from_pretrained(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] ):
_A = FSMTForConditionalGeneration.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['en-ru', 26.0],
['ru-en', 22.0],
['en-de', 22.0],
['de-en', 29.0],
] )
@slow
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
_A = F'''facebook/wmt19-{pair}'''
_A = self.get_tokenizer(_UpperCAmelCase )
_A = self.get_model(_UpperCAmelCase )
_A = bleu_data[pair]['src']
_A = bleu_data[pair]['tgt']
_A = tokenizer(_UpperCAmelCase , return_tensors='pt' , truncation=_UpperCAmelCase , padding='longest' ).to(_UpperCAmelCase )
_A = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
_A = tokenizer.batch_decode(
_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase )
_A = calculate_bleu(_UpperCAmelCase , _UpperCAmelCase )
print(_UpperCAmelCase )
self.assertGreaterEqual(scores['bleu'] , _UpperCAmelCase )
| 315 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''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 lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = '''xlnet'''
UpperCAmelCase : List[Any] = ['''mems''']
UpperCAmelCase : Any = {
'''n_token''': '''vocab_size''', # Backward compatibility
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Union[str, Any] , _UpperCAmelCase : Dict=32_000 , _UpperCAmelCase : List[str]=1_024 , _UpperCAmelCase : Any=24 , _UpperCAmelCase : Union[str, Any]=16 , _UpperCAmelCase : Union[str, Any]=4_096 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Any=True , _UpperCAmelCase : str="bi" , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Optional[Any]=1E-1_2 , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : Any=512 , _UpperCAmelCase : Dict=None , _UpperCAmelCase : int=True , _UpperCAmelCase : int=False , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : int=-1 , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : Union[str, Any]="last" , _UpperCAmelCase : int=True , _UpperCAmelCase : str="tanh" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Dict=5 , _UpperCAmelCase : Optional[Any]=5 , _UpperCAmelCase : Union[str, Any]=5 , _UpperCAmelCase : List[str]=1 , _UpperCAmelCase : Dict=2 , **_UpperCAmelCase : int , ):
_A = vocab_size
_A = d_model
_A = n_layer
_A = 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})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = 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.' , _UpperCAmelCase , )
_A = kwargs['use_cache']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : Tuple ):
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 : Optional[Any] , _UpperCAmelCase : Optional[Any] ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 315 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( _snake_case : int , _snake_case : List[str] , _snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
if gpta_config_file == "":
_A = GPTaConfig()
else:
_A = GPTaConfig.from_json_file(_snake_case )
_A = GPTaModel(_snake_case )
# Load weights from numpy
load_tf_weights_in_gpta(_snake_case , _snake_case , _snake_case )
# Save pytorch-model
_A = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
_A = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(F'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(model.state_dict() , _snake_case )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(_snake_case , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--gpt2_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
'''This specifies the model architecture.'''
),
)
a = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 315 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
a = {
'''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
'''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def _snake_case ( _snake_case : Tuple ) -> Dict:
'''simple docstring'''
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def _snake_case ( _snake_case : str , _snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
if args.student_type == "roberta":
_A = False
elif args.student_type == "gpt2":
_A = False
def _snake_case ( _snake_case : str , _snake_case : int ) -> Tuple:
'''simple docstring'''
if args.student_type == "roberta":
_A = False
def _snake_case ( ) -> Tuple:
'''simple docstring'''
_A = argparse.ArgumentParser(description='Training' )
parser.add_argument('--force' , action='store_true' , help='Overwrite dump_path if it already exists.' )
parser.add_argument(
'--dump_path' , type=_snake_case , required=_snake_case , help='The output directory (log, checkpoints, parameters, etc.)' )
parser.add_argument(
'--data_file' , type=_snake_case , required=_snake_case , help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.' , )
parser.add_argument(
'--student_type' , type=_snake_case , choices=['distilbert', 'roberta', 'gpt2'] , required=_snake_case , help='The student type (DistilBERT, RoBERTa).' , )
parser.add_argument('--student_config' , type=_snake_case , required=_snake_case , help='Path to the student configuration.' )
parser.add_argument(
'--student_pretrained_weights' , default=_snake_case , type=_snake_case , help='Load student initialization checkpoint.' )
parser.add_argument(
'--teacher_type' , choices=['bert', 'roberta', 'gpt2'] , required=_snake_case , help='Teacher type (BERT, RoBERTa).' )
parser.add_argument('--teacher_name' , type=_snake_case , required=_snake_case , help='The teacher model.' )
parser.add_argument('--temperature' , default=2.0 , type=_snake_case , help='Temperature for the softmax temperature.' )
parser.add_argument(
'--alpha_ce' , default=0.5 , type=_snake_case , help='Linear weight for the distillation loss. Must be >=0.' )
parser.add_argument(
'--alpha_mlm' , default=0.0 , type=_snake_case , help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.' , )
parser.add_argument('--alpha_clm' , default=0.5 , type=_snake_case , help='Linear weight for the CLM loss. Must be >=0.' )
parser.add_argument('--alpha_mse' , default=0.0 , type=_snake_case , help='Linear weight of the MSE loss. Must be >=0.' )
parser.add_argument(
'--alpha_cos' , default=0.0 , type=_snake_case , help='Linear weight of the cosine embedding loss. Must be >=0.' )
parser.add_argument(
'--mlm' , action='store_true' , help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.' )
parser.add_argument(
'--mlm_mask_prop' , default=0.15 , type=_snake_case , help='Proportion of tokens for which we need to make a prediction.' , )
parser.add_argument('--word_mask' , default=0.8 , type=_snake_case , help='Proportion of tokens to mask out.' )
parser.add_argument('--word_keep' , default=0.1 , type=_snake_case , help='Proportion of tokens to keep.' )
parser.add_argument('--word_rand' , default=0.1 , type=_snake_case , help='Proportion of tokens to randomly replace.' )
parser.add_argument(
'--mlm_smoothing' , default=0.7 , type=_snake_case , help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).' , )
parser.add_argument('--token_counts' , type=_snake_case , help='The token counts in the data_file for MLM.' )
parser.add_argument(
'--restrict_ce_to_mask' , action='store_true' , help='If true, compute the distillation loss only the [MLM] prediction distribution.' , )
parser.add_argument(
'--freeze_pos_embs' , action='store_true' , help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.' , )
parser.add_argument(
'--freeze_token_type_embds' , action='store_true' , help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.' , )
parser.add_argument('--n_epoch' , type=_snake_case , default=3 , help='Number of pass on the whole dataset.' )
parser.add_argument('--batch_size' , type=_snake_case , default=5 , help='Batch size (for each process).' )
parser.add_argument(
'--group_by_size' , action='store_false' , help='If true, group sequences that have similar length into the same batch. Default is true.' , )
parser.add_argument(
'--gradient_accumulation_steps' , type=_snake_case , default=50 , help='Gradient accumulation for larger training batches.' , )
parser.add_argument('--warmup_prop' , default=0.05 , type=_snake_case , help='Linear warmup proportion.' )
parser.add_argument('--weight_decay' , default=0.0 , type=_snake_case , help='Weight decay if we apply some.' )
parser.add_argument('--learning_rate' , default=5E-4 , type=_snake_case , help='The initial learning rate for Adam.' )
parser.add_argument('--adam_epsilon' , default=1E-6 , type=_snake_case , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , default=5.0 , type=_snake_case , help='Max gradient norm.' )
parser.add_argument('--initializer_range' , default=0.02 , type=_snake_case , help='Random initialization range.' )
parser.add_argument(
'--fp16' , action='store_true' , help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit' , )
parser.add_argument(
'--fp16_opt_level' , type=_snake_case , default='O1' , help=(
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
'See details at https://nvidia.github.io/apex/amp.html'
) , )
parser.add_argument('--n_gpu' , type=_snake_case , default=1 , help='Number of GPUs in the node.' )
parser.add_argument('--local_rank' , type=_snake_case , default=-1 , help='Distributed training - Local rank' )
parser.add_argument('--seed' , type=_snake_case , default=56 , help='Random seed' )
parser.add_argument('--log_interval' , type=_snake_case , default=5_00 , help='Tensorboard logging interval.' )
parser.add_argument('--checkpoint_interval' , type=_snake_case , default=40_00 , help='Checkpoint interval.' )
_A = parser.parse_args()
sanity_checks(_snake_case )
# ARGS #
init_gpu_params(_snake_case )
set_seed(_snake_case )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
' itUse `--force` if you want to overwrite it' )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(F'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(F'''Param: {args}''' )
with open(os.path.join(args.dump_path , 'parameters.json' ) , 'w' ) as f:
json.dump(vars(_snake_case ) , _snake_case , indent=4 )
git_log(args.dump_path )
_A , _A , _A = MODEL_CLASSES[args.student_type]
_A , _A , _A = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
_A = teacher_tokenizer_class.from_pretrained(args.teacher_name )
_A = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
_A = tokenizer.all_special_tokens.index(_snake_case )
_A = tokenizer.all_special_ids[idx]
logger.info(F'''Special tokens {special_tok_ids}''' )
_A = special_tok_ids
_A = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(F'''Loading data from {args.data_file}''' )
with open(args.data_file , 'rb' ) as fp:
_A = pickle.load(_snake_case )
if args.mlm:
logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , 'rb' ) as fp:
_A = pickle.load(_snake_case )
_A = np.maximum(_snake_case , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
_A = 0.0 # do not predict special tokens
_A = torch.from_numpy(_snake_case )
else:
_A = None
_A = LmSeqsDataset(params=_snake_case , data=_snake_case )
logger.info('Data loader created.' )
# STUDENT #
logger.info(F'''Loading student config from {args.student_config}''' )
_A = student_config_class.from_pretrained(args.student_config )
_A = True
if args.student_pretrained_weights is not None:
logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
_A = student_model_class.from_pretrained(args.student_pretrained_weights , config=_snake_case )
else:
_A = student_model_class(_snake_case )
if args.n_gpu > 0:
student.to(F'''cuda:{args.local_rank}''' )
logger.info('Student loaded.' )
# TEACHER #
_A = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=_snake_case )
if args.n_gpu > 0:
teacher.to(F'''cuda:{args.local_rank}''' )
logger.info(F'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(_snake_case , _snake_case )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(_snake_case , _snake_case )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
_A = Distiller(
params=_snake_case , dataset=_snake_case , token_probs=_snake_case , student=_snake_case , teacher=_snake_case )
distiller.train()
logger.info('Let\'s go get some drinks.' )
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
a = random.Random()
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : Optional[int]=1.0 , _snake_case : List[str]=None , _snake_case : str=None ) -> List[Any]:
'''simple docstring'''
if rng is None:
_A = global_rng
_A = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : List[Any]=7 , _UpperCAmelCase : Optional[int]=400 , _UpperCAmelCase : Tuple=2_000 , _UpperCAmelCase : List[Any]=10 , _UpperCAmelCase : Optional[int]=160 , _UpperCAmelCase : Optional[Any]=8 , _UpperCAmelCase : int=0.0 , _UpperCAmelCase : Optional[int]=4_000 , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Optional[int]=True , ):
_A = parent
_A = batch_size
_A = min_seq_length
_A = max_seq_length
_A = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_A = padding_value
_A = sampling_rate
_A = return_attention_mask
_A = do_normalize
_A = feature_size
_A = chunk_length
_A = hop_length
def lowerCAmelCase_ ( self : Tuple ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : List[str]=False ):
def _flatten(_UpperCAmelCase : str ):
return list(itertools.chain(*_UpperCAmelCase ) )
if equal_length:
_A = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_A = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_A = [np.asarray(_UpperCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = WhisperFeatureExtractor if is_speech_available() else None
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = WhisperFeatureExtractionTester(self )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_A = feat_extract_first.save_pretrained(_UpperCAmelCase )[0]
check_json_file_has_correct_format(_UpperCAmelCase )
_A = self.feature_extraction_class.from_pretrained(_UpperCAmelCase )
_A = feat_extract_first.to_dict()
_A = feat_extract_second.to_dict()
_A = feat_extract_first.mel_filters
_A = feat_extract_second.mel_filters
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_A = os.path.join(_UpperCAmelCase , 'feat_extract.json' )
feat_extract_first.to_json_file(_UpperCAmelCase )
_A = self.feature_extraction_class.from_json_file(_UpperCAmelCase )
_A = feat_extract_first.to_dict()
_A = feat_extract_second.to_dict()
_A = feat_extract_first.mel_filters
_A = feat_extract_second.mel_filters
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
# Test feature size
_A = feature_extractor(_UpperCAmelCase , padding='max_length' , return_tensors='np' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
_A = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_features
_A = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_features
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test batched
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
_A = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_A = np.asarray(_UpperCAmelCase )
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test truncation required
_A = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
_A = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
_A = [x[: feature_extractor.n_samples] for x in speech_inputs]
_A = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs_truncated]
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
def lowerCAmelCase_ ( self : Dict ):
import torch
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = np.random.rand(100 , 32 ).astype(np.floataa )
_A = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_A = feature_extractor.pad([{'input_features': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_A = feature_extractor.pad([{'input_features': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Dict ):
_A = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
_A = ds.sort('id' ).select(range(_UpperCAmelCase ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def lowerCAmelCase_ ( self : List[Any] ):
# fmt: off
_A = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
_A = self._load_datasamples(1 )
_A = WhisperFeatureExtractor()
_A = feature_extractor(_UpperCAmelCase , return_tensors='pt' ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , _UpperCAmelCase , atol=1E-4 ) )
def lowerCAmelCase_ ( self : List[str] ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = self._load_datasamples(1 )[0]
_A = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
_A = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=_UpperCAmelCase )[0]
self.assertTrue(np.all(np.mean(_UpperCAmelCase ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase ) - 1 ) < 1E-3 ) )
| 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_A = Rectangle(height=0.25 , width=0.25 )
_A = [mem.copy() for i in range(6 )]
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('CPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(4 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('GPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Model' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(_UpperCAmelCase )
_A = []
_A = []
for i, rect in enumerate(_UpperCAmelCase ):
_A = fill.copy().set_fill(_UpperCAmelCase , opacity=0.8 )
target.move_to(_UpperCAmelCase )
model_arr.append(_UpperCAmelCase )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_UpperCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(_UpperCAmelCase )
self.add(*_UpperCAmelCase , *_UpperCAmelCase )
_A = [meta_mem.copy() for i in range(6 )]
_A = [meta_mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Disk' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_A = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_UpperCAmelCase )
_A = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase ) )
_A = Square(0.3 )
input.set_fill(_UpperCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , _UpperCAmelCase , buff=0.5 )
self.play(Write(_UpperCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=_UpperCAmelCase , buff=0.02 )
self.play(MoveToTarget(_UpperCAmelCase ) )
self.play(FadeOut(_UpperCAmelCase ) )
_A = Arrow(start=_UpperCAmelCase , end=_UpperCAmelCase , color=_UpperCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , _UpperCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
_A = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) )
_A = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(_UpperCAmelCase ) , Circumscribe(model_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
_A = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , _UpperCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
_A = AnimationGroup(
FadeOut(_UpperCAmelCase , run_time=0.5 ) , MoveToTarget(_UpperCAmelCase , run_time=0.5 ) , FadeIn(_UpperCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(_UpperCAmelCase )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
_A = 0.7
self.play(
Circumscribe(model_arr[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_arr[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
_A = a_c
_A = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(_UpperCAmelCase ) , FadeOut(_UpperCAmelCase , run_time=0.5 ) , )
_A = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) , MoveToTarget(_UpperCAmelCase ) )
self.wait()
| 315 | 1 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_A = Vector()
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(_UpperCAmelCase ) , '(0,0,0,0,0,1)' )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = Vector([1, 2, 3, 4] )
self.assertEqual(len(_UpperCAmelCase ) , 4 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2] )
_A = Vector([1, 2, 3, 4, 5] )
_A = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_A = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCAmelCase_ ( self : str ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
_A = Vector([2, -1, 4] ) # for test of dot product
_A = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , '(3.0,6.0,9.0)' )
self.assertEqual((a * b) , 0 )
def lowerCAmelCase_ ( self : Dict ):
self.assertEqual(str(zero_vector(10 ) ).count('0' ) , 10 )
def lowerCAmelCase_ ( self : Tuple ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , _UpperCAmelCase , _UpperCAmelCase ) ) , '(3,4,7)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 0, 0, 0, 0, 0] )
_A = x.copy()
self.assertEqual(str(_UpperCAmelCase ) , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(_UpperCAmelCase ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : str ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_A = Vector([1, 2, 3] )
self.assertEqual('(14,32,50)' , str(a * x ) )
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' , str(a * 2 ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' , str(a + b ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' , str(a - b ) )
def lowerCAmelCase_ ( self : int ):
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
_A , _A = list_data[i + 1], list_data[i]
_A = True
return list_data if not swapped else bubble_sort(_snake_case , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list[int] ) -> list[int]:
'''simple docstring'''
_A = len(_snake_case )
for i in range(_snake_case ):
for j in range(i + 1 , _snake_case ):
if numbers[j] < numbers[i]:
_A , _A = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
a = input('''Enter numbers separated by a comma:\n''').strip()
a = [int(item) for item in user_input.split(''',''')]
print(exchange_sort(unsorted))
| 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Any = ['''input_values''', '''attention_mask''']
def __init__( self : Dict , _UpperCAmelCase : int = 1 , _UpperCAmelCase : int = 16_000 , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 80 , _UpperCAmelCase : int = 16 , _UpperCAmelCase : int = 64 , _UpperCAmelCase : str = "hann_window" , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 80 , _UpperCAmelCase : float = 7_600 , _UpperCAmelCase : float = 1E-1_0 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : bool = True , **_UpperCAmelCase : List[Any] , ):
super().__init__(feature_size=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , padding_value=_UpperCAmelCase , **_UpperCAmelCase )
_A = do_normalize
_A = return_attention_mask
_A = num_mel_bins
_A = hop_length
_A = win_length
_A = win_function
_A = frame_signal_scale
_A = fmin
_A = fmax
_A = mel_floor
_A = reduction_factor
_A = win_length * sampling_rate // 1_000
_A = hop_length * sampling_rate // 1_000
_A = optimal_fft_length(self.sample_size )
_A = (self.n_fft // 2) + 1
_A = window_function(window_length=self.sample_size , name=self.win_function , periodic=_UpperCAmelCase )
_A = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm='slaney' , mel_scale='slaney' , )
if frame_signal_scale != 1.0:
warnings.warn(
'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , )
if reduction_factor != 2.0:
warnings.warn(
'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def lowerCAmelCase_ ( _UpperCAmelCase : List[np.ndarray] , _UpperCAmelCase : List[np.ndarray] , _UpperCAmelCase : float = 0.0 ):
if attention_mask is not None:
_A = np.array(_UpperCAmelCase , np.intaa )
_A = []
for vector, length in zip(_UpperCAmelCase , attention_mask.sum(-1 ) ):
_A = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
_A = padding_value
normed_input_values.append(_UpperCAmelCase )
else:
_A = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : np.ndarray , ):
_A = spectrogram(
_UpperCAmelCase , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel='log10' , )
return log_mel_spec.T
def __call__( self : int , _UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : Optional[int] = None , **_UpperCAmelCase : Optional[int] , ):
if audio is None and audio_target is None:
raise ValueError('You must provide either `audio` or `audio_target` values.' )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
if audio is not None:
_A = self._process_audio(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , )
else:
_A = None
if audio_target is not None:
_A = self._process_audio(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , )
if inputs is None:
return inputs_target
else:
_A = inputs_target['input_values']
_A = inputs_target.get('attention_mask' )
if decoder_attention_mask is not None:
_A = decoder_attention_mask
return inputs
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _UpperCAmelCase : bool = False , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , **_UpperCAmelCase : List[Any] , ):
_A = isinstance(_UpperCAmelCase , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
_A = is_batched_numpy or (
isinstance(_UpperCAmelCase , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_A = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(_UpperCAmelCase , np.ndarray ):
_A = np.asarray(_UpperCAmelCase , dtype=np.floataa )
elif isinstance(_UpperCAmelCase , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
_A = speech.astype(np.floataa )
# always return batch
if not is_batched:
_A = [speech]
# needed to make pad() work on spectrogram inputs
_A = self.feature_size
# convert into correct format for padding
if is_target:
_A = [self._extract_mel_features(_UpperCAmelCase ) for waveform in speech]
_A = BatchFeature({'input_values': features} )
_A = self.num_mel_bins
else:
_A = BatchFeature({'input_values': speech} )
_A = self.pad(
_UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , **_UpperCAmelCase , )
_A = feature_size_hack
# convert input values to correct format
_A = padded_inputs['input_values']
if not isinstance(input_values[0] , np.ndarray ):
_A = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(_UpperCAmelCase , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
_A = [array.astype(np.floataa ) for array in input_values]
elif isinstance(_UpperCAmelCase , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
_A = input_values.astype(np.floataa )
# convert attention_mask to correct format
_A = padded_inputs.get('attention_mask' )
if attention_mask is not None:
_A = [np.asarray(_UpperCAmelCase , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
_A = (
attention_mask
if self._get_padding_strategies(_UpperCAmelCase , max_length=_UpperCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
else None
)
_A = self.zero_mean_unit_var_norm(
padded_inputs['input_values'] , attention_mask=_UpperCAmelCase , padding_value=self.padding_value )
if return_tensors is not None:
_A = padded_inputs.convert_to_tensors(_UpperCAmelCase )
return padded_inputs
def lowerCAmelCase_ ( self : Any ):
_A = super().to_dict()
# Don't serialize these as they are derived from the other properties.
_A = ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
for name in names:
if name in output:
del output[name]
return output
| 315 | 1 |
"""simple docstring"""
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : list[list[int]] ):
_A = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which must be of type '
'int or float.' )
if len(_UpperCAmelCase ) != 0:
_A = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(_UpperCAmelCase ) != cols:
raise error
for value in row:
if not isinstance(_UpperCAmelCase , (int, float) ):
raise error
_A = rows
else:
_A = []
def lowerCAmelCase_ ( self : Optional[int] ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def lowerCAmelCase_ ( self : Tuple ):
return len(self.rows )
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
return len(self.rows[0] )
@property
def lowerCAmelCase_ ( self : int ):
return (self.num_rows, self.num_columns)
@property
def lowerCAmelCase_ ( self : Tuple ):
return self.order[0] == self.order[1]
def lowerCAmelCase_ ( self : int ):
_A = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Any ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def lowerCAmelCase_ ( self : str ):
return bool(self.determinant() )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(_UpperCAmelCase ).determinant()
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
if (row + column) % 2 == 0:
return self.get_minor(_UpperCAmelCase , _UpperCAmelCase )
return -1 * self.get_minor(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
return Matrix(
[
[self.get_minor(_UpperCAmelCase , _UpperCAmelCase ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def lowerCAmelCase_ ( self : Optional[Any] ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def lowerCAmelCase_ ( self : int ):
_A = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.determinant()
if not determinant:
raise TypeError('Only matrices with a non-zero determinant have an inverse' )
return self.adjugate() * (1 / determinant)
def __repr__( self : Tuple ):
return str(self.rows )
def __str__( self : Optional[int] ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'[' + '. '.join([str(_UpperCAmelCase ) for value in row] ) + '.]'
for row in self.rows
] )
+ "]"
)
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : list[int] , _UpperCAmelCase : int | None = None ):
_A = TypeError('Row must be a list containing all ints and/or floats' )
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise type_error
for value in row:
if not isinstance(_UpperCAmelCase , (int, float) ):
raise type_error
if len(_UpperCAmelCase ) != self.num_columns:
raise ValueError(
'Row must be equal in length to the other rows in the matrix' )
if position is None:
self.rows.append(_UpperCAmelCase )
else:
_A = self.rows[0:position] + [row] + self.rows[position:]
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : list[int] , _UpperCAmelCase : int | None = None ):
_A = TypeError(
'Column must be a list containing all ints and/or floats' )
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise type_error
for value in column:
if not isinstance(_UpperCAmelCase , (int, float) ):
raise type_error
if len(_UpperCAmelCase ) != self.num_rows:
raise ValueError(
'Column must be equal in length to the other columns in the matrix' )
if position is None:
_A = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
_A = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self : Optional[int] , _UpperCAmelCase : object ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return NotImplemented
return self.rows == other.rows
def __ne__( self : Optional[int] , _UpperCAmelCase : object ):
return not self == other
def __neg__( self : int ):
return self * -1
def __add__( self : int , _UpperCAmelCase : Matrix ):
if self.order != other.order:
raise ValueError('Addition requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self : str , _UpperCAmelCase : Matrix ):
if self.order != other.order:
raise ValueError('Subtraction requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self : Union[str, Any] , _UpperCAmelCase : Matrix | int | float ):
if isinstance(_UpperCAmelCase , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
if self.num_columns != other.num_rows:
raise ValueError(
'The number of columns in the first matrix must '
'be equal to the number of rows in the second' )
return Matrix(
[
[Matrix.dot_product(_UpperCAmelCase , _UpperCAmelCase ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'A Matrix can only be multiplied by an int, float, or another matrix' )
def __pow__( self : Optional[Any] , _UpperCAmelCase : int ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('A Matrix can only be raised to the power of an int' )
if not self.is_square:
raise ValueError('Only square matrices can be raised to a power' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'Only invertable matrices can be raised to a negative power' )
_A = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def lowerCAmelCase_ ( cls : Dict , _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] ):
return sum(row[i] * column[i] for i in range(len(_UpperCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = []
create_all_state(1 , _snake_case , _snake_case , [] , _snake_case )
return result
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : list[int] , _snake_case : list[list[int]] , ) -> None:
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(_snake_case , total_number - level + 2 ):
current_list.append(_snake_case )
create_all_state(i + 1 , _snake_case , level - 1 , _snake_case , _snake_case )
current_list.pop()
def _snake_case ( _snake_case : list[list[int]] ) -> None:
'''simple docstring'''
for i in total_list:
print(*_snake_case )
if __name__ == "__main__":
a = 4
a = 2
a = generate_all_combinations(n, k)
print_all_state(total_list)
| 315 | 1 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
a = {
'''vocab_file''': {
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
},
'''merges_file''': {
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''Salesforce/codegen-350M-mono''': (
'''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
),
},
}
a = {
'''Salesforce/codegen-350M-mono''': 2_048,
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : str = VOCAB_FILES_NAMES
UpperCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Union[str, Any] = ['''input_ids''', '''attention_mask''']
UpperCAmelCase : Dict = CodeGenTokenizer
def __init__( self : Dict , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Any="<|endoftext|>" , _UpperCAmelCase : Dict="<|endoftext|>" , _UpperCAmelCase : Tuple="<|endoftext|>" , _UpperCAmelCase : Any=False , **_UpperCAmelCase : int , ):
super().__init__(
_UpperCAmelCase , _UpperCAmelCase , tokenizer_file=_UpperCAmelCase , unk_token=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , **_UpperCAmelCase , )
if kwargs.pop('add_bos_token' , _UpperCAmelCase ):
_A = kwargs.pop('name_or_path' , '' )
raise ValueError(
'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
F'''`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'''
F'''`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'''
'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
' so that the fast tokenizer works correctly.' )
_A = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , _UpperCAmelCase ) != add_prefix_space:
_A = getattr(_UpperCAmelCase , pre_tok_state.pop('type' ) )
_A = add_prefix_space
_A = pre_tok_class(**_UpperCAmelCase )
_A = add_prefix_space
def lowerCAmelCase_ ( self : Any , *_UpperCAmelCase : Any , **_UpperCAmelCase : int ):
_A = kwargs.get('is_split_into_words' , _UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*_UpperCAmelCase , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Any ):
_A = kwargs.get('is_split_into_words' , _UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*_UpperCAmelCase , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
_A = self._tokenizer.model.save(_UpperCAmelCase , name=_UpperCAmelCase )
return tuple(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"] , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = None , _UpperCAmelCase : Optional[List[str]] = None , **_UpperCAmelCase : Dict , ):
_A = super().decode(
token_ids=_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase , **_UpperCAmelCase , )
if truncate_before_pattern is not None and len(_UpperCAmelCase ) > 0:
_A = self.truncate(_UpperCAmelCase , _UpperCAmelCase )
return decoded_text
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any ):
def find_re(_UpperCAmelCase : Tuple , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ):
_A = pattern.search(_UpperCAmelCase , _UpperCAmelCase )
return m.start() if m else -1
_A = [re.compile(_UpperCAmelCase , re.MULTILINE ) for pattern in truncate_before_pattern]
_A = list(re.finditer('^print' , _UpperCAmelCase , re.MULTILINE ) )
if len(_UpperCAmelCase ) > 1:
_A = completion[: prints[1].start()]
_A = list(re.finditer('^def' , _UpperCAmelCase , re.MULTILINE ) )
if len(_UpperCAmelCase ) > 1:
_A = completion[: defs[1].start()]
_A = 0
_A = [
pos for pos in [find_re(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) for terminal in terminals] if pos != -1
]
if len(_UpperCAmelCase ) > 0:
return completion[: min(_UpperCAmelCase )]
else:
return completion
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 315 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = ['''DeiTFeatureExtractor''']
a = ['''DeiTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DeiTForImageClassification''',
'''DeiTForImageClassificationWithTeacher''',
'''DeiTForMaskedImageModeling''',
'''DeiTModel''',
'''DeiTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'''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 = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[str] ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple=True ):
_A = ()
for resnet, attn in zip(self.resnets , self.attentions ):
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[Any] ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[str]=True ):
_A = ()
for resnet in self.resnets:
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any]=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : int , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int]=True ):
for resnet in self.resnets:
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Dict ):
# there is always at least one resnet
_A = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_A = []
for _ in range(self.num_layers ):
_A = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
_A = attentions
def __call__( self : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int]=True ):
_A = self.resnets[0](_UpperCAmelCase , _UpperCAmelCase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
return hidden_states
| 315 | 1 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A = b
if function(_snake_case ) == 0: # one of the a or b is a root for the function
return a
elif function(_snake_case ) == 0:
return b
elif (
function(_snake_case ) * function(_snake_case ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
_A = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(_snake_case ) == 0:
return mid
elif function(_snake_case ) * function(_snake_case ) < 0:
_A = mid
else:
_A = mid
_A = start + (end - start) / 2.0
return mid
def _snake_case ( _snake_case : float ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
_A = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
_A = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
_A = numpy.random.rand(3 , 1 )
# Real output values provided.
_A = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
_A = numpy.zeros(output_array.shape )
def lowerCAmelCase_ ( self : List[str] ):
_A = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def lowerCAmelCase_ ( self : Optional[int] ):
_A = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
_A = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
_A = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : int , _UpperCAmelCase : bool ):
for iteration in range(1 , iterations + 1 ):
_A = self.feedforward()
self.back_propagation()
if give_loss:
_A = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F'''Iteration {iteration} Loss: {loss}''' )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : numpy.ndarray ):
_A = input_arr
_A = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return 1 / (1 + numpy.exp(-value ))
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return (value) * (1 - (value))
def _snake_case ( ) -> int:
'''simple docstring'''
_A = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
_A = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
_A = TwoHiddenLayerNeuralNetwork(
input_array=_snake_case , output_array=_snake_case )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_snake_case , iterations=10 , give_loss=_snake_case )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 315 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = LDMTextToImagePipeline
UpperCAmelCase : Dict = TEXT_TO_IMAGE_PARAMS - {
'''negative_prompt''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
'''prompt_embeds''',
}
UpperCAmelCase : List[Any] = PipelineTesterMixin.required_optional_params - {
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
UpperCAmelCase : Any = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase : Tuple = False
def lowerCAmelCase_ ( self : List[str] ):
torch.manual_seed(0 )
_A = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
_A = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
torch.manual_seed(0 )
_A = AutoencoderKL(
block_out_channels=(32, 64) , in_channels=3 , out_channels=3 , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , latent_channels=4 , )
torch.manual_seed(0 )
_A = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
_A = CLIPTextModel(_UpperCAmelCase )
_A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_A = {
'unet': unet,
'scheduler': scheduler,
'vqvae': vae,
'bert': text_encoder,
'tokenizer': tokenizer,
}
return components
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int]=0 ):
if str(_UpperCAmelCase ).startswith('mps' ):
_A = torch.manual_seed(_UpperCAmelCase )
else:
_A = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
_A = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def lowerCAmelCase_ ( self : Any ):
_A = 'cpu' # ensure determinism for the device-dependent torch.Generator
_A = self.get_dummy_components()
_A = LDMTextToImagePipeline(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = self.get_dummy_inputs(_UpperCAmelCase )
_A = pipe(**_UpperCAmelCase ).images
_A = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
_A = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : int=torch.floataa , _UpperCAmelCase : str=0 ):
_A = torch.manual_seed(_UpperCAmelCase )
_A = np.random.RandomState(_UpperCAmelCase ).standard_normal((1, 4, 32, 32) )
_A = torch.from_numpy(_UpperCAmelCase ).to(device=_UpperCAmelCase , dtype=_UpperCAmelCase )
_A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def lowerCAmelCase_ ( self : List[str] ):
_A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = self.get_inputs(_UpperCAmelCase )
_A = pipe(**_UpperCAmelCase ).images
_A = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
_A = np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878] )
_A = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Tuple ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : int=torch.floataa , _UpperCAmelCase : str=0 ):
_A = torch.manual_seed(_UpperCAmelCase )
_A = np.random.RandomState(_UpperCAmelCase ).standard_normal((1, 4, 32, 32) )
_A = torch.from_numpy(_UpperCAmelCase ).to(device=_UpperCAmelCase , dtype=_UpperCAmelCase )
_A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = self.get_inputs(_UpperCAmelCase )
_A = pipe(**_UpperCAmelCase ).images[0]
_A = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
_A = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : T ):
_A = data
_A = None
def __str__( self : str ):
return F'''{self.data}'''
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple ):
_A = None
def __iter__( self : List[Any] ):
_A = self.top
while node:
yield node.data
_A = node.next
def __str__( self : Union[str, Any] ):
return "->".join([str(_UpperCAmelCase ) for item in self] )
def __len__( self : List[Any] ):
return len(tuple(iter(self ) ) )
def lowerCAmelCase_ ( self : str ):
return self.top is None
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : T ):
_A = Node(_UpperCAmelCase )
if not self.is_empty():
_A = self.top
_A = node
def lowerCAmelCase_ ( self : Dict ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _UpperCAmelCase )
_A = self.top
_A = self.top.next
return pop_node.data
def lowerCAmelCase_ ( self : Tuple ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 315 | 1 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
a = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
}
def _snake_case ( _snake_case : str = "dhaka" , _snake_case : int = 5 ) -> int:
'''simple docstring'''
_A = min(_snake_case , 50 ) # Prevent abuse!
_A = {
'q': query,
'tbm': 'isch',
'hl': 'en',
'ijn': '0',
}
_A = requests.get('https://www.google.com/search' , params=_snake_case , headers=_snake_case )
_A = BeautifulSoup(html.text , 'html.parser' )
_A = ''.join(
re.findall(R'AF_initDataCallback\(([^<]+)\);' , str(soup.select('script' ) ) ) )
_A = json.dumps(_snake_case )
_A = json.loads(_snake_case )
_A = re.findall(
R'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",' , _snake_case , )
if not matched_google_image_data:
return 0
_A = re.sub(
R'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]' , '' , str(_snake_case ) , )
_A = re.findall(
R'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]' , _snake_case , )
for index, fixed_full_res_image in enumerate(_snake_case ):
if index >= max_images:
return index
_A = bytes(_snake_case , 'ascii' ).decode(
'unicode-escape' )
_A = bytes(_snake_case , 'ascii' ).decode(
'unicode-escape' )
_A = urllib.request.build_opener()
_A = [
(
'User-Agent',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
)
]
urllib.request.install_opener(_snake_case )
_A = F'''query_{query.replace(" " , "_" )}'''
if not os.path.exists(_snake_case ):
os.makedirs(_snake_case )
urllib.request.urlretrieve( # noqa: S310
_snake_case , F'''{path_name}/original_size_img_{index}.jpg''' )
return index
if __name__ == "__main__":
try:
a = download_images_from_google_query(sys.argv[1])
print(F'''{image_count} images were downloaded to disk.''')
except IndexError:
print('''Please provide a search term.''')
raise
| 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Union[str, Any] ):
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _snake_case ( _snake_case : dict , _snake_case : Any , _snake_case : int ) -> list[str]:
'''simple docstring'''
_A = set()
# keep track of all the paths to be checked
_A = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
_A = queue.pop(0 )
# get the last node from the path
_A = path[-1]
if node not in explored:
_A = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
_A = list(_snake_case )
new_path.append(_snake_case )
queue.append(_snake_case )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(_snake_case )
# in case there's no path between the 2 nodes
return []
def _snake_case ( _snake_case : dict , _snake_case : Union[str, Any] , _snake_case : int ) -> int:
'''simple docstring'''
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
_A = [start]
_A = set(_snake_case )
# Keep tab on distances from `start` node.
_A = {start: 0, target: -1}
while queue:
_A = queue.pop(0 )
if node == target:
_A = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(_snake_case )
queue.append(_snake_case )
_A = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def _snake_case ( _snake_case : str ) -> list[str]:
'''simple docstring'''
return word_by_signature[signature(_snake_case )]
a = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
a = sorted({word.strip().lower() for word in data.splitlines()})
a = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
a = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 315 | 1 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
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 .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The column name of the images in the files.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the training data.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the validation data.'''} )
UpperCAmelCase : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def lowerCAmelCase_ ( self : Dict ):
_A = {}
if self.train_dir is not None:
_A = self.train_dir
if self.validation_dir is not None:
_A = self.validation_dir
_A = data_files if data_files else None
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : str = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
UpperCAmelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase : str = field(default=__lowerCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCAmelCase : float = field(
default=0.75 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : float = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def _snake_case ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
_A = torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _snake_case ( ) -> List[str]:
'''simple docstring'''
_A = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A , _A , _A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , _snake_case , _snake_case )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_A = training_args.get_process_log_level()
logger.setLevel(_snake_case )
transformers.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
_A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_A = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _snake_case ) and data_args.train_val_split > 0.0:
_A = ds['train'].train_test_split(data_args.train_val_split )
_A = split['train']
_A = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_A = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
_A = ViTMAEConfig.from_pretrained(model_args.config_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_A = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_A = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
_A = ViTMAEForPreTraining(_snake_case )
if training_args.do_train:
_A = ds['train'].column_names
else:
_A = ds['validation'].column_names
if data_args.image_column_name is not None:
_A = data_args.image_column_name
elif "image" in column_names:
_A = 'image'
elif "img" in column_names:
_A = 'img'
else:
_A = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_A = image_processor.size['shortest_edge']
else:
_A = (image_processor.size['height'], image_processor.size['width'])
_A = Compose(
[
Lambda(lambda _snake_case : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_snake_case , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_snake_case : List[Any] ):
_A = [transforms(_snake_case ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
_A = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_snake_case )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
_A = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_snake_case )
# Compute absolute learning rate
_A = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_A = training_args.base_learning_rate * total_train_batch_size / 2_56
# Initialize our trainer
_A = Trainer(
model=_snake_case , args=_snake_case , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_snake_case , data_collator=_snake_case , )
# Training
if training_args.do_train:
_A = None
if training_args.resume_from_checkpoint is not None:
_A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A = last_checkpoint
_A = trainer.train(resume_from_checkpoint=_snake_case )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_A = trainer.evaluate()
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
# Write model card and (optionally) push to hub
_A = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def _snake_case ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
a = logging.get_logger(__name__)
a = {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
),
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = '''longformer'''
def __init__( self : int , _UpperCAmelCase : Union[List[int], int] = 512 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : int = 1 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : int = 30_522 , _UpperCAmelCase : int = 768 , _UpperCAmelCase : int = 12 , _UpperCAmelCase : int = 12 , _UpperCAmelCase : int = 3_072 , _UpperCAmelCase : str = "gelu" , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : int = 512 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : float = 0.02 , _UpperCAmelCase : float = 1E-1_2 , _UpperCAmelCase : bool = False , **_UpperCAmelCase : int , ):
super().__init__(pad_token_id=_UpperCAmelCase , **_UpperCAmelCase )
_A = attention_window
_A = sep_token_id
_A = bos_token_id
_A = eos_token_id
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = onnx_export
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : List[Any] , _UpperCAmelCase : "PretrainedConfig" , _UpperCAmelCase : str = "default" , _UpperCAmelCase : "List[PatchingSpec]" = None ):
super().__init__(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
_A = True
@property
def lowerCAmelCase_ ( self : List[str] ):
if self.task == "multiple-choice":
_A = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_A = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('global_attention_mask', dynamic_axis),
] )
@property
def lowerCAmelCase_ ( self : List[str] ):
_A = super().outputs
if self.task == "default":
_A = {0: 'batch'}
return outputs
@property
def lowerCAmelCase_ ( self : List[str] ):
return 1E-4
@property
def lowerCAmelCase_ ( self : int ):
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : "PreTrainedTokenizerBase" , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[TensorType] = None , ):
_A = super().generate_dummy_inputs(
preprocessor=_UpperCAmelCase , batch_size=_UpperCAmelCase , seq_length=_UpperCAmelCase , is_pair=_UpperCAmelCase , framework=_UpperCAmelCase )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
_A = torch.zeros_like(inputs['input_ids'] )
# make every second token global
_A = 1
return inputs
| 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer names
a = 70 # max width of quantizer names
def _snake_case ( _snake_case : int ) -> List[Any]:
'''simple docstring'''
_A = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=_snake_case , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=_snake_case , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=_snake_case , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=_snake_case , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=_snake_case , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=_snake_case , type=_snake_case , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=_snake_case , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def _snake_case ( _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
if args.calibrator == "max":
_A = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
_A = 'histogram'
elif args.calibrator == "mse":
_A = 'histogram'
else:
raise ValueError(F'''Invalid calibrator {args.calibrator}''' )
_A = QuantDescriptor(num_bits=args.aprec , calib_method=_snake_case )
_A = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(_snake_case )
quant_nn.QuantLinear.set_default_quant_desc_weight(_snake_case )
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Any=False , _snake_case : Union[str, Any]=False ) -> Optional[int]:
'''simple docstring'''
logger.info('Configuring Model for Quantization' )
logger.info(F'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_snake_case , ['embeddings'] , which='weight' , _disabled=_snake_case )
if args.quant_disable:
set_quantizer_by_name(_snake_case , [''] , _disabled=_snake_case )
if args.quant_disable_keyword:
set_quantizer_by_name(_snake_case , args.quant_disable_keyword , _disabled=_snake_case )
if args.quant_disable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_disable_layer_module] , _disabled=_snake_case )
if args.quant_enable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_enable_layer_module] , _disabled=_snake_case )
if args.recalibrate_weights:
recalibrate_weights(_snake_case )
if args.fuse_qkv:
fuse_qkv(_snake_case , _snake_case )
if args.clip_gelu:
clip_gelu(_snake_case , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str ) -> Any:
'''simple docstring'''
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F'''{name:80}: {module}''' )
def _snake_case ( _snake_case : List[Any] , _snake_case : List[Any] ) -> str:
'''simple docstring'''
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
def fusea(_snake_case : int , _snake_case : str , _snake_case : Optional[Any] ):
for mod in [qq, qk, qv]:
if not hasattr(_snake_case , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
_A = qq._amax.detach().item()
_A = qk._amax.detach().item()
_A = qv._amax.detach().item()
_A = max(_snake_case , _snake_case , _snake_case )
qq._amax.fill_(_snake_case )
qk._amax.fill_(_snake_case )
qv._amax.fill_(_snake_case )
logger.info(F''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(F'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def _snake_case ( _snake_case : int , _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
_A = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_snake_case )
_A = mod._input_quantizer._amax.data.detach().item()
logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def _snake_case ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
_A = mod.weight.shape[0]
_A = mod._weight_quantizer._amax.detach()
_A = torch.ones(_snake_case , dtype=amax.dtype , device=amax.device ) * amax
print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def _snake_case ( _snake_case : Dict ) -> Tuple:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
_A = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
_A = set(range(len(mod.weight.size() ) ) ) - axis_set
_A = pytorch_quantization.utils.reduce_amax(mod.weight , axis=_snake_case , keepdims=_snake_case ).detach()
logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
_A = amax
def _snake_case ( _snake_case : Tuple , _snake_case : List[str]=25 , _snake_case : str=1_80 , _snake_case : int=None ) -> List[Any]:
'''simple docstring'''
if ignore is None:
_A = []
elif not isinstance(_snake_case , _snake_case ):
_A = [ignore]
_A = 0
for name, mod in model.named_modules():
if not hasattr(_snake_case , 'weight' ):
continue
_A = max(_snake_case , len(_snake_case ) )
for name, mod in model.named_modules():
_A = getattr(_snake_case , '_input_quantizer' , _snake_case )
_A = getattr(_snake_case , '_weight_quantizer' , _snake_case )
if not hasattr(_snake_case , 'weight' ):
continue
if type(_snake_case ) in ignore:
continue
if [True for s in ignore if type(_snake_case ) is str and s in name]:
continue
_A = F'''Act:{input_q.extra_repr()}'''
_A = F'''Wgt:{weight_q.extra_repr()}'''
_A = F'''{name:{name_width}} {act_str} {wgt_str}'''
if len(_snake_case ) <= line_width:
logger.info(_snake_case )
else:
logger.info(F'''{name:{name_width}} {act_str}''' )
logger.info(F'''{" ":{name_width}} {wgt_str}''' )
def _snake_case ( _snake_case : Dict ) -> int:
'''simple docstring'''
_A = 0
for name, mod in model.named_modules():
if isinstance(_snake_case , pytorch_quantization.nn.TensorQuantizer ):
print(F'''{name:80} {mod}''' )
count += 1
print(F'''{count} TensorQuantizers found in model''' )
def _snake_case ( _snake_case : str , _snake_case : Dict , _snake_case : List[Any] , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
_A = getattr(_snake_case , _snake_case , _snake_case )
if quantizer_mod is not None:
assert hasattr(_snake_case , _snake_case )
setattr(_snake_case , _snake_case , _snake_case )
else:
logger.warning(F'''{name} has no {quantizer}''' )
def _snake_case ( _snake_case : Dict , _snake_case : Optional[int] , _snake_case : str="both" , **_snake_case : List[Any] ) -> str:
'''simple docstring'''
_A = F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(_snake_case , _snake_case , '_input_quantizer' , _snake_case , _snake_case )
if which in ["weight", "both"]:
set_quantizer(_snake_case , _snake_case , '_weight_quantizer' , _snake_case , _snake_case )
logger.info(_snake_case )
def _snake_case ( _snake_case : Any , _snake_case : int , **_snake_case : Dict ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_input_quantizer' ) or hasattr(_snake_case , '_weight_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
set_quantizers(_snake_case , _snake_case , **_snake_case )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
_A = F'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
setattr(_snake_case , _snake_case , _snake_case )
logger.info(_snake_case )
| 315 | 1 |
"""simple docstring"""
class lowercase_ :
'''simple docstring'''
def __init__( self : Optional[Any] ):
_A = {}
def lowerCAmelCase_ ( self : Dict ):
print(self.vertex )
for i in self.vertex:
print(_UpperCAmelCase , ' -> ' , ' -> '.join([str(_UpperCAmelCase ) for j in self.vertex[i]] ) )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(_UpperCAmelCase )
else:
# else make a new vertex
_A = [to_vertex]
def lowerCAmelCase_ ( self : int ):
# visited array for storing already visited nodes
_A = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : list ):
# mark start vertex as visited
_A = True
print(_UpperCAmelCase , end=' ' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
a = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('''DFS:''')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'] , )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int]=False ):
_A = spearmanr(_UpperCAmelCase , _UpperCAmelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 315 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert import RemBertTokenizer
else:
a = None
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
a = {
'''vocab_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
},
'''tokenizer_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
},
}
a = {
'''google/rembert''': 256,
}
a = '''▁'''
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = VOCAB_FILES_NAMES
UpperCAmelCase : str = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Optional[int] = RemBertTokenizer
def __init__( self : List[Any] , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : str=True , _UpperCAmelCase : int=True , _UpperCAmelCase : str=False , _UpperCAmelCase : Optional[Any]="[CLS]" , _UpperCAmelCase : List[str]="[SEP]" , _UpperCAmelCase : Union[str, Any]="<unk>" , _UpperCAmelCase : Any="[SEP]" , _UpperCAmelCase : Tuple="<pad>" , _UpperCAmelCase : Any="[CLS]" , _UpperCAmelCase : List[Any]="[MASK]" , **_UpperCAmelCase : List[Any] , ):
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , **_UpperCAmelCase , )
_A = do_lower_case
_A = remove_space
_A = keep_accents
_A = vocab_file
_A = False if not self.vocab_file else True
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : 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 x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1]
return [1] + ([0] * len(_UpperCAmelCase )) + [1]
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
if not os.path.isdir(_UpperCAmelCase ):
logger.error('Vocabulary path ({}) should be a directory'.format(_UpperCAmelCase ) )
return
_A = os.path.join(
_UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
return (out_vocab_file,)
| 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A = b
if function(_snake_case ) == 0: # one of the a or b is a root for the function
return a
elif function(_snake_case ) == 0:
return b
elif (
function(_snake_case ) * function(_snake_case ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
_A = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(_snake_case ) == 0:
return mid
elif function(_snake_case ) * function(_snake_case ) < 0:
_A = mid
else:
_A = mid
_A = start + (end - start) / 2.0
return mid
def _snake_case ( _snake_case : float ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
a = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
'''simple docstring'''
UpperCAmelCase : Optional[datasets.Features] = None
def _snake_case ( _snake_case : "pyspark.sql.DataFrame" , _snake_case : List[int] , ) -> Dict:
'''simple docstring'''
import pyspark
def generate_fn():
_A = df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id' ) )
for partition_id in partition_order:
_A = df_with_partition_id.select('*' ).where(F'''part_id = {partition_id}''' ).drop('part_id' )
_A = partition_df.collect()
_A = 0
for row in rows:
yield F'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class lowercase_ ( _BaseExamplesIterable ):
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : "pyspark.sql.DataFrame" , _UpperCAmelCase : Tuple=None , ):
_A = df
_A = partition_order or range(self.df.rdd.getNumPartitions() )
_A = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : Optional[Any] ):
yield from self.generate_examples_fn()
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : np.random.Generator ):
_A = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(_UpperCAmelCase )
return SparkExamplesIterable(self.df , partition_order=_UpperCAmelCase )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = self.split_shard_indices_by_worker(_UpperCAmelCase , _UpperCAmelCase )
return SparkExamplesIterable(self.df , partition_order=_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : int ):
return len(self.partition_order )
class lowercase_ ( datasets.DatasetBuilder ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = SparkConfig
def __init__( self : int , _UpperCAmelCase : "pyspark.sql.DataFrame" , _UpperCAmelCase : str = None , _UpperCAmelCase : str = None , **_UpperCAmelCase : Optional[Any] , ):
import pyspark
_A = pyspark.sql.SparkSession.builder.getOrCreate()
_A = df
_A = working_dir
super().__init__(
cache_dir=_UpperCAmelCase , config_name=str(self.df.semanticHash() ) , **_UpperCAmelCase , )
def lowerCAmelCase_ ( self : List[Any] ):
# Returns the path of the created file.
def create_cache_and_write_probe(_UpperCAmelCase : Union[str, Any] ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=_UpperCAmelCase )
_A = os.path.join(self._cache_dir , 'fs_test' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(_UpperCAmelCase , 'a' )
return [probe_file]
if self._spark.conf.get('spark.master' , '' ).startswith('local' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_A = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(_UpperCAmelCase ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' )
def lowerCAmelCase_ ( self : List[str] ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : datasets.download.download_manager.DownloadManager ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Any ):
import pyspark
def get_arrow_batch_size(_UpperCAmelCase : Optional[Any] ):
for batch in it:
yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} )
_A = self.df.count()
_A = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_A = (
self.df.limit(_UpperCAmelCase )
.repartition(1 )
.mapInArrow(_UpperCAmelCase , 'batch_bytes: long' )
.agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_A = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_A = min(_UpperCAmelCase , int(approx_total_size / max_shard_size ) )
_A = self.df.repartition(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : int , ):
import pyspark
_A = ParquetWriter if file_format == 'parquet' else ArrowWriter
_A = os.path.join(self._working_dir , os.path.basename(_UpperCAmelCase ) ) if self._working_dir else fpath
_A = file_format == 'parquet'
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_A = self.config.features
_A = self._writer_batch_size
_A = self._fs.storage_options
def write_arrow(_UpperCAmelCase : List[str] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_A = pyspark.TaskContext().taskAttemptId()
_A = next(_UpperCAmelCase , _UpperCAmelCase )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['task_id', 'num_examples', 'num_bytes'] , )
_A = 0
_A = writer_class(
features=_UpperCAmelCase , path=working_fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , writer_batch_size=_UpperCAmelCase , storage_options=_UpperCAmelCase , embed_local_files=_UpperCAmelCase , )
_A = pa.Table.from_batches([first_batch] )
writer.write_table(_UpperCAmelCase )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_A , _A = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
shard_id += 1
_A = writer_class(
features=writer._features , path=working_fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , writer_batch_size=_UpperCAmelCase , storage_options=_UpperCAmelCase , embed_local_files=_UpperCAmelCase , )
_A = pa.Table.from_batches([batch] )
writer.write_table(_UpperCAmelCase )
if writer._num_bytes > 0:
_A , _A = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(_UpperCAmelCase ) ):
_A = os.path.join(os.path.dirname(_UpperCAmelCase ) , os.path.basename(_UpperCAmelCase ) )
shutil.move(_UpperCAmelCase , _UpperCAmelCase )
_A = (
self.df.mapInArrow(_UpperCAmelCase , 'task_id: long, num_examples: long, num_bytes: long' )
.groupBy('task_id' )
.agg(
pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' ) , pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' ) , pyspark.sql.functions.count('num_bytes' ).alias('num_shards' ) , pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : "datasets.SplitGenerator" , _UpperCAmelCase : str = "arrow" , _UpperCAmelCase : Optional[Union[str, int]] = None , _UpperCAmelCase : Optional[int] = None , **_UpperCAmelCase : Dict , ):
self._validate_cache_dir()
_A = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(_UpperCAmelCase )
_A = not is_remote_filesystem(self._fs )
_A = os.path.join if is_local else posixpath.join
_A = '-TTTTT-SSSSS-of-NNNNN'
_A = F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
_A = path_join(self._output_dir , _UpperCAmelCase )
_A = 0
_A = 0
_A = 0
_A = []
_A = []
for task_id, content in self._prepare_split_single(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
(
(
_A
) , (
_A
) , (
_A
) , (
_A
) ,
) = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(_UpperCAmelCase )
_A = total_num_examples
_A = total_num_bytes
# should rename everything at the end
logger.debug(F'''Renaming {total_shards} shards.''' )
if total_shards > 1:
_A = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_A = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
_UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , ):
rename(
_UpperCAmelCase , fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , fpath.replace('TTTTT-SSSSS' , F'''{global_shard_id:05d}''' ).replace('NNNNN' , F'''{total_shards:05d}''' ) , )
_A = []
_A = 0
for i in range(len(_UpperCAmelCase ) ):
_A , _A = task_id_and_num_shards[i]
for shard_id in range(_UpperCAmelCase ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(_UpperCAmelCase , len(_UpperCAmelCase ) ).map(lambda _UpperCAmelCase : _rename_shard(*_UpperCAmelCase ) ).collect()
else:
# don't use any pattern
_A = 0
_A = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('SSSSS' , F'''{shard_id:05d}''' ).replace('TTTTT' , F'''{task_id:05d}''' ) , fpath.replace(_UpperCAmelCase , '' ) , )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : "datasets.SplitGenerator" , ):
return SparkExamplesIterable(self.df )
| 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmelCase ) , '''Tatoeba directory does not exist.''' )
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Optional[int] ):
self.resolver.convert_models(['heb-eng'] )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A , _A = self.resolver.write_model_card('opus-mt-he-en' , dry_run=_UpperCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 315 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple=100 , _UpperCAmelCase : Optional[Any]=13 , _UpperCAmelCase : int=30 , _UpperCAmelCase : Tuple=2 , _UpperCAmelCase : int=3 , _UpperCAmelCase : Optional[int]=True , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Optional[Any]=32 , _UpperCAmelCase : Any=5 , _UpperCAmelCase : int=4 , _UpperCAmelCase : Optional[int]=37 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Dict=10 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : List[Any]=3 , ):
_A = parent
_A = vocab_size
_A = batch_size
_A = image_size
_A = patch_size
_A = num_channels
_A = is_training
_A = use_labels
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = type_sequence_label_size
_A = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_A = (image_size // patch_size) ** 2
_A = num_patches + 1
def lowerCAmelCase_ ( self : int ):
_A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ):
_A = FlaxBeitModel(config=_UpperCAmelCase )
_A = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ):
_A = FlaxBeitForMaskedImageModeling(config=_UpperCAmelCase )
_A = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Dict , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] ):
_A = self.type_sequence_label_size
_A = FlaxBeitForImageClassification(config=_UpperCAmelCase )
_A = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_A = 1
_A = FlaxBeitForImageClassification(_UpperCAmelCase )
_A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_A = model(_UpperCAmelCase )
def lowerCAmelCase_ ( self : int ):
_A = self.prepare_config_and_inputs()
(
(
_A
) , (
_A
) , (
_A
) ,
) = config_and_inputs
_A = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Any = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = FlaxBeitModelTester(self )
_A = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def lowerCAmelCase_ ( self : List[str] ):
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : Optional[Any] ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(_UpperCAmelCase )
_A = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['pixel_values']
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Tuple ):
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_A = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase )
_A = model_class(_UpperCAmelCase )
@jax.jit
def model_jitted(_UpperCAmelCase : Dict , **_UpperCAmelCase : Optional[int] ):
return model(pixel_values=_UpperCAmelCase , **_UpperCAmelCase )
with self.subTest('JIT Enabled' ):
_A = model_jitted(**_UpperCAmelCase ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
_A = model_jitted(**_UpperCAmelCase ).to_tuple()
self.assertEqual(len(_UpperCAmelCase ) , len(_UpperCAmelCase ) )
for jitted_output, output in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Tuple ):
for model_class_name in self.all_model_classes:
_A = model_class_name.from_pretrained('microsoft/beit-base-patch16-224' )
_A = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(_UpperCAmelCase )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
_A = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@require_flax
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : Optional[Any] ):
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def lowerCAmelCase_ ( self : str ):
_A = FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=_UpperCAmelCase , return_tensors='np' ).pixel_values
# prepare bool_masked_pos
_A = np.ones((1, 196) , dtype=_UpperCAmelCase )
# forward pass
_A = model(pixel_values=_UpperCAmelCase , bool_masked_pos=_UpperCAmelCase )
_A = outputs.logits
# verify the logits
_A = (1, 196, 8_192)
self.assertEqual(logits.shape , _UpperCAmelCase )
_A = np.array(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , _UpperCAmelCase , atol=1E-2 ) )
@slow
def lowerCAmelCase_ ( self : Tuple ):
_A = FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=_UpperCAmelCase , return_tensors='np' )
# forward pass
_A = model(**_UpperCAmelCase )
_A = outputs.logits
# verify the logits
_A = (1, 1_000)
self.assertEqual(logits.shape , _UpperCAmelCase )
_A = np.array([-1.2385, -1.0987, -1.0108] )
self.assertTrue(np.allclose(logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) )
_A = 281
self.assertEqual(logits.argmax(-1 ).item() , _UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Dict ):
_A = FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=_UpperCAmelCase , return_tensors='np' )
# forward pass
_A = model(**_UpperCAmelCase )
_A = outputs.logits
# verify the logits
_A = (1, 21_841)
self.assertEqual(logits.shape , _UpperCAmelCase )
_A = np.array([1.6881, -0.2787, 0.5901] )
self.assertTrue(np.allclose(logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) )
_A = 2_396
self.assertEqual(logits.argmax(-1 ).item() , _UpperCAmelCase )
| 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
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 .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 315 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
a = None
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
a = {
'''vocab_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
),
},
}
a = {
'''moussaKam/mbarthez''': 1_024,
'''moussaKam/barthez''': 1_024,
'''moussaKam/barthez-orangesum-title''': 1_024,
}
a = '''▁'''
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = VOCAB_FILES_NAMES
UpperCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : int = ['''input_ids''', '''attention_mask''']
UpperCAmelCase : Tuple = BarthezTokenizer
def __init__( self : Optional[Any] , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : int=None , _UpperCAmelCase : str="<s>" , _UpperCAmelCase : Optional[Any]="</s>" , _UpperCAmelCase : str="</s>" , _UpperCAmelCase : int="<s>" , _UpperCAmelCase : Union[str, Any]="<unk>" , _UpperCAmelCase : List[Any]="<pad>" , _UpperCAmelCase : List[str]="<mask>" , **_UpperCAmelCase : Optional[int] , ):
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , **_UpperCAmelCase , )
_A = vocab_file
_A = False if not self.vocab_file else True
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_UpperCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
_UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
return (out_vocab_file,)
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
_A = []
for position in positions:
_A , _A = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_snake_case )
return permissible_positions
def _snake_case ( _snake_case : list[list[int]] ) -> bool:
'''simple docstring'''
return not any(elem == 0 for row in board for elem in row )
def _snake_case ( _snake_case : list[list[int]] , _snake_case : tuple[int, int] , _snake_case : int ) -> bool:
'''simple docstring'''
if is_complete(_snake_case ):
return True
for position in get_valid_pos(_snake_case , len(_snake_case ) ):
_A , _A = position
if board[y][x] == 0:
_A = curr + 1
if open_knight_tour_helper(_snake_case , _snake_case , curr + 1 ):
return True
_A = 0
return False
def _snake_case ( _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
for i in range(_snake_case ):
for j in range(_snake_case ):
_A = 1
if open_knight_tour_helper(_snake_case , (i, j) , 1 ):
return board
_A = 0
_A = F'''Open Kight Tour cannot be performed on a board of size {n}'''
raise ValueError(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = '''▁'''
a = {
'''vocab_file''': '''vocab.json''',
'''spm_file''': '''sentencepiece.bpe.model''',
}
a = {
'''vocab_file''': {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
),
},
'''spm_file''': {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
)
},
}
a = {
'''facebook/s2t-small-librispeech-asr''': 1_024,
}
a = ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']
a = {'''mustc''': MUSTC_LANGS}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = VOCAB_FILES_NAMES
UpperCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : Optional[int] = MAX_MODEL_INPUT_SIZES
UpperCAmelCase : Optional[int] = ['''input_ids''', '''attention_mask''']
UpperCAmelCase : List[int] = []
def __init__( self : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int="<s>" , _UpperCAmelCase : Optional[Any]="</s>" , _UpperCAmelCase : List[str]="<pad>" , _UpperCAmelCase : List[str]="<unk>" , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Any=False , _UpperCAmelCase : str=None , _UpperCAmelCase : Any=None , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : Any , ):
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , do_upper_case=_UpperCAmelCase , do_lower_case=_UpperCAmelCase , tgt_lang=_UpperCAmelCase , lang_codes=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , )
_A = do_upper_case
_A = do_lower_case
_A = load_json(_UpperCAmelCase )
_A = {v: k for k, v in self.encoder.items()}
_A = spm_file
_A = load_spm(_UpperCAmelCase , self.sp_model_kwargs )
if lang_codes is not None:
_A = lang_codes
_A = LANGUAGES[lang_codes]
_A = [F'''<lang:{lang}>''' for lang in self.langs]
_A = {lang: self.sp_model.PieceToId(F'''<lang:{lang}>''' ) for lang in self.langs}
_A = self.lang_tokens
_A = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
_A = {}
@property
def lowerCAmelCase_ ( self : Any ):
return len(self.encoder )
@property
def lowerCAmelCase_ ( self : Union[str, Any] ):
return self._tgt_lang
@tgt_lang.setter
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : List[str] ):
_A = new_tgt_lang
self.set_tgt_lang_special_tokens(_UpperCAmelCase )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str ):
_A = self.lang_code_to_id[tgt_lang]
_A = [lang_code_id]
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : str ):
return self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Optional[Any] ):
return self.encoder.get(_UpperCAmelCase , self.encoder[self.unk_token] )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : int ):
return self.decoder.get(_UpperCAmelCase , self.unk_token )
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : List[str] ):
_A = []
_A = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
_A = self.sp_model.decode(_UpperCAmelCase )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
_A = []
else:
current_sub_tokens.append(_UpperCAmelCase )
_A = self.sp_model.decode(_UpperCAmelCase )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str]=None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase )
_A = [1] * len(self.prefix_tokens )
_A = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(_UpperCAmelCase )) + suffix_ones
return prefix_ones + ([0] * len(_UpperCAmelCase )) + ([0] * len(_UpperCAmelCase )) + suffix_ones
def lowerCAmelCase_ ( self : Dict ):
_A = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ):
_A = self.__dict__.copy()
_A = None
return state
def __setstate__( self : Dict , _UpperCAmelCase : Dict ):
_A = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_A = {}
_A = load_spm(self.spm_file , self.sp_model_kwargs )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
_A = Path(_UpperCAmelCase )
assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
_A = save_dir / (
(filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
)
_A = save_dir / (
(filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
)
save_json(self.encoder , _UpperCAmelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _UpperCAmelCase )
elif not os.path.isfile(self.spm_file ):
with open(_UpperCAmelCase , 'wb' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase )
return (str(_UpperCAmelCase ), str(_UpperCAmelCase ))
def _snake_case ( _snake_case : str , _snake_case : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
'''simple docstring'''
_A = sentencepiece.SentencePieceProcessor(**_snake_case )
spm.Load(str(_snake_case ) )
return spm
def _snake_case ( _snake_case : str ) -> Union[Dict, List]:
'''simple docstring'''
with open(_snake_case , 'r' ) as f:
return json.load(_snake_case )
def _snake_case ( _snake_case : Optional[Any] , _snake_case : str ) -> None:
'''simple docstring'''
with open(_snake_case , 'w' ) as f:
json.dump(_snake_case , _snake_case , indent=2 )
| 315 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , *_UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str=None , **_UpperCAmelCase : List[Any] ):
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
_A = eval_examples
_A = post_process_function
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : str = "eval" ):
_A = self.eval_dataset if eval_dataset is None else eval_dataset
_A = self.get_eval_dataloader(_UpperCAmelCase )
_A = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
else:
_A = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_UpperCAmelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_A = self.callback_handler.on_evaluate(self.args , self.state , self.control , _UpperCAmelCase )
return metrics
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : str = "test" ):
_A = self.get_test_dataloader(_UpperCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
_A = self.compute_metrics
_A = None
_A = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_A = time.time()
try:
_A = eval_loop(
_UpperCAmelCase , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCAmelCase , metric_key_prefix=_UpperCAmelCase , )
finally:
_A = compute_metrics
_A = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_UpperCAmelCase , _UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
_A = self.post_process_function(_UpperCAmelCase , _UpperCAmelCase , output.predictions , 'predict' )
_A = self.compute_metrics(_UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
_A = metrics.pop(_UpperCAmelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
a = logging.get_logger(__name__)
a = {
'''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Dict = '''perceiver'''
def __init__( self : Tuple , _UpperCAmelCase : Optional[int]=256 , _UpperCAmelCase : List[Any]=1_280 , _UpperCAmelCase : int=768 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : List[Any]=26 , _UpperCAmelCase : Union[str, Any]=8 , _UpperCAmelCase : Union[str, Any]=8 , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : List[Any]="kv" , _UpperCAmelCase : Union[str, Any]=1 , _UpperCAmelCase : Any=1 , _UpperCAmelCase : List[str]="gelu" , _UpperCAmelCase : List[str]=0.1 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : List[str]=1E-1_2 , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Optional[Any]=262 , _UpperCAmelCase : Tuple=2_048 , _UpperCAmelCase : int=56 , _UpperCAmelCase : Tuple=[368, 496] , _UpperCAmelCase : Tuple=16 , _UpperCAmelCase : Dict=1_920 , _UpperCAmelCase : Union[str, Any]=16 , _UpperCAmelCase : Any=[1, 16, 224, 224] , **_UpperCAmelCase : Dict , ):
super().__init__(**_UpperCAmelCase )
_A = num_latents
_A = d_latents
_A = d_model
_A = num_blocks
_A = num_self_attends_per_block
_A = num_self_attention_heads
_A = num_cross_attention_heads
_A = qk_channels
_A = v_channels
_A = cross_attention_shape_for_attention
_A = self_attention_widening_factor
_A = cross_attention_widening_factor
_A = hidden_act
_A = attention_probs_dropout_prob
_A = initializer_range
_A = layer_norm_eps
_A = use_query_residual
# masked language modeling attributes
_A = vocab_size
_A = max_position_embeddings
# image classification attributes
_A = image_size
# flow attributes
_A = train_size
# multimodal autoencoding attributes
_A = num_frames
_A = audio_samples_per_frame
_A = samples_per_patch
_A = output_shape
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
@property
def lowerCAmelCase_ ( self : Any ):
if self.task == "multiple-choice":
_A = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_A = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('inputs', dynamic_axis),
('attention_mask', dynamic_axis),
] )
@property
def lowerCAmelCase_ ( self : List[str] ):
return 1E-4
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[TensorType] = None , _UpperCAmelCase : int = 3 , _UpperCAmelCase : int = 40 , _UpperCAmelCase : int = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_A = compute_effective_axis_dimension(
_UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_A = preprocessor.num_special_tokens_to_add(_UpperCAmelCase )
_A = compute_effective_axis_dimension(
_UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_UpperCAmelCase )
# Generate dummy inputs according to compute batch and sequence
_A = [' '.join(['a'] ) * seq_length] * batch_size
_A = dict(preprocessor(_UpperCAmelCase , return_tensors=_UpperCAmelCase ) )
_A = inputs.pop('input_ids' )
return inputs
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_A = compute_effective_axis_dimension(_UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch )
_A = self._generate_dummy_images(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
_A = dict(preprocessor(images=_UpperCAmelCase , return_tensors=_UpperCAmelCase ) )
_A = inputs.pop('pixel_values' )
return inputs
else:
raise ValueError(
'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.' )
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : int ) -> bool:
'''simple docstring'''
assert isinstance(_snake_case , _snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
_A = range(3 , int(math.sqrt(_snake_case ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _snake_case ( _snake_case : str , _snake_case : str=1 , **_snake_case : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
_A = factor * value
_A = value
while not is_prime(_snake_case ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **_snake_case )
return value
| 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_A = Vector()
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(_UpperCAmelCase ) , '(0,0,0,0,0,1)' )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = Vector([1, 2, 3, 4] )
self.assertEqual(len(_UpperCAmelCase ) , 4 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2] )
_A = Vector([1, 2, 3, 4, 5] )
_A = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_A = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCAmelCase_ ( self : str ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
_A = Vector([2, -1, 4] ) # for test of dot product
_A = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , '(3.0,6.0,9.0)' )
self.assertEqual((a * b) , 0 )
def lowerCAmelCase_ ( self : Dict ):
self.assertEqual(str(zero_vector(10 ) ).count('0' ) , 10 )
def lowerCAmelCase_ ( self : Tuple ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , _UpperCAmelCase , _UpperCAmelCase ) ) , '(3,4,7)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 0, 0, 0, 0, 0] )
_A = x.copy()
self.assertEqual(str(_UpperCAmelCase ) , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(_UpperCAmelCase ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : str ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_A = Vector([1, 2, 3] )
self.assertEqual('(14,32,50)' , str(a * x ) )
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' , str(a * 2 ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' , str(a + b ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' , str(a - b ) )
def lowerCAmelCase_ ( self : int ):
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 315 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
a = {
'''tokenizer_file''': {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
},
}
a = {
'''gpt-neox-20b''': 2_048,
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : str = VOCAB_FILES_NAMES
UpperCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self : Dict , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Any=None , _UpperCAmelCase : Tuple="<|endoftext|>" , _UpperCAmelCase : Optional[int]="<|endoftext|>" , _UpperCAmelCase : Tuple="<|endoftext|>" , _UpperCAmelCase : Dict=False , **_UpperCAmelCase : int , ):
super().__init__(
_UpperCAmelCase , _UpperCAmelCase , tokenizer_file=_UpperCAmelCase , unk_token=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , **_UpperCAmelCase , )
_A = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , _UpperCAmelCase ) != add_prefix_space:
_A = getattr(_UpperCAmelCase , pre_tok_state.pop('type' ) )
_A = add_prefix_space
_A = pre_tok_class(**_UpperCAmelCase )
_A = add_prefix_space
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
_A = self._tokenizer.model.save(_UpperCAmelCase , name=_UpperCAmelCase )
return tuple(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : "Conversation" ):
_A = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) + [self.eos_token_id] )
if len(_UpperCAmelCase ) > self.model_max_length:
_A = input_ids[-self.model_max_length :]
return input_ids
| 315 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''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 lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = '''xlnet'''
UpperCAmelCase : List[Any] = ['''mems''']
UpperCAmelCase : Any = {
'''n_token''': '''vocab_size''', # Backward compatibility
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Union[str, Any] , _UpperCAmelCase : Dict=32_000 , _UpperCAmelCase : List[str]=1_024 , _UpperCAmelCase : Any=24 , _UpperCAmelCase : Union[str, Any]=16 , _UpperCAmelCase : Union[str, Any]=4_096 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Any=True , _UpperCAmelCase : str="bi" , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Optional[Any]=1E-1_2 , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : Any=512 , _UpperCAmelCase : Dict=None , _UpperCAmelCase : int=True , _UpperCAmelCase : int=False , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : int=-1 , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : Union[str, Any]="last" , _UpperCAmelCase : int=True , _UpperCAmelCase : str="tanh" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : Dict=5 , _UpperCAmelCase : Optional[Any]=5 , _UpperCAmelCase : Union[str, Any]=5 , _UpperCAmelCase : List[str]=1 , _UpperCAmelCase : Dict=2 , **_UpperCAmelCase : int , ):
_A = vocab_size
_A = d_model
_A = n_layer
_A = 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})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = 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.' , _UpperCAmelCase , )
_A = kwargs['use_cache']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : Tuple ):
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 : Optional[Any] , _UpperCAmelCase : Optional[Any] ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 315 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''sentencepiece.model'''}
a = {
'''vocab_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
},
}
a = {
'''google/rembert''': 256,
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
UpperCAmelCase : int = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Optional[int]="[CLS]" , _UpperCAmelCase : Tuple="[SEP]" , _UpperCAmelCase : Optional[int]="[UNK]" , _UpperCAmelCase : Optional[Any]="[SEP]" , _UpperCAmelCase : List[str]="[PAD]" , _UpperCAmelCase : Dict="[CLS]" , _UpperCAmelCase : Optional[int]="[MASK]" , **_UpperCAmelCase : Any , ):
super().__init__(
do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , **_UpperCAmelCase , )
_A = do_lower_case
_A = remove_space
_A = keep_accents
_A = vocab_file
_A = spm.SentencePieceProcessor()
self.sp_model.Load(_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : Union[str, Any] ):
return len(self.sp_model )
def lowerCAmelCase_ ( self : Tuple ):
_A = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : int ):
_A = self.__dict__.copy()
_A = None
return state
def __setstate__( self : Any , _UpperCAmelCase : Any ):
_A = d
_A = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any]=False ):
_A = self.sp_model.EncodeAsPieces(_UpperCAmelCase )
return pieces
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] ):
return self.sp_model.PieceToId(_UpperCAmelCase )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Optional[int] ):
return self.sp_model.IdToPiece(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Dict ):
_A = self.sp_model.decode_pieces(_UpperCAmelCase )
return out_string
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : 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 x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1]
return [1] + ([0] * len(_UpperCAmelCase )) + [1]
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
if not os.path.isdir(_UpperCAmelCase ):
logger.error('Vocabulary path ({}) should be a directory'.format(_UpperCAmelCase ) )
return
_A = os.path.join(
_UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
return (out_vocab_file,)
| 315 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
a = {
'''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
'''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def _snake_case ( _snake_case : Tuple ) -> Dict:
'''simple docstring'''
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def _snake_case ( _snake_case : str , _snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
if args.student_type == "roberta":
_A = False
elif args.student_type == "gpt2":
_A = False
def _snake_case ( _snake_case : str , _snake_case : int ) -> Tuple:
'''simple docstring'''
if args.student_type == "roberta":
_A = False
def _snake_case ( ) -> Tuple:
'''simple docstring'''
_A = argparse.ArgumentParser(description='Training' )
parser.add_argument('--force' , action='store_true' , help='Overwrite dump_path if it already exists.' )
parser.add_argument(
'--dump_path' , type=_snake_case , required=_snake_case , help='The output directory (log, checkpoints, parameters, etc.)' )
parser.add_argument(
'--data_file' , type=_snake_case , required=_snake_case , help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.' , )
parser.add_argument(
'--student_type' , type=_snake_case , choices=['distilbert', 'roberta', 'gpt2'] , required=_snake_case , help='The student type (DistilBERT, RoBERTa).' , )
parser.add_argument('--student_config' , type=_snake_case , required=_snake_case , help='Path to the student configuration.' )
parser.add_argument(
'--student_pretrained_weights' , default=_snake_case , type=_snake_case , help='Load student initialization checkpoint.' )
parser.add_argument(
'--teacher_type' , choices=['bert', 'roberta', 'gpt2'] , required=_snake_case , help='Teacher type (BERT, RoBERTa).' )
parser.add_argument('--teacher_name' , type=_snake_case , required=_snake_case , help='The teacher model.' )
parser.add_argument('--temperature' , default=2.0 , type=_snake_case , help='Temperature for the softmax temperature.' )
parser.add_argument(
'--alpha_ce' , default=0.5 , type=_snake_case , help='Linear weight for the distillation loss. Must be >=0.' )
parser.add_argument(
'--alpha_mlm' , default=0.0 , type=_snake_case , help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.' , )
parser.add_argument('--alpha_clm' , default=0.5 , type=_snake_case , help='Linear weight for the CLM loss. Must be >=0.' )
parser.add_argument('--alpha_mse' , default=0.0 , type=_snake_case , help='Linear weight of the MSE loss. Must be >=0.' )
parser.add_argument(
'--alpha_cos' , default=0.0 , type=_snake_case , help='Linear weight of the cosine embedding loss. Must be >=0.' )
parser.add_argument(
'--mlm' , action='store_true' , help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.' )
parser.add_argument(
'--mlm_mask_prop' , default=0.15 , type=_snake_case , help='Proportion of tokens for which we need to make a prediction.' , )
parser.add_argument('--word_mask' , default=0.8 , type=_snake_case , help='Proportion of tokens to mask out.' )
parser.add_argument('--word_keep' , default=0.1 , type=_snake_case , help='Proportion of tokens to keep.' )
parser.add_argument('--word_rand' , default=0.1 , type=_snake_case , help='Proportion of tokens to randomly replace.' )
parser.add_argument(
'--mlm_smoothing' , default=0.7 , type=_snake_case , help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).' , )
parser.add_argument('--token_counts' , type=_snake_case , help='The token counts in the data_file for MLM.' )
parser.add_argument(
'--restrict_ce_to_mask' , action='store_true' , help='If true, compute the distillation loss only the [MLM] prediction distribution.' , )
parser.add_argument(
'--freeze_pos_embs' , action='store_true' , help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.' , )
parser.add_argument(
'--freeze_token_type_embds' , action='store_true' , help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.' , )
parser.add_argument('--n_epoch' , type=_snake_case , default=3 , help='Number of pass on the whole dataset.' )
parser.add_argument('--batch_size' , type=_snake_case , default=5 , help='Batch size (for each process).' )
parser.add_argument(
'--group_by_size' , action='store_false' , help='If true, group sequences that have similar length into the same batch. Default is true.' , )
parser.add_argument(
'--gradient_accumulation_steps' , type=_snake_case , default=50 , help='Gradient accumulation for larger training batches.' , )
parser.add_argument('--warmup_prop' , default=0.05 , type=_snake_case , help='Linear warmup proportion.' )
parser.add_argument('--weight_decay' , default=0.0 , type=_snake_case , help='Weight decay if we apply some.' )
parser.add_argument('--learning_rate' , default=5E-4 , type=_snake_case , help='The initial learning rate for Adam.' )
parser.add_argument('--adam_epsilon' , default=1E-6 , type=_snake_case , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , default=5.0 , type=_snake_case , help='Max gradient norm.' )
parser.add_argument('--initializer_range' , default=0.02 , type=_snake_case , help='Random initialization range.' )
parser.add_argument(
'--fp16' , action='store_true' , help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit' , )
parser.add_argument(
'--fp16_opt_level' , type=_snake_case , default='O1' , help=(
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
'See details at https://nvidia.github.io/apex/amp.html'
) , )
parser.add_argument('--n_gpu' , type=_snake_case , default=1 , help='Number of GPUs in the node.' )
parser.add_argument('--local_rank' , type=_snake_case , default=-1 , help='Distributed training - Local rank' )
parser.add_argument('--seed' , type=_snake_case , default=56 , help='Random seed' )
parser.add_argument('--log_interval' , type=_snake_case , default=5_00 , help='Tensorboard logging interval.' )
parser.add_argument('--checkpoint_interval' , type=_snake_case , default=40_00 , help='Checkpoint interval.' )
_A = parser.parse_args()
sanity_checks(_snake_case )
# ARGS #
init_gpu_params(_snake_case )
set_seed(_snake_case )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
' itUse `--force` if you want to overwrite it' )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(F'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(F'''Param: {args}''' )
with open(os.path.join(args.dump_path , 'parameters.json' ) , 'w' ) as f:
json.dump(vars(_snake_case ) , _snake_case , indent=4 )
git_log(args.dump_path )
_A , _A , _A = MODEL_CLASSES[args.student_type]
_A , _A , _A = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
_A = teacher_tokenizer_class.from_pretrained(args.teacher_name )
_A = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
_A = tokenizer.all_special_tokens.index(_snake_case )
_A = tokenizer.all_special_ids[idx]
logger.info(F'''Special tokens {special_tok_ids}''' )
_A = special_tok_ids
_A = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(F'''Loading data from {args.data_file}''' )
with open(args.data_file , 'rb' ) as fp:
_A = pickle.load(_snake_case )
if args.mlm:
logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , 'rb' ) as fp:
_A = pickle.load(_snake_case )
_A = np.maximum(_snake_case , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
_A = 0.0 # do not predict special tokens
_A = torch.from_numpy(_snake_case )
else:
_A = None
_A = LmSeqsDataset(params=_snake_case , data=_snake_case )
logger.info('Data loader created.' )
# STUDENT #
logger.info(F'''Loading student config from {args.student_config}''' )
_A = student_config_class.from_pretrained(args.student_config )
_A = True
if args.student_pretrained_weights is not None:
logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
_A = student_model_class.from_pretrained(args.student_pretrained_weights , config=_snake_case )
else:
_A = student_model_class(_snake_case )
if args.n_gpu > 0:
student.to(F'''cuda:{args.local_rank}''' )
logger.info('Student loaded.' )
# TEACHER #
_A = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=_snake_case )
if args.n_gpu > 0:
teacher.to(F'''cuda:{args.local_rank}''' )
logger.info(F'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(_snake_case , _snake_case )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(_snake_case , _snake_case )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
_A = Distiller(
params=_snake_case , dataset=_snake_case , token_probs=_snake_case , student=_snake_case , teacher=_snake_case )
distiller.train()
logger.info('Let\'s go get some drinks.' )
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ) -> List[Any]:
'''simple docstring'''
_A = HfArgumentParser(_snake_case )
_A = parser.parse_args_into_dataclasses()[0]
_A = TensorFlowBenchmark(args=_snake_case )
try:
_A = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
_A = 'Arg --no_{0} is no longer used, please use --no-{0} instead.'
_A = ' '.join(str(_snake_case ).split(' ' )[:-1] )
_A = ''
_A = eval(str(_snake_case ).split(' ' )[-1] )
_A = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(_snake_case )
if len(_snake_case ) > 0:
_A = full_error_msg + begin_error_msg + str(_snake_case )
raise ValueError(_snake_case )
benchmark.run()
if __name__ == "__main__":
main()
| 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_A = Rectangle(height=0.25 , width=0.25 )
_A = [mem.copy() for i in range(6 )]
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('CPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(4 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('GPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Model' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(_UpperCAmelCase )
_A = []
_A = []
for i, rect in enumerate(_UpperCAmelCase ):
_A = fill.copy().set_fill(_UpperCAmelCase , opacity=0.8 )
target.move_to(_UpperCAmelCase )
model_arr.append(_UpperCAmelCase )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_UpperCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(_UpperCAmelCase )
self.add(*_UpperCAmelCase , *_UpperCAmelCase )
_A = [meta_mem.copy() for i in range(6 )]
_A = [meta_mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Disk' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_A = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_UpperCAmelCase )
_A = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase ) )
_A = Square(0.3 )
input.set_fill(_UpperCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , _UpperCAmelCase , buff=0.5 )
self.play(Write(_UpperCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=_UpperCAmelCase , buff=0.02 )
self.play(MoveToTarget(_UpperCAmelCase ) )
self.play(FadeOut(_UpperCAmelCase ) )
_A = Arrow(start=_UpperCAmelCase , end=_UpperCAmelCase , color=_UpperCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , _UpperCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
_A = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) )
_A = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(_UpperCAmelCase ) , Circumscribe(model_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
_A = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , _UpperCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
_A = AnimationGroup(
FadeOut(_UpperCAmelCase , run_time=0.5 ) , MoveToTarget(_UpperCAmelCase , run_time=0.5 ) , FadeIn(_UpperCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(_UpperCAmelCase )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
_A = 0.7
self.play(
Circumscribe(model_arr[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_arr[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
_A = a_c
_A = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(_UpperCAmelCase ) , FadeOut(_UpperCAmelCase , run_time=0.5 ) , )
_A = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) , MoveToTarget(_UpperCAmelCase ) )
self.wait()
| 315 | 1 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class lowercase_ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : Union[str, Any] ):
_A = str(id_ )
_A = None
_A = None
_A = []
_A = {} # {vertex:distance}
def __lt__( self : Optional[Any] , _UpperCAmelCase : List[Any] ):
return self.key < other.key
def __repr__( self : List[Any] ):
return self.id
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : int ):
self.neighbors.append(_UpperCAmelCase )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] ):
_A = weight
def _snake_case ( _snake_case : Optional[Any] , _snake_case : int , _snake_case : int , _snake_case : Union[str, Any] ) -> int:
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , _snake_case )
graph[b - 1].add_edge(graph[a - 1] , _snake_case )
def _snake_case ( _snake_case : list , _snake_case : Vertex ) -> list:
'''simple docstring'''
_A = []
for u in graph:
_A = math.inf
_A = None
_A = 0
_A = graph[:]
while q:
_A = min(_snake_case )
q.remove(_snake_case )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_A = u
_A = u.edges[v.id]
for i in range(1 , len(_snake_case ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _snake_case ( _snake_case : list , _snake_case : Vertex ) -> Iterator[tuple]:
'''simple docstring'''
for u in graph:
_A = math.inf
_A = None
_A = 0
_A = list(_snake_case )
hq.heapify(_snake_case )
while h:
_A = hq.heappop(_snake_case )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_A = u
_A = u.edges[v.id]
hq.heapify(_snake_case )
for i in range(1 , len(_snake_case ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _snake_case ( ) -> None:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
_A , _A = list_data[i + 1], list_data[i]
_A = True
return list_data if not swapped else bubble_sort(_snake_case , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
a = {str(digit): digit**5 for digit in range(10)}
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_snake_case ) )
def _snake_case ( ) -> int:
'''simple docstring'''
return sum(
number
for number in range(10_00 , 1_00_00_00 )
if number == digits_fifth_powers_sum(_snake_case ) )
if __name__ == "__main__":
print(solution())
| 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Any = ['''input_values''', '''attention_mask''']
def __init__( self : Dict , _UpperCAmelCase : int = 1 , _UpperCAmelCase : int = 16_000 , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 80 , _UpperCAmelCase : int = 16 , _UpperCAmelCase : int = 64 , _UpperCAmelCase : str = "hann_window" , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 80 , _UpperCAmelCase : float = 7_600 , _UpperCAmelCase : float = 1E-1_0 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : bool = True , **_UpperCAmelCase : List[Any] , ):
super().__init__(feature_size=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , padding_value=_UpperCAmelCase , **_UpperCAmelCase )
_A = do_normalize
_A = return_attention_mask
_A = num_mel_bins
_A = hop_length
_A = win_length
_A = win_function
_A = frame_signal_scale
_A = fmin
_A = fmax
_A = mel_floor
_A = reduction_factor
_A = win_length * sampling_rate // 1_000
_A = hop_length * sampling_rate // 1_000
_A = optimal_fft_length(self.sample_size )
_A = (self.n_fft // 2) + 1
_A = window_function(window_length=self.sample_size , name=self.win_function , periodic=_UpperCAmelCase )
_A = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm='slaney' , mel_scale='slaney' , )
if frame_signal_scale != 1.0:
warnings.warn(
'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , )
if reduction_factor != 2.0:
warnings.warn(
'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def lowerCAmelCase_ ( _UpperCAmelCase : List[np.ndarray] , _UpperCAmelCase : List[np.ndarray] , _UpperCAmelCase : float = 0.0 ):
if attention_mask is not None:
_A = np.array(_UpperCAmelCase , np.intaa )
_A = []
for vector, length in zip(_UpperCAmelCase , attention_mask.sum(-1 ) ):
_A = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
_A = padding_value
normed_input_values.append(_UpperCAmelCase )
else:
_A = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : np.ndarray , ):
_A = spectrogram(
_UpperCAmelCase , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel='log10' , )
return log_mel_spec.T
def __call__( self : int , _UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _UpperCAmelCase : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : Optional[int] = None , **_UpperCAmelCase : Optional[int] , ):
if audio is None and audio_target is None:
raise ValueError('You must provide either `audio` or `audio_target` values.' )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
if audio is not None:
_A = self._process_audio(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , )
else:
_A = None
if audio_target is not None:
_A = self._process_audio(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , )
if inputs is None:
return inputs_target
else:
_A = inputs_target['input_values']
_A = inputs_target.get('attention_mask' )
if decoder_attention_mask is not None:
_A = decoder_attention_mask
return inputs
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _UpperCAmelCase : bool = False , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , **_UpperCAmelCase : List[Any] , ):
_A = isinstance(_UpperCAmelCase , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
_A = is_batched_numpy or (
isinstance(_UpperCAmelCase , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_A = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(_UpperCAmelCase , np.ndarray ):
_A = np.asarray(_UpperCAmelCase , dtype=np.floataa )
elif isinstance(_UpperCAmelCase , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
_A = speech.astype(np.floataa )
# always return batch
if not is_batched:
_A = [speech]
# needed to make pad() work on spectrogram inputs
_A = self.feature_size
# convert into correct format for padding
if is_target:
_A = [self._extract_mel_features(_UpperCAmelCase ) for waveform in speech]
_A = BatchFeature({'input_values': features} )
_A = self.num_mel_bins
else:
_A = BatchFeature({'input_values': speech} )
_A = self.pad(
_UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , **_UpperCAmelCase , )
_A = feature_size_hack
# convert input values to correct format
_A = padded_inputs['input_values']
if not isinstance(input_values[0] , np.ndarray ):
_A = [np.asarray(_UpperCAmelCase , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(_UpperCAmelCase , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
_A = [array.astype(np.floataa ) for array in input_values]
elif isinstance(_UpperCAmelCase , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
_A = input_values.astype(np.floataa )
# convert attention_mask to correct format
_A = padded_inputs.get('attention_mask' )
if attention_mask is not None:
_A = [np.asarray(_UpperCAmelCase , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
_A = (
attention_mask
if self._get_padding_strategies(_UpperCAmelCase , max_length=_UpperCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
else None
)
_A = self.zero_mean_unit_var_norm(
padded_inputs['input_values'] , attention_mask=_UpperCAmelCase , padding_value=self.padding_value )
if return_tensors is not None:
_A = padded_inputs.convert_to_tensors(_UpperCAmelCase )
return padded_inputs
def lowerCAmelCase_ ( self : Any ):
_A = super().to_dict()
# Don't serialize these as they are derived from the other properties.
_A = ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
for name in names:
if name in output:
del output[name]
return output
| 315 | 1 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _snake_case ( _snake_case : int = 1_00_00_00 , _snake_case : int = 10 ) -> int:
'''simple docstring'''
_A = defaultdict(_snake_case )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_A = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_A = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(_snake_case , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = []
create_all_state(1 , _snake_case , _snake_case , [] , _snake_case )
return result
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : list[int] , _snake_case : list[list[int]] , ) -> None:
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(_snake_case , total_number - level + 2 ):
current_list.append(_snake_case )
create_all_state(i + 1 , _snake_case , level - 1 , _snake_case , _snake_case )
current_list.pop()
def _snake_case ( _snake_case : list[list[int]] ) -> None:
'''simple docstring'''
for i in total_list:
print(*_snake_case )
if __name__ == "__main__":
a = 4
a = 2
a = generate_all_combinations(n, k)
print_all_state(total_list)
| 315 | 1 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
a = sys.version_info >= (3, 10)
def _snake_case ( _snake_case : Tuple=None , _snake_case : Dict=None ) -> Any:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_snake_case )
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : float
UpperCAmelCase : str
UpperCAmelCase : bool
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : int = 42
UpperCAmelCase : str = field(default='''toto''' , metadata={'''help''': '''help message'''} )
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : bool = False
UpperCAmelCase : bool = True
UpperCAmelCase : Optional[bool] = None
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = '''titi'''
UpperCAmelCase : int = '''toto'''
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = '''titi'''
UpperCAmelCase : Any = '''toto'''
UpperCAmelCase : str = 42
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : BasicEnum = "toto"
def lowerCAmelCase_ ( self : List[Any] ):
_A = BasicEnum(self.foo )
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : MixedTypeEnum = "toto"
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = MixedTypeEnum(self.foo )
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Optional[int] = None
UpperCAmelCase : Optional[float] = field(default=__lowerCAmelCase , metadata={'''help''': '''help message'''} )
UpperCAmelCase : Optional[str] = None
UpperCAmelCase : Optional[List[str]] = list_field(default=[] )
UpperCAmelCase : Optional[List[int]] = list_field(default=[] )
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : List[int] = list_field(default=[] )
UpperCAmelCase : List[int] = list_field(default=[1, 2, 3] )
UpperCAmelCase : List[str] = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
UpperCAmelCase : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : List[int] = field()
UpperCAmelCase : str = field()
UpperCAmelCase : BasicEnum = field()
def lowerCAmelCase_ ( self : List[Any] ):
_A = BasicEnum(self.required_enum )
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : "BasicEnum" = field()
UpperCAmelCase : "Optional[bool]" = None
UpperCAmelCase : "str" = field(default='''toto''' , metadata={'''help''': '''help message'''} )
UpperCAmelCase : "List[str]" = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
if is_python_no_less_than_3_10:
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : bool = False
UpperCAmelCase : bool = True
UpperCAmelCase : bool | None = None
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : int | None = None
UpperCAmelCase : float | None = field(default=__lowerCAmelCase , metadata={'''help''': '''help message'''} )
UpperCAmelCase : str | None = None
UpperCAmelCase : list[str] | None = list_field(default=[] )
UpperCAmelCase : list[int] | None = list_field(default=[] )
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : argparse.ArgumentParser , _UpperCAmelCase : argparse.ArgumentParser ):
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_A = {k: v for k, v in vars(_UpperCAmelCase ).items() if k != 'container'}
_A = {k: v for k, v in vars(_UpperCAmelCase ).items() if k != 'container'}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('choices' , _UpperCAmelCase ) and yy.get('choices' , _UpperCAmelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['type'](_UpperCAmelCase ) , yy['type'](_UpperCAmelCase ) )
del xx["type"], yy["type"]
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = argparse.ArgumentParser()
expected.add_argument('--foo' , type=_UpperCAmelCase , required=_UpperCAmelCase )
expected.add_argument('--bar' , type=_UpperCAmelCase , required=_UpperCAmelCase )
expected.add_argument('--baz' , type=_UpperCAmelCase , required=_UpperCAmelCase )
expected.add_argument('--flag' , type=_UpperCAmelCase , default=_UpperCAmelCase , const=_UpperCAmelCase , nargs='?' )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = ['--foo', '1', '--baz', 'quux', '--bar', '0.5']
((_A) , ) = parser.parse_args_into_dataclasses(_UpperCAmelCase , look_for_args_file=_UpperCAmelCase )
self.assertFalse(example.flag )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = argparse.ArgumentParser()
expected.add_argument('--foo' , default=42 , type=_UpperCAmelCase )
expected.add_argument('--baz' , default='toto' , type=_UpperCAmelCase , help='help message' )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = argparse.ArgumentParser()
expected.add_argument('--foo' , type=_UpperCAmelCase , default=_UpperCAmelCase , const=_UpperCAmelCase , nargs='?' )
expected.add_argument('--baz' , type=_UpperCAmelCase , default=_UpperCAmelCase , const=_UpperCAmelCase , nargs='?' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('--no_baz' , action='store_false' , default=_UpperCAmelCase , dest='baz' )
expected.add_argument('--opt' , type=_UpperCAmelCase , default=_UpperCAmelCase )
_A = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_UpperCAmelCase )
for dataclass_type in dataclass_types:
_A = HfArgumentParser(_UpperCAmelCase )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = parser.parse_args([] )
self.assertEqual(_UpperCAmelCase , Namespace(foo=_UpperCAmelCase , baz=_UpperCAmelCase , opt=_UpperCAmelCase ) )
_A = parser.parse_args(['--foo', '--no_baz'] )
self.assertEqual(_UpperCAmelCase , Namespace(foo=_UpperCAmelCase , baz=_UpperCAmelCase , opt=_UpperCAmelCase ) )
_A = parser.parse_args(['--foo', '--baz'] )
self.assertEqual(_UpperCAmelCase , Namespace(foo=_UpperCAmelCase , baz=_UpperCAmelCase , opt=_UpperCAmelCase ) )
_A = parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'] )
self.assertEqual(_UpperCAmelCase , Namespace(foo=_UpperCAmelCase , baz=_UpperCAmelCase , opt=_UpperCAmelCase ) )
_A = parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'] )
self.assertEqual(_UpperCAmelCase , Namespace(foo=_UpperCAmelCase , baz=_UpperCAmelCase , opt=_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=['titi', 'toto', 42] , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
_A = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_A = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
_A = parser.parse_args_into_dataclasses(['--foo', 'titi'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_A = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
_A = parser.parse_args_into_dataclasses(['--foo', '42'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowerCAmelCase_ ( self : Dict ):
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Literal["titi", "toto", 42] = "toto"
_A = HfArgumentParser(_UpperCAmelCase )
_A = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=('titi', 'toto', 42) , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
_A = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
_A = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = argparse.ArgumentParser()
expected.add_argument('--foo_int' , nargs='+' , default=[] , type=_UpperCAmelCase )
expected.add_argument('--bar_int' , nargs='+' , default=[1, 2, 3] , type=_UpperCAmelCase )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=_UpperCAmelCase )
expected.add_argument('--foo_float' , nargs='+' , default=[0.1, 0.2, 0.3] , type=_UpperCAmelCase )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = parser.parse_args([] )
self.assertEqual(
_UpperCAmelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['Hallo', 'Bonjour', 'Hello'] , foo_float=[0.1, 0.2, 0.3] ) , )
_A = parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split() )
self.assertEqual(_UpperCAmelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['a', 'b', 'c'] , foo_float=[0.1, 0.7] ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = argparse.ArgumentParser()
expected.add_argument('--foo' , default=_UpperCAmelCase , type=_UpperCAmelCase )
expected.add_argument('--bar' , default=_UpperCAmelCase , type=_UpperCAmelCase , help='help message' )
expected.add_argument('--baz' , default=_UpperCAmelCase , type=_UpperCAmelCase )
expected.add_argument('--ces' , nargs='+' , default=[] , type=_UpperCAmelCase )
expected.add_argument('--des' , nargs='+' , default=[] , type=_UpperCAmelCase )
_A = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_UpperCAmelCase )
for dataclass_type in dataclass_types:
_A = HfArgumentParser(_UpperCAmelCase )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
_A = parser.parse_args([] )
self.assertEqual(_UpperCAmelCase , Namespace(foo=_UpperCAmelCase , bar=_UpperCAmelCase , baz=_UpperCAmelCase , ces=[] , des=[] ) )
_A = parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split() )
self.assertEqual(_UpperCAmelCase , Namespace(foo=12 , bar=3.14 , baz='42' , ces=['a', 'b', 'c'] , des=[1, 2, 3] ) )
def lowerCAmelCase_ ( self : List[str] ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = argparse.ArgumentParser()
expected.add_argument('--required_list' , nargs='+' , type=_UpperCAmelCase , required=_UpperCAmelCase )
expected.add_argument('--required_str' , type=_UpperCAmelCase , required=_UpperCAmelCase )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=_UpperCAmelCase , )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Tuple ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = argparse.ArgumentParser()
expected.add_argument('--foo' , type=_UpperCAmelCase , required=_UpperCAmelCase )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=_UpperCAmelCase , )
expected.add_argument('--opt' , type=_UpperCAmelCase , default=_UpperCAmelCase )
expected.add_argument('--baz' , default='toto' , type=_UpperCAmelCase , help='help message' )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=_UpperCAmelCase )
self.argparsersEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : str ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
_A = parser.parse_dict(_UpperCAmelCase )[0]
_A = BasicExample(**_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : str ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
'extra': 42,
}
self.assertRaises(_UpperCAmelCase , parser.parse_dict , _UpperCAmelCase , allow_extra_keys=_UpperCAmelCase )
def lowerCAmelCase_ ( self : int ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_A = os.path.join(_UpperCAmelCase , 'temp_json' )
os.mkdir(_UpperCAmelCase )
with open(temp_local_path + '.json' , 'w+' ) as f:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
_A = parser.parse_yaml_file(Path(temp_local_path + '.json' ) )[0]
_A = BasicExample(**_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = HfArgumentParser(_UpperCAmelCase )
_A = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_A = os.path.join(_UpperCAmelCase , 'temp_yaml' )
os.mkdir(_UpperCAmelCase )
with open(temp_local_path + '.yaml' , 'w+' ) as f:
yaml.dump(_UpperCAmelCase , _UpperCAmelCase )
_A = parser.parse_yaml_file(Path(temp_local_path + '.yaml' ) )[0]
_A = BasicExample(**_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = HfArgumentParser(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 315 | 1 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[str] ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple=True ):
_A = ()
for resnet, attn in zip(self.resnets , self.attentions ):
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : List[Any] ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=_UpperCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_downsample:
_A = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[str]=True ):
_A = ()
for resnet in self.resnets:
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
_A = self.downsamplers_a(_UpperCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = resnets
_A = attentions
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any]=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : bool = True
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Any ):
_A = []
for i in range(self.num_layers ):
_A = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_A = self.prev_output_channel if i == 0 else self.out_channels
_A = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
if self.add_upsample:
_A = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : int , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int]=True ):
for resnet in self.resnets:
# pop res hidden states
_A = res_hidden_states_tuple[-1]
_A = res_hidden_states_tuple[:-1]
_A = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
if self.add_upsample:
_A = self.upsamplers_a(_UpperCAmelCase )
return hidden_states
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : float = 0.0
UpperCAmelCase : int = 1
UpperCAmelCase : int = 1
UpperCAmelCase : bool = False
UpperCAmelCase : bool = False
UpperCAmelCase : jnp.dtype = jnp.floataa
def lowerCAmelCase_ ( self : Dict ):
# there is always at least one resnet
_A = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_A = []
for _ in range(self.num_layers ):
_A = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_UpperCAmelCase )
_A = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_UpperCAmelCase )
_A = resnets
_A = attentions
def __call__( self : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int]=True ):
_A = self.resnets[0](_UpperCAmelCase , _UpperCAmelCase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_A = attn(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
_A = resnet(_UpperCAmelCase , _UpperCAmelCase , deterministic=_UpperCAmelCase )
return hidden_states
| 315 | 1 |
"""simple docstring"""
import copy
import re
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = '''hp'''
UpperCAmelCase : str = {}
UpperCAmelCase : List[Any] = None
@classmethod
def lowerCAmelCase_ ( cls : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int ):
_A = prefix
_A = defaults
cls.build_naming_info()
@staticmethod
def lowerCAmelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : int ):
if len(_UpperCAmelCase ) == 0:
return ""
_A = None
if any(char.isdigit() for char in word ):
raise Exception(F'''Parameters should not contain numbers: \'{word}\' contains a number''' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(_UpperCAmelCase ) + 1 ):
_A = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
_A = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(_UpperCAmelCase : str ):
_A = ''
while integer != 0:
_A = chr(ord('A' ) + integer % 10 ) + s
integer //= 10
return s
_A = 0
while True:
_A = word + '#' + int_to_alphabetic(_UpperCAmelCase )
if sword in info["reverse_short_word"]:
continue
else:
_A = sword
break
_A = short_word
_A = word
return short_word
@staticmethod
def lowerCAmelCase_ ( _UpperCAmelCase : List[str] , _UpperCAmelCase : int ):
_A = param_name.split('_' )
_A = [TrialShortNamer.shortname_for_word(_UpperCAmelCase , _UpperCAmelCase ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
_A = ['', '_']
for separator in separators:
_A = separator.join(_UpperCAmelCase )
if shortname not in info["reverse_short_param"]:
_A = shortname
_A = param_name
return shortname
return param_name
@staticmethod
def lowerCAmelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] ):
_A = TrialShortNamer.shortname_for_key(_UpperCAmelCase , _UpperCAmelCase )
_A = short_name
_A = param_name
@classmethod
def lowerCAmelCase_ ( cls : Dict ):
if cls.NAMING_INFO is not None:
return
_A = {
'short_word': {},
'reverse_short_word': {},
'short_param': {},
'reverse_short_param': {},
}
_A = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(_UpperCAmelCase , _UpperCAmelCase )
_A = info
@classmethod
def lowerCAmelCase_ ( cls : List[Any] , _UpperCAmelCase : List[Any] ):
cls.build_naming_info()
assert cls.PREFIX is not None
_A = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(F'''You should provide a default value for the param name {k} with value {v}''' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
_A = cls.NAMING_INFO['short_param'][k]
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_A = 1 if v else 0
_A = '' if isinstance(_UpperCAmelCase , (int, float) ) else '-'
_A = F'''{key}{sep}{v}'''
name.append(_UpperCAmelCase )
return "_".join(_UpperCAmelCase )
@classmethod
def lowerCAmelCase_ ( cls : Tuple , _UpperCAmelCase : List[Any] ):
_A = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
_A = []
else:
_A = repr.split('_' )
_A = {}
for value in values:
if "-" in value:
_A , _A = value.split('-' )
else:
_A = re.sub('[0-9.]' , '' , _UpperCAmelCase )
_A = float(re.sub('[^0-9.]' , '' , _UpperCAmelCase ) )
_A = cls.NAMING_INFO['reverse_short_param'][p_k]
_A = p_v
for k in cls.DEFAULTS:
if k not in parameters:
_A = cls.DEFAULTS[k]
return parameters
| 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
_A = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
_A = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
_A = numpy.random.rand(3 , 1 )
# Real output values provided.
_A = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
_A = numpy.zeros(output_array.shape )
def lowerCAmelCase_ ( self : List[str] ):
_A = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def lowerCAmelCase_ ( self : Optional[int] ):
_A = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
_A = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
_A = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : int , _UpperCAmelCase : bool ):
for iteration in range(1 , iterations + 1 ):
_A = self.feedforward()
self.back_propagation()
if give_loss:
_A = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F'''Iteration {iteration} Loss: {loss}''' )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : numpy.ndarray ):
_A = input_arr
_A = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
_A = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
_A = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return 1 / (1 + numpy.exp(-value ))
def _snake_case ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return (value) * (1 - (value))
def _snake_case ( ) -> int:
'''simple docstring'''
_A = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
_A = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
_A = TwoHiddenLayerNeuralNetwork(
input_array=_snake_case , output_array=_snake_case )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_snake_case , iterations=10 , give_loss=_snake_case )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 315 | 1 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : str=True , _UpperCAmelCase : Dict=99 , _UpperCAmelCase : Optional[Any]=32 , _UpperCAmelCase : List[Any]=5 , _UpperCAmelCase : List[str]=4 , _UpperCAmelCase : str=64 , _UpperCAmelCase : Any="gelu" , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : Optional[Any]=0.1 , _UpperCAmelCase : Optional[int]=512 , _UpperCAmelCase : Union[str, Any]=16 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : Any=3 , _UpperCAmelCase : Dict=4 , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Any=2 , _UpperCAmelCase : int=2 , _UpperCAmelCase : str=2 , _UpperCAmelCase : Tuple=2 , _UpperCAmelCase : Tuple=4 , _UpperCAmelCase : Dict=1 , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_input_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = scope
_A = q_groups
_A = k_groups
_A = v_groups
_A = post_attention_groups
_A = intermediate_groups
_A = output_groups
def lowerCAmelCase_ ( self : Optional[int] ):
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase_ ( self : Union[str, Any] ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , 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 , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] ):
_A = SqueezeBertModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , _UpperCAmelCase )
_A = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] ):
_A = SqueezeBertForMaskedLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] ):
_A = SqueezeBertForQuestionAnswering(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] ):
_A = self.num_labels
_A = SqueezeBertForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int] ):
_A = self.num_labels
_A = SqueezeBertForTokenClassification(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] ):
_A = self.num_choices
_A = SqueezeBertForMultipleChoice(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase_ ( self : Any ):
_A = self.prepare_config_and_inputs()
((_A) , (_A) , (_A) , (_A) , (_A) , (_A)) = config_and_inputs
_A = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
UpperCAmelCase : Optional[int] = (
{
'''feature-extraction''': SqueezeBertModel,
'''fill-mask''': SqueezeBertForMaskedLM,
'''question-answering''': SqueezeBertForQuestionAnswering,
'''text-classification''': SqueezeBertForSequenceClassification,
'''token-classification''': SqueezeBertForTokenClassification,
'''zero-shot''': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : str = True
UpperCAmelCase : Optional[Any] = False
def lowerCAmelCase_ ( self : List[Any] ):
_A = SqueezeBertModelTester(self )
_A = ConfigTester(self , config_class=_UpperCAmelCase , dim=37 )
def lowerCAmelCase_ ( self : int ):
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : List[str] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : int ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = SqueezeBertModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli' )
_A = torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]] )
_A = model(_UpperCAmelCase )[0]
_A = torch.Size((1, 3) )
self.assertEqual(output.shape , _UpperCAmelCase )
_A = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-4 ) )
| 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : T ):
_A = data
_A = None
def __str__( self : str ):
return F'''{self.data}'''
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple ):
_A = None
def __iter__( self : List[Any] ):
_A = self.top
while node:
yield node.data
_A = node.next
def __str__( self : Union[str, Any] ):
return "->".join([str(_UpperCAmelCase ) for item in self] )
def __len__( self : List[Any] ):
return len(tuple(iter(self ) ) )
def lowerCAmelCase_ ( self : str ):
return self.top is None
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : T ):
_A = Node(_UpperCAmelCase )
if not self.is_empty():
_A = self.top
_A = node
def lowerCAmelCase_ ( self : Dict ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _UpperCAmelCase )
_A = self.top
_A = self.top.next
return pop_node.data
def lowerCAmelCase_ ( self : Tuple ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 315 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple=13 , _UpperCAmelCase : Tuple=7 , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : str=True , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Union[str, Any]=99 , _UpperCAmelCase : Any=32 , _UpperCAmelCase : Union[str, Any]=5 , _UpperCAmelCase : int=4 , _UpperCAmelCase : str=37 , _UpperCAmelCase : Any="gelu" , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : Tuple=512 , _UpperCAmelCase : Dict=16 , _UpperCAmelCase : Any=2 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[str]=4 , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_attention_mask
_A = use_token_type_ids
_A = use_labels
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = type_sequence_label_size
_A = initializer_range
_A = num_choices
def lowerCAmelCase_ ( self : Any ):
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_attention_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=_UpperCAmelCase , )
return config, input_ids, attention_mask
def lowerCAmelCase_ ( self : Optional[int] ):
_A = self.prepare_config_and_inputs()
_A , _A , _A = config_and_inputs
_A = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Dict = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase_ ( self : Optional[int] ):
_A = FlaxDistilBertModelTester(self )
@slow
def lowerCAmelCase_ ( self : List[Any] ):
for model_class_name in self.all_model_classes:
_A = model_class_name.from_pretrained('distilbert-base-uncased' )
_A = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCAmelCase )
@require_flax
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase_ ( self : List[Any] ):
_A = FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )
_A = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_A = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
_A = (1, 11, 768)
self.assertEqual(output.shape , _UpperCAmelCase )
_A = np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )
| 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Union[str, Any] ):
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 315 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
a = logging.get_logger(__name__)
a = OrderedDict(
[
('''align''', '''EfficientNetImageProcessor'''),
('''beit''', '''BeitImageProcessor'''),
('''bit''', '''BitImageProcessor'''),
('''blip''', '''BlipImageProcessor'''),
('''blip-2''', '''BlipImageProcessor'''),
('''bridgetower''', '''BridgeTowerImageProcessor'''),
('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
('''clip''', '''CLIPImageProcessor'''),
('''clipseg''', '''ViTImageProcessor'''),
('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
('''convnext''', '''ConvNextImageProcessor'''),
('''convnextv2''', '''ConvNextImageProcessor'''),
('''cvt''', '''ConvNextImageProcessor'''),
('''data2vec-vision''', '''BeitImageProcessor'''),
('''deformable_detr''', '''DeformableDetrImageProcessor'''),
('''deit''', '''DeiTImageProcessor'''),
('''deta''', '''DetaImageProcessor'''),
('''detr''', '''DetrImageProcessor'''),
('''dinat''', '''ViTImageProcessor'''),
('''donut-swin''', '''DonutImageProcessor'''),
('''dpt''', '''DPTImageProcessor'''),
('''efficientformer''', '''EfficientFormerImageProcessor'''),
('''efficientnet''', '''EfficientNetImageProcessor'''),
('''flava''', '''FlavaImageProcessor'''),
('''focalnet''', '''BitImageProcessor'''),
('''git''', '''CLIPImageProcessor'''),
('''glpn''', '''GLPNImageProcessor'''),
('''groupvit''', '''CLIPImageProcessor'''),
('''imagegpt''', '''ImageGPTImageProcessor'''),
('''instructblip''', '''BlipImageProcessor'''),
('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
('''levit''', '''LevitImageProcessor'''),
('''mask2former''', '''Mask2FormerImageProcessor'''),
('''maskformer''', '''MaskFormerImageProcessor'''),
('''mgp-str''', '''ViTImageProcessor'''),
('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
('''mobilevit''', '''MobileViTImageProcessor'''),
('''mobilevit''', '''MobileViTImageProcessor'''),
('''mobilevitv2''', '''MobileViTImageProcessor'''),
('''nat''', '''ViTImageProcessor'''),
('''oneformer''', '''OneFormerImageProcessor'''),
('''owlvit''', '''OwlViTImageProcessor'''),
('''perceiver''', '''PerceiverImageProcessor'''),
('''pix2struct''', '''Pix2StructImageProcessor'''),
('''poolformer''', '''PoolFormerImageProcessor'''),
('''regnet''', '''ConvNextImageProcessor'''),
('''resnet''', '''ConvNextImageProcessor'''),
('''sam''', '''SamImageProcessor'''),
('''segformer''', '''SegformerImageProcessor'''),
('''swiftformer''', '''ViTImageProcessor'''),
('''swin''', '''ViTImageProcessor'''),
('''swin2sr''', '''Swin2SRImageProcessor'''),
('''swinv2''', '''ViTImageProcessor'''),
('''table-transformer''', '''DetrImageProcessor'''),
('''timesformer''', '''VideoMAEImageProcessor'''),
('''tvlt''', '''TvltImageProcessor'''),
('''upernet''', '''SegformerImageProcessor'''),
('''van''', '''ConvNextImageProcessor'''),
('''videomae''', '''VideoMAEImageProcessor'''),
('''vilt''', '''ViltImageProcessor'''),
('''vit''', '''ViTImageProcessor'''),
('''vit_hybrid''', '''ViTHybridImageProcessor'''),
('''vit_mae''', '''ViTImageProcessor'''),
('''vit_msn''', '''ViTImageProcessor'''),
('''xclip''', '''CLIPImageProcessor'''),
('''yolos''', '''YolosImageProcessor'''),
]
)
a = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def _snake_case ( _snake_case : str ) -> Optional[Any]:
'''simple docstring'''
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
_A = model_type_to_module_name(_snake_case )
_A = importlib.import_module(F'''.{module_name}''' , 'transformers.models' )
try:
return getattr(_snake_case , _snake_case )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(_snake_case , '__name__' , _snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
_A = importlib.import_module('transformers' )
if hasattr(_snake_case , _snake_case ):
return getattr(_snake_case , _snake_case )
return None
def _snake_case ( _snake_case : Union[str, os.PathLike] , _snake_case : Optional[Union[str, os.PathLike]] = None , _snake_case : bool = False , _snake_case : bool = False , _snake_case : Optional[Dict[str, str]] = None , _snake_case : Optional[Union[bool, str]] = None , _snake_case : Optional[str] = None , _snake_case : bool = False , **_snake_case : Tuple , ) -> Union[str, Any]:
'''simple docstring'''
_A = get_file_from_repo(
_snake_case , _snake_case , cache_dir=_snake_case , force_download=_snake_case , resume_download=_snake_case , proxies=_snake_case , use_auth_token=_snake_case , revision=_snake_case , local_files_only=_snake_case , )
if resolved_config_file is None:
logger.info(
'Could not locate the image processor configuration file, will try to use the model config instead.' )
return {}
with open(_snake_case , encoding='utf-8' ) as reader:
return json.load(_snake_case )
class lowercase_ :
'''simple docstring'''
def __init__( self : List[Any] ):
raise EnvironmentError(
'AutoImageProcessor is designed to be instantiated '
'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.' )
@classmethod
@replace_list_option_in_docstrings(_UpperCAmelCase )
def lowerCAmelCase_ ( cls : Any , _UpperCAmelCase : str , **_UpperCAmelCase : Any ):
_A = kwargs.pop('config' , _UpperCAmelCase )
_A = kwargs.pop('trust_remote_code' , _UpperCAmelCase )
_A = True
_A , _A = ImageProcessingMixin.get_image_processor_dict(_UpperCAmelCase , **_UpperCAmelCase )
_A = config_dict.get('image_processor_type' , _UpperCAmelCase )
_A = None
if "AutoImageProcessor" in config_dict.get('auto_map' , {} ):
_A = config_dict['auto_map']['AutoImageProcessor']
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
_A = config_dict.pop('feature_extractor_type' , _UpperCAmelCase )
if feature_extractor_class is not None:
logger.warning(
'Could not find image processor class in the image processor config or the model config. Loading'
' based on pattern matching with the model\'s feature extractor configuration.' )
_A = feature_extractor_class.replace('FeatureExtractor' , 'ImageProcessor' )
if "AutoFeatureExtractor" in config_dict.get('auto_map' , {} ):
_A = config_dict['auto_map']['AutoFeatureExtractor']
_A = feature_extractor_auto_map.replace('FeatureExtractor' , 'ImageProcessor' )
logger.warning(
'Could not find image processor auto map in the image processor config or the model config.'
' Loading based on pattern matching with the model\'s feature extractor configuration.' )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_A = AutoConfig.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
# It could be in `config.image_processor_type``
_A = getattr(_UpperCAmelCase , 'image_processor_type' , _UpperCAmelCase )
if hasattr(_UpperCAmelCase , 'auto_map' ) and "AutoImageProcessor" in config.auto_map:
_A = config.auto_map['AutoImageProcessor']
if image_processor_class is not None:
_A = image_processor_class_from_name(_UpperCAmelCase )
_A = image_processor_auto_map is not None
_A = image_processor_class is not None or type(_UpperCAmelCase ) in IMAGE_PROCESSOR_MAPPING
_A = resolve_trust_remote_code(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if has_remote_code and trust_remote_code:
_A = get_class_from_dynamic_module(
_UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase )
_A = kwargs.pop('code_revision' , _UpperCAmelCase )
if os.path.isdir(_UpperCAmelCase ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
elif image_processor_class is not None:
return image_processor_class.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(_UpperCAmelCase ) in IMAGE_PROCESSOR_MAPPING:
_A = IMAGE_PROCESSOR_MAPPING[type(_UpperCAmelCase )]
return image_processor_class.from_dict(_UpperCAmelCase , **_UpperCAmelCase )
raise ValueError(
F'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
F'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def lowerCAmelCase_ ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[Any] ):
IMAGE_PROCESSOR_MAPPING.register(_UpperCAmelCase , _UpperCAmelCase )
| 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def _snake_case ( _snake_case : str ) -> list[str]:
'''simple docstring'''
return word_by_signature[signature(_snake_case )]
a = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
a = sorted({word.strip().lower() for word in data.splitlines()})
a = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
a = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 315 | 1 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
a = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
'''
a = '''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''
a = '''
Computes SQuAD scores (F1 and EM).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair as given in the references (see below)
- \'prediction_text\': the text of the answer
references: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair (see above),
- \'answers\': a Dict in the SQuAD dataset format
{
\'text\': list of possible texts for the answer, as a list of strings
\'answer_start\': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
\'exact_match\': Exact match (the normalized answer exactly match the gold answer)
\'f1\': The F-score of predicted tokens versus the gold answer
Examples:
>>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
>>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
>>> squad_metric = datasets.load_metric("squad")
>>> results = squad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 100.0, \'f1\': 100.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': {'id': datasets.Value('string' ), 'prediction_text': datasets.Value('string' )},
'references': {
'id': datasets.Value('string' ),
'answers': datasets.features.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
},
} ) , codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] , reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] , )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] ):
_A = {prediction['id']: prediction['prediction_text'] for prediction in predictions}
_A = [
{
'paragraphs': [
{
'qas': [
{
'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
'id': ref['id'],
}
for ref in references
]
}
]
}
]
_A = evaluate(dataset=_UpperCAmelCase , predictions=_UpperCAmelCase )
return score
| 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''The column name of the images in the files.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the training data.'''} )
UpperCAmelCase : Optional[str] = field(default=__lowerCAmelCase , metadata={'''help''': '''A folder containing the validation data.'''} )
UpperCAmelCase : Optional[float] = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase : Optional[int] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def lowerCAmelCase_ ( self : Dict ):
_A = {}
if self.train_dir is not None:
_A = self.train_dir
if self.validation_dir is not None:
_A = self.validation_dir
_A = data_files if data_files else None
@dataclass
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : str = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCAmelCase : Optional[str] = field(
default=__lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
UpperCAmelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase : str = field(default=__lowerCAmelCase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCAmelCase : float = field(
default=0.75 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
UpperCAmelCase : bool = field(
default=__lowerCAmelCase , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : float = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def _snake_case ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
_A = torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _snake_case ( ) -> List[str]:
'''simple docstring'''
_A = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A , _A , _A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , _snake_case , _snake_case )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_A = training_args.get_process_log_level()
logger.setLevel(_snake_case )
transformers.utils.logging.set_verbosity(_snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
_A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_A = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _snake_case ) and data_args.train_val_split > 0.0:
_A = ds['train'].train_test_split(data_args.train_val_split )
_A = split['train']
_A = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_A = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
_A = ViTMAEConfig.from_pretrained(model_args.config_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_A = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_snake_case )
elif model_args.model_name_or_path:
_A = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_snake_case )
else:
_A = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_A = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
_A = ViTMAEForPreTraining(_snake_case )
if training_args.do_train:
_A = ds['train'].column_names
else:
_A = ds['validation'].column_names
if data_args.image_column_name is not None:
_A = data_args.image_column_name
elif "image" in column_names:
_A = 'image'
elif "img" in column_names:
_A = 'img'
else:
_A = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_A = image_processor.size['shortest_edge']
else:
_A = (image_processor.size['height'], image_processor.size['width'])
_A = Compose(
[
Lambda(lambda _snake_case : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_snake_case , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_snake_case : List[Any] ):
_A = [transforms(_snake_case ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
_A = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_snake_case )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
_A = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_snake_case )
# Compute absolute learning rate
_A = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_A = training_args.base_learning_rate * total_train_batch_size / 2_56
# Initialize our trainer
_A = Trainer(
model=_snake_case , args=_snake_case , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_snake_case , data_collator=_snake_case , )
# Training
if training_args.do_train:
_A = None
if training_args.resume_from_checkpoint is not None:
_A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A = last_checkpoint
_A = trainer.train(resume_from_checkpoint=_snake_case )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_A = trainer.evaluate()
trainer.log_metrics('eval' , _snake_case )
trainer.save_metrics('eval' , _snake_case )
# Write model card and (optionally) push to hub
_A = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_snake_case )
else:
trainer.create_model_card(**_snake_case )
def _snake_case ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 315 | 1 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case ( ) -> tuple[list[int], int]:
'''simple docstring'''
_A = [randint(-10_00 , 10_00 ) for i in range(10 )]
_A = randint(-50_00 , 50_00 )
return (arr, r)
a = make_dataset()
def _snake_case ( _snake_case : list[int] , _snake_case : int ) -> tuple[int, ...]:
'''simple docstring'''
for triplet in permutations(_snake_case , 3 ):
if sum(_snake_case ) == target:
return tuple(sorted(_snake_case ) )
return (0, 0, 0)
def _snake_case ( _snake_case : list[int] , _snake_case : int ) -> tuple[int, int, int]:
'''simple docstring'''
arr.sort()
_A = len(_snake_case )
for i in range(n - 1 ):
_A , _A = 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 _snake_case ( ) -> tuple[float, float]:
'''simple docstring'''
_A = '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
_A = '\ntriplet_sum1(*dataset)\n'
_A = '\ntriplet_sum2(*dataset)\n'
_A = repeat(setup=_snake_case , stmt=_snake_case , repeat=5 , number=1_00_00 )
_A = repeat(setup=_snake_case , stmt=_snake_case , repeat=5 , number=1_00_00 )
return (min(_snake_case ), min(_snake_case ))
if __name__ == "__main__":
from doctest import testmod
testmod()
a = solution_times()
print(F'''The time for naive implementation is {times[0]}.''')
print(F'''The time for optimized implementation is {times[1]}.''')
| 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer names
a = 70 # max width of quantizer names
def _snake_case ( _snake_case : int ) -> List[Any]:
'''simple docstring'''
_A = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=_snake_case , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=_snake_case , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=_snake_case , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=_snake_case , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=_snake_case , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=_snake_case , type=_snake_case , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=_snake_case , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def _snake_case ( _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
if args.calibrator == "max":
_A = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
_A = 'histogram'
elif args.calibrator == "mse":
_A = 'histogram'
else:
raise ValueError(F'''Invalid calibrator {args.calibrator}''' )
_A = QuantDescriptor(num_bits=args.aprec , calib_method=_snake_case )
_A = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(_snake_case )
quant_nn.QuantLinear.set_default_quant_desc_weight(_snake_case )
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Any=False , _snake_case : Union[str, Any]=False ) -> Optional[int]:
'''simple docstring'''
logger.info('Configuring Model for Quantization' )
logger.info(F'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_snake_case , ['embeddings'] , which='weight' , _disabled=_snake_case )
if args.quant_disable:
set_quantizer_by_name(_snake_case , [''] , _disabled=_snake_case )
if args.quant_disable_keyword:
set_quantizer_by_name(_snake_case , args.quant_disable_keyword , _disabled=_snake_case )
if args.quant_disable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_disable_layer_module] , _disabled=_snake_case )
if args.quant_enable_layer_module:
set_quantizer_by_name(_snake_case , [R'layer.\d+.' + args.quant_enable_layer_module] , _disabled=_snake_case )
if args.recalibrate_weights:
recalibrate_weights(_snake_case )
if args.fuse_qkv:
fuse_qkv(_snake_case , _snake_case )
if args.clip_gelu:
clip_gelu(_snake_case , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str ) -> Any:
'''simple docstring'''
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F'''{name:80}: {module}''' )
def _snake_case ( _snake_case : List[Any] , _snake_case : List[Any] ) -> str:
'''simple docstring'''
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_snake_case )
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
def fusea(_snake_case : int , _snake_case : str , _snake_case : Optional[Any] ):
for mod in [qq, qk, qv]:
if not hasattr(_snake_case , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
_A = qq._amax.detach().item()
_A = qk._amax.detach().item()
_A = qv._amax.detach().item()
_A = max(_snake_case , _snake_case , _snake_case )
qq._amax.fill_(_snake_case )
qk._amax.fill_(_snake_case )
qv._amax.fill_(_snake_case )
logger.info(F''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(F'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def _snake_case ( _snake_case : int , _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
_A = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_snake_case )
_A = mod._input_quantizer._amax.data.detach().item()
logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def _snake_case ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
_A = mod.weight.shape[0]
_A = mod._weight_quantizer._amax.detach()
_A = torch.ones(_snake_case , dtype=amax.dtype , device=amax.device ) * amax
print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def _snake_case ( _snake_case : Dict ) -> Tuple:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
_A = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
_A = set(range(len(mod.weight.size() ) ) ) - axis_set
_A = pytorch_quantization.utils.reduce_amax(mod.weight , axis=_snake_case , keepdims=_snake_case ).detach()
logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
_A = amax
def _snake_case ( _snake_case : Tuple , _snake_case : List[str]=25 , _snake_case : str=1_80 , _snake_case : int=None ) -> List[Any]:
'''simple docstring'''
if ignore is None:
_A = []
elif not isinstance(_snake_case , _snake_case ):
_A = [ignore]
_A = 0
for name, mod in model.named_modules():
if not hasattr(_snake_case , 'weight' ):
continue
_A = max(_snake_case , len(_snake_case ) )
for name, mod in model.named_modules():
_A = getattr(_snake_case , '_input_quantizer' , _snake_case )
_A = getattr(_snake_case , '_weight_quantizer' , _snake_case )
if not hasattr(_snake_case , 'weight' ):
continue
if type(_snake_case ) in ignore:
continue
if [True for s in ignore if type(_snake_case ) is str and s in name]:
continue
_A = F'''Act:{input_q.extra_repr()}'''
_A = F'''Wgt:{weight_q.extra_repr()}'''
_A = F'''{name:{name_width}} {act_str} {wgt_str}'''
if len(_snake_case ) <= line_width:
logger.info(_snake_case )
else:
logger.info(F'''{name:{name_width}} {act_str}''' )
logger.info(F'''{" ":{name_width}} {wgt_str}''' )
def _snake_case ( _snake_case : Dict ) -> int:
'''simple docstring'''
_A = 0
for name, mod in model.named_modules():
if isinstance(_snake_case , pytorch_quantization.nn.TensorQuantizer ):
print(F'''{name:80} {mod}''' )
count += 1
print(F'''{count} TensorQuantizers found in model''' )
def _snake_case ( _snake_case : str , _snake_case : Dict , _snake_case : List[Any] , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
_A = getattr(_snake_case , _snake_case , _snake_case )
if quantizer_mod is not None:
assert hasattr(_snake_case , _snake_case )
setattr(_snake_case , _snake_case , _snake_case )
else:
logger.warning(F'''{name} has no {quantizer}''' )
def _snake_case ( _snake_case : Dict , _snake_case : Optional[int] , _snake_case : str="both" , **_snake_case : List[Any] ) -> str:
'''simple docstring'''
_A = F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(_snake_case , _snake_case , '_input_quantizer' , _snake_case , _snake_case )
if which in ["weight", "both"]:
set_quantizer(_snake_case , _snake_case , '_weight_quantizer' , _snake_case , _snake_case )
logger.info(_snake_case )
def _snake_case ( _snake_case : Any , _snake_case : int , **_snake_case : Dict ) -> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(_snake_case , '_input_quantizer' ) or hasattr(_snake_case , '_weight_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
set_quantizers(_snake_case , _snake_case , **_snake_case )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(_snake_case , _snake_case ):
_A = F'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
setattr(_snake_case , _snake_case , _snake_case )
logger.info(_snake_case )
| 315 | 1 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , **_UpperCAmelCase : int ):
requires_backends(self , ['bs4'] )
super().__init__(**_UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : int ):
_A = []
_A = []
_A = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_A = parent.find_all(child.name , recursive=_UpperCAmelCase )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_UpperCAmelCase ) else next(i for i, s in enumerate(_UpperCAmelCase , 1 ) if s is child ) )
_A = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Optional[int] ):
_A = BeautifulSoup(_UpperCAmelCase , 'html.parser' )
_A = []
_A = []
_A = []
for element in html_code.descendants:
if type(_UpperCAmelCase ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_A = html.unescape(_UpperCAmelCase ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_UpperCAmelCase )
_A , _A = self.xpath_soup(_UpperCAmelCase )
stringaxtag_seq.append(_UpperCAmelCase )
stringaxsubs_seq.append(_UpperCAmelCase )
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('Number of doc strings and xtags does not correspond' )
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('Number of doc strings and xsubs does not correspond' )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple ):
_A = ''
for tagname, subs in zip(_UpperCAmelCase , _UpperCAmelCase ):
xpath += F'''/{tagname}'''
if subs != 0:
xpath += F'''[{subs}]'''
return xpath
def __call__( self : int , _UpperCAmelCase : Optional[Any] ):
_A = False
# Check that strings has a valid type
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_A = True
elif isinstance(_UpperCAmelCase , (list, tuple) ):
if len(_UpperCAmelCase ) == 0 or isinstance(html_strings[0] , _UpperCAmelCase ):
_A = True
if not valid_strings:
raise ValueError(
'HTML strings must of type `str`, `List[str]` (batch of examples), '
F'''but is of type {type(_UpperCAmelCase )}.''' )
_A = bool(isinstance(_UpperCAmelCase , (list, tuple) ) and (isinstance(html_strings[0] , _UpperCAmelCase )) )
if not is_batched:
_A = [html_strings]
# Get nodes + xpaths
_A = []
_A = []
for html_string in html_strings:
_A , _A , _A = self.get_three_from_single(_UpperCAmelCase )
nodes.append(_UpperCAmelCase )
_A = []
for node, tag_list, sub_list in zip(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_A = self.construct_xpath(_UpperCAmelCase , _UpperCAmelCase )
xpath_strings.append(_UpperCAmelCase )
xpaths.append(_UpperCAmelCase )
# return as Dict
_A = {'nodes': nodes, 'xpaths': xpaths}
_A = BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
return encoded_inputs
| 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'] , )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int]=False ):
_A = spearmanr(_UpperCAmelCase , _UpperCAmelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 315 | 1 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def _snake_case ( _snake_case : Optional[int] ) -> str:
'''simple docstring'''
_A = np.max(_snake_case , axis=-1 , keepdims=_snake_case )
_A = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=_snake_case )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Tuple , **_UpperCAmelCase : Any ):
_A = {}
if "second_text" in kwargs:
_A = kwargs['second_text']
return preprocess_kwargs, {}, {}
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int=None ):
return self.tokenizer(_UpperCAmelCase , text_pair=_UpperCAmelCase , return_tensors=self.framework )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Union[str, Any] ):
return self.model(**_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Optional[int] ):
_A = model_outputs.logits[0].numpy()
_A = softmax(_UpperCAmelCase )
_A = np.argmax(_UpperCAmelCase )
_A = self.model.config.idalabel[best_class]
_A = probabilities[best_class].item()
_A = logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A = b
if function(_snake_case ) == 0: # one of the a or b is a root for the function
return a
elif function(_snake_case ) == 0:
return b
elif (
function(_snake_case ) * function(_snake_case ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
_A = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(_snake_case ) == 0:
return mid
elif function(_snake_case ) * function(_snake_case ) < 0:
_A = mid
else:
_A = mid
_A = start + (end - start) / 2.0
return mid
def _snake_case ( _snake_case : float ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(chr(ord(_snake_case ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmelCase ) , '''Tatoeba directory does not exist.''' )
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Optional[int] ):
self.resolver.convert_models(['heb-eng'] )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_A , _A = self.resolver.write_model_card('opus-mt-he-en' , dry_run=_UpperCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 315 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = ['''image_processor''', '''tokenizer''']
UpperCAmelCase : Union[str, Any] = '''ViltImageProcessor'''
UpperCAmelCase : Optional[int] = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Any , _UpperCAmelCase : str=None , _UpperCAmelCase : Tuple=None , **_UpperCAmelCase : Union[str, Any] ):
_A = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , _UpperCAmelCase , )
_A = kwargs.pop('feature_extractor' )
_A = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(_UpperCAmelCase , _UpperCAmelCase )
_A = self.image_processor
def __call__( self : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : int = 0 , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , **_UpperCAmelCase : List[Any] , ):
_A = self.tokenizer(
text=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , stride=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , return_overflowing_tokens=_UpperCAmelCase , return_special_tokens_mask=_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , return_length=_UpperCAmelCase , verbose=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase , )
# add pixel_values + pixel_mask
_A = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase )
encoding.update(_UpperCAmelCase )
return encoding
def lowerCAmelCase_ ( self : List[Any] , *_UpperCAmelCase : Optional[Any] , **_UpperCAmelCase : Union[str, Any] ):
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : List[Any] ):
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : Tuple ):
_A = self.tokenizer.model_input_names
_A = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def lowerCAmelCase_ ( self : Dict ):
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , _UpperCAmelCase , )
return self.image_processor_class
@property
def lowerCAmelCase_ ( self : Tuple ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _UpperCAmelCase , )
return self.image_processor
| 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
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 .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
_A , _A = list_data[i + 1], list_data[i]
_A = True
return list_data if not swapped else bubble_sort(_snake_case , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
_A = []
for position in positions:
_A , _A = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_snake_case )
return permissible_positions
def _snake_case ( _snake_case : list[list[int]] ) -> bool:
'''simple docstring'''
return not any(elem == 0 for row in board for elem in row )
def _snake_case ( _snake_case : list[list[int]] , _snake_case : tuple[int, int] , _snake_case : int ) -> bool:
'''simple docstring'''
if is_complete(_snake_case ):
return True
for position in get_valid_pos(_snake_case , len(_snake_case ) ):
_A , _A = position
if board[y][x] == 0:
_A = curr + 1
if open_knight_tour_helper(_snake_case , _snake_case , curr + 1 ):
return True
_A = 0
return False
def _snake_case ( _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
for i in range(_snake_case ):
for j in range(_snake_case ):
_A = 1
if open_knight_tour_helper(_snake_case , (i, j) , 1 ):
return board
_A = 0
_A = F'''Open Kight Tour cannot be performed on a board of size {n}'''
raise ValueError(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
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